CN119814252A

CN119814252A - A method and device for CSI reporting in a node used for wireless communication

Info

Publication number: CN119814252A
Application number: CN202410720929.5A
Authority: CN
Inventors: 胡杨; 张煜昊
Original assignee: Honor Device Co Ltd
Current assignee: Honor Device Co Ltd
Priority date: 2024-06-04
Filing date: 2024-06-04
Publication date: 2025-04-11
Anticipated expiration: 2044-06-04
Also published as: CN119814252B; WO2025251884A1

Abstract

The present application discloses a method and apparatus for CSI reporting in a node used for wireless communication. A first node receives at least one CSI reporting configuration; receives a first DCI on a first PDCCH, the first DCI triggering the reporting of at least one CSI on a first PUSCH; determines whether to send a target CSI on the first PUSCH; sends the target CSI on the first PUSCH only when a first condition is met; a target CSI reporting configuration is used to configure the reporting of the target CSI; the target CSI reporting configuration indicates a first resource set; the first condition includes that a first symbol is not earlier than a first reference symbol, the first symbol is the first uplink symbol in the first PUSCH used to carry the at least one CSI, the first reference symbol is a CP starting at the next uplink symbol in the first time interval after the end of the last symbol of the first PDCCH; the first time interval depends on whether the generation method of the target CSI is based on AI.

Description

Method and apparatus for CSI reporting in a node for wireless communications

Technical Field

The present application relates to a transmission method and apparatus in a wireless communication system, and in particular, to a scheme and apparatus for reporting CSI (CHANNEL STATE Information) in a wireless communication system.

Background

In conventional wireless communications, a UE (User Equipment) reports various auxiliary information, such as channel information, beam Management (Beam Management) related auxiliary information, positioning related auxiliary information, and so on, obtained by measuring downlink signals and/or channels. Wherein the channel information includes, but is not limited to, one or more of CRI (CSI-RS Resource Indicator, channel state information reference signal resource indication), RI (Rank Indicator), PMI (Precoding Matrix Indicator, precoding indication), CQI (Channel quality Indicator, channel quality indication), or beam indication. The UE may use this information itself to select the appropriate transmission parameters or report this information. The network device selects appropriate transmission parameters for the UE according to the UE reporting, such as parameters of the camping cell, MCS (Modulation and Coding Scheme, modulation coding scheme), TPMI (TRANSMITTED PRECODING MATRIX INDICATOR, transmit precoding matrix indication), TCI (Transmission Configuration Indication, transmit configuration indication), and the like. In addition, UE reporting may be used to optimize network parameters, such as better cell coverage, switching base stations based on UE location, etc.

With the adoption of new technology, the number of antennas is increased, the application scene is diversified, the system performance requirement is improved, and the like, and a large amount of redundancy overhead is brought by the traditional measurement and reporting mode. Thus, in NR (New Radio) Rel-18 (Release-18, version 18), AI (ARTIFICIAL INTELLIGENCE )/ML (MACHINE LEARNING, machine learning) technology has been developed to investigate its impact on system performance and system design. In contrast to conventional approaches, AI/ML has characteristics based on training and deployment requirements.

Disclosure of Invention

The applicant has found through research that when AI/ML functionality is introduced, the existing measurement mechanisms, reporting mechanisms and associated configuration signaling may not be able to accommodate AI/ML requirements. In view of the above, the present application discloses a solution. It should be noted that, in the description of the above problem, the NR system is taken as an example, the present application is also applicable to a future 6G system scenario, so as to obtain a technical effect similar to the NR system, further, although the present application is primarily aimed at an AI/ML scenario, the present application can also apply other non-AI/ML scenarios, such as a traditional scheme for reporting CSI (CHANNEL STATE Information ), and further, a unified design scheme is also adopted for different scenarios (such as other non-AI/ML scenarios including but not limited to internet of vehicles (Vehicle to Everything, V2X), capacity enhancement system, near field communication system, NTN (Non Terrestrial Network, non-terrestrial network), ioT (Internet of Things ), URLLC (Ultra Reliable Low Latency Communication, ultra-robust low latency communication) network, etc.), which is also helpful for reducing hardware complexity and cost. Embodiments in any one node of the application and features in embodiments may be applied to any other node without conflict. The embodiments of the application and the features of the embodiments may be combined with each other arbitrarily without conflict.

In particular, the term (Terminology), noun, function, variable in the present application may be interpreted (if not specifically described) with reference to the definitions in the 3GPP specification protocols TS28 series, TS36 series, TS38 series, TS37 series. Reference may be made to 3GPP standards TS38.211, TS38.212, TS38.213, TS38.214, TS38.215, TS38.321, TS38.331, TS38.305, TS38.304, TS37.355 as needed to aid in the understanding of the application.

The application discloses a method used in a first node of wireless communication, which is characterized by comprising the following steps:

Receiving first DCI on a first PDCCH, wherein the first DCI triggers the reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used for configuring the reporting of the at least one CSI;

Determining whether to transmit a target CSI on the first PUSCH; transmitting the target CSI on the first PUSCH only when a first condition is satisfied;

The method comprises the steps of configuring reporting of target CSI, wherein the target CSI reporting configuration is used for configuring reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, the target CSI is one of the at least one CSI, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set comprises one or more RS resources, the first condition comprises a first symbol which is not earlier than a first reference symbol, the first symbol is a first uplink symbol used for bearing the at least one CSI in the first PUSCH, the first reference symbol is a next uplink symbol of a first time interval after the end of a last symbol of the first PDCCH, and the first time interval depends on whether the generation mode of the target CSI is based on AI or not.

As one embodiment, the AI (ARTIFICIAL INTELLIGENCE ) includes ML (MACHINE LEARNING, machine learning).

As one embodiment, the problem to be solved by the present application includes how to determine whether to send CSI on PUSCH.

As one embodiment, the essence of the above method includes transmitting CSI on PUSCH only when the first condition is met.

As one embodiment, the essence of the above method includes whether the generation manner of the CSI-dependent CSI is sent on PUSCH is based on AI.

As one embodiment, the benefits of the above method include supporting AI-based CSI reporting.

As one embodiment, the method has the advantages of ensuring consistency of understanding of the CSI report by a receiving end.

As one embodiment, the benefits of the above method include improved accuracy and effectiveness of CSI reporting.

As an embodiment, the method has the advantages that whether the CSI is sent on the PUSCH depends on the first time interval, the system design is simplified, and the complexity of the scheme is reduced.

As one embodiment, the method has the advantages of enhancing the flexibility and the robustness of the system, adapting to various different transmission conditions and application scenes better and improving the overall performance of the system.

According to one aspect of the present application, the calculation formula of the first time interval depends on a first parameter, and the first parameter depends on whether the generation manner of the target CSI is AI-based or not.

As an embodiment, the essence of the method includes that the value of the first time interval depends on whether the generation mode of the target CSI is AI-based.

As one example, the benefits of the above approach include compatibility with existing system designs and standards, and improved system forward and backward compatibility.

As one embodiment, the benefits of the above approach include simplifying the design of the system and reducing the implementation complexity of the solution.

According to one aspect of the present application, when the generation manner of the target CSI is AI-based, the target CSI includes N information blocks, the N information blocks include channel information of N time units, respectively, N is a positive integer greater than 1, and the first time interval depends on at least one of the N time units.

As one example, the benefits of the above-described approach include compatibility with current system designs and standards.

As an embodiment, the benefits of the above method include enhanced system flexibility and robustness, adapting to different transmission scenarios and applications.

As one example, benefits of the above method include improving overall performance of the system.

According to an aspect of the present application, when the first set of resources consists of one or more aperiodic RS resources, the first condition further includes that a second symbol is not earlier than a second reference symbol, the second reference symbol being a next uplink symbol of a CP starting at a second time interval after an end of a last symbol of the first RS in the first set of resources.

As an embodiment, the essence of the above method comprises that the first condition comprises at least two conditions.

As one embodiment, the benefits of the above-described method include enhanced system adaptability and robustness.

According to one aspect of the application, the generation of the target CSI is based on AI, and the generation of the target CSI comprises performing a first operation, wherein the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation.

As an embodiment, the first operation is training-based or AI-based.

As one embodiment, the benefits of the above approach include supporting AI/ML-based CSI reporting.

According to one aspect of the application, the generation mode of the target CSI is based on AI, and the generation mode of the target CSI is associated with a first type identifier.

As one embodiment, the method has the advantages of adapting to various different scenes and terminals and improving the adaptability and flexibility of the system.

As one example, the benefits of the above approach include a simplified system design with good flexibility.

According to an aspect of the present application, when the generation manner of the target CSI is AI-based, the first time interval depends on the first type identifier to which the generation manner of the target CSI is associated.

As one embodiment, the benefits of the above approach include simplifying the system design and reducing the implementation complexity of the solution.

As one example, benefits of the above approach include increased system flexibility and overall system performance.

According to one aspect of the application, the generation of the target CSI is based on AI, and the generation of the target CSI is based on AI, wherein the target CSI indicates at least one resource in a second resource set, and the second resource set comprises resources not belonging to the first resource set.

As one embodiment, the benefits of the above approach include reduced overhead required to obtain the target CSI.

As one embodiment, benefits of the above method include reducing the measurement resources required to obtain the target CSI.

According to one aspect of the present application, it is characterized by comprising:

Ignoring the first DCI when the first condition is not satisfied;

wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

As one embodiment, the essence of the above method comprises not transmitting CSI on PUSCH and ignoring the corresponding DCI when the first condition is not met.

As one example, the benefits of the above approach include compatibility with current system designs and standards, and improved system forward and backward compatibility.

As one example, benefits of the above method include improved system stability and robustness.

The application discloses a method used in a second node of wireless communication, which is characterized by comprising the following steps:

Transmitting a first DCI on a first PDCCH, wherein the first DCI triggers the reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used for configuring the reporting of the at least one CSI;

wherein, the target receiver of the first DCI determines whether to send target CSI on the first PUSCH; the target receiver of the first DCI transmits the target CSI on the first PUSCH only when a first condition is met, wherein a target CSI reporting configuration is used for configuring reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, the target CSI is one of the at least one CSI, the target CSI reporting configuration indicates a first resource set which is used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set comprises one or more RS resources, the first condition comprises a first symbol which is not earlier than a first reference symbol, the first symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI, the first reference symbol is a next uplink symbol of a first time interval after the end of a last symbol of the first PDCCH, and the first time interval depends on whether the generation of the target CSI is based on AI or not.

Determining whether to receive a target CSI on the first PUSCH; the target CSI is received on the first PUSCH only when the first condition is satisfied.

According to one aspect of the application, the generation of the target CSI is based on AI, and the generation of the target CSI comprises the target receiver of the first DCI executing a first operation, wherein the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation.

discarding receiving a signal on the first PUSCH or discarding receiving the target CSI on the first PUSCH when the first condition is not satisfied;

wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

when the first condition is not satisfied, the target receiver of the first DCI ignores the first DCI;

wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

The application discloses a terminal, which is characterized by comprising one or more processors and a memory;

the memory is coupled with the one or more processors, the memory for storing computer program code comprising computer instructions that the one or more processors call to cause the terminal to perform a method in the first node.

The application discloses a base station, which is characterized by comprising one or more processors and a memory;

the memory is coupled with the one or more processors, the memory for storing computer program code comprising computer instructions that the one or more processors invoke the computer instructions to cause the base station to perform a method in the second node.

The application discloses a first node used for wireless communication, which is characterized by comprising the following components:

The system comprises a first receiver, a first PDCCH, a second PDCCH, a first DCI, a second PDCCH, a first receiver and a second receiver, wherein the first receiver receives at least one CSI reporting configuration, the first DCI triggers the reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used for configuring the reporting of the at least one CSI;

A first processor that determines whether to transmit a target CSI on the first PUSCH, the target CSI being transmitted on the first PUSCH only when a first condition is satisfied;

The present application discloses a second node used for wireless communication, which is characterized by comprising:

the second processor is used for sending at least one CSI reporting configuration, sending first DCI on a first PDCCH, triggering the reporting of at least one CSI on a first PUSCH by the first DCI, wherein the at least one CSI reporting configuration is used for configuring the reporting of the at least one CSI;

As an embodiment, the present application has the following advantages over the conventional scheme:

-ensuring consistency of reporting understanding of CSI by the transmitting and receiving end;

-support AI-based CSI reporting;

-reducing system measurement resources and overhead;

-enhancing the forward-backward compatibility of the system;

-improving the flexibility and adaptability of the system;

-improving the reliability and robustness of the system;

-simplifying the system design, reducing the complexity of implementation of the scheme;

-improving the accuracy and effectiveness of information reporting;

-enhancing the overall performance of the system.

Drawings

Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings in which:

fig. 1 shows a flowchart of a first DCI, at least one CSI reporting configuration, and a target CSI according to an embodiment of the present application;

FIG. 2 shows a schematic diagram of a network architecture according to one embodiment of the application;

Fig. 3 shows a schematic diagram of an embodiment of a radio protocol architecture of a user plane and a control plane according to an embodiment of the application;

FIG. 4 shows a schematic diagram of a first communication device and a second communication device according to one embodiment of the application;

fig. 5 shows a flow chart of wireless transmission according to an embodiment of the application;

FIG. 6 shows a schematic diagram of a calculation formula for a first time interval according to one embodiment of the application;

FIG. 7 shows a schematic diagram of N information blocks and N time units according to one embodiment of the application;

FIG. 8 shows a schematic diagram of a second symbol and a second reference symbol in accordance with an embodiment of the application;

FIG. 9 illustrates a schematic diagram of a first operation according to one embodiment of the application;

FIG. 10 shows a schematic diagram of a first type of identity according to one embodiment of the application;

FIG. 11 illustrates a schematic diagram of a first time interval and a first type of identification relationship, according to one embodiment of the application;

FIG. 12 shows a schematic diagram of a second set of resources according to an embodiment of the application;

FIG. 13 shows a schematic diagram in which a first condition is not satisfied according to an embodiment of the application;

FIG. 14 illustrates a schematic diagram of a second operation according to one embodiment of the application;

FIG. 15 illustrates a schematic diagram of a first operation according to another embodiment of the present application;

FIG. 16 shows a schematic diagram of a deployment first operation according to an embodiment of the application;

FIG. 17 shows a schematic diagram of an artificial intelligence or machine learning based processing system in accordance with an embodiment of the application;

FIG. 18 shows a schematic diagram based on artificial intelligence or machine learning according to an embodiment of the application;

FIG. 19 shows a block diagram of a processing arrangement for use in a first node according to an embodiment of the application;

fig. 20 shows a block diagram of a processing arrangement for use in a second node according to an embodiment of the application.

Detailed Description

The technical scheme of the present application will be described in further detail with reference to the accompanying drawings, and it should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be arbitrarily combined with each other. Based on performance, flexibility, complexity, overhead, and compatibility considerations, those skilled in the art will be motivated to flexibly combine the embodiments of the different figures, such as, but not limited to, the embodiment of fig. 1 and the embodiments of fig. 5-20, the embodiment of fig. 5 and the embodiments of fig. 6-20, and so forth, without conflict.

Example 1

Embodiment 1 illustrates a flowchart of a first DCI, at least one CSI reporting configuration, and a target CSI according to an embodiment of the present application, as shown in fig. 1. In 100 shown in fig. 1, each block represents a step. In particular, the order of steps in the blocks does not represent a particular chronological relationship between the individual steps.

In embodiment 1, the first node in the present application receives at least one CSI reporting configuration in step 101, receives a first DCI on a first PDCCH in step 102, determines whether to transmit a target CSI on the first PUSCH in step 103, and transmits the target CSI on the first PUSCH in step 104 only when a first condition is satisfied;

The method comprises the steps of triggering reporting of at least one CSI on a first PUSCH by the first DCI, enabling the at least one CSI reporting configuration to be used for configuring reporting of the at least one CSI, enabling a target CSI reporting configuration to be used for configuring reporting of the target CSI, enabling the target CSI reporting configuration to be one of the at least one CSI reporting configuration, enabling the target CSI to be one of the at least one CSI, enabling the target CSI reporting configuration to indicate a first resource set to be used for at least one of channel measurement or interference resource measurement of the target CSI, enabling the first resource set to comprise one or more RS resources, enabling the first condition to comprise a first symbol not earlier than a first reference symbol, enabling the first symbol to be a first uplink symbol in the first PUSCH for carrying the at least one CSI, enabling a CP to start from a next uplink symbol of a first time interval after the end of a last symbol of the first PDCCH, and enabling the first time interval to depend on whether the target CSI is generated or not.

As an embodiment, the at least one CSI reporting configuration is carried by higher layer (HIGHER LAYER) signaling.

As an embodiment, the at least one CSI reporting configuration is carried by RRC (Radio Resource Control ) signaling.

As an embodiment, the at least one CSI reporting configuration is carried by one RRC IE (Information Element ).

As an embodiment, the at least one CSI reporting configuration is carried by at least one RRC IE.

As an embodiment, the at least one CSI reporting configuration includes information in one or more domains in at least one RRC IE.

As an embodiment, the at least one CSI reporting configuration includes information in one or more domains of each of a plurality of RRC IEs.

As an embodiment, the at least one CSI reporting configuration includes part or all of the fields in the CSI-ReportConfig IE.

As an embodiment, the at least one CSI reporting configuration includes some or all of the fields in ServingCellConfig IE.

As an embodiment, the at least one CSI reporting configuration includes part or all of the fields in the CSI-MeasConfig IE.

As an embodiment, the at least one CSI reporting configuration includes some or all of the fields in ServingCellConfigCommon IE.

As an embodiment, the at least one CSI reporting configuration includes some or all of the fields in ServingCellConfigCommonSIB IE.

As an embodiment, any CSI reporting configuration of the at least one CSI reporting configuration is transmitted on PDSCH.

As an embodiment, CSI reporting of any CSI reporting configuration of the at least one CSI reporting configuration is periodic.

As an embodiment, CSI reporting of any of the at least one CSI reporting configuration is semi-persistent.

As an embodiment, CSI reporting of any one of the at least one CSI reporting configuration is aperiodic.

As an embodiment, any CSI reporting configuration of the at least one CSI reporting configuration is an RRC IE.

As an embodiment, any CSI reporting configuration of the at least one CSI reporting configuration belongs to a CSI-ReportConfig IE.

As an embodiment, any CSI reporting configuration of the at least one CSI reporting configuration belongs to ServingCellConfig IE.

As an embodiment, any CSI reporting configuration of the at least one CSI reporting configuration belongs to a CSI-MeasConfig IE.

As an embodiment, any CSI reporting configuration of the at least one CSI reporting configuration belongs to ServingCellConfigCommon IE.

As an embodiment, any CSI reporting configuration of the at least one CSI reporting configuration belongs to ServingCellConfigCommonSIB IE.

As an embodiment, the first set of resources includes one or more RS resources.

As an embodiment, the first set of resources includes one or more downlink RS resources.

As an embodiment, the first set of resources includes periodic (periodic) RS resources.

As one embodiment, the first set of resources includes semi-persistent (semi-persistent) RS resources.

As an embodiment, the first set of resources includes aperiodic (aperiodic) RS resources.

As one embodiment, the first set of resources consists of one or more aperiodic RS resources.

As an embodiment, the resources in the first set of resources include at least one of antenna ports, TCI (Transmission Configuration Indication, transmit configuration indication) status, QCL (Quasi Co-Location) information, time-frequency resources, time-frequency code resources, beams, RS resources, vectors, or matrices.

As an embodiment, the first set of resources includes one or more sets of RS (REFERENCE SIGNAL ) resources, and one set of RS resources includes one or more RS resources.

As an embodiment, the first set of resources comprises at least one of at least one CSI-RS set of resources, at least one CSI-SSB (CHANNEL STATE Information-Synchronization Signal Block) set of resources, or at least one CSI-IM (CHANNEL STATE Information-INTERFERENCE MEASUREMENT) set of resources.

As an embodiment, the first set of resources includes at least one set of RS resources for channel measurement.

As an embodiment, the first set of resources includes at least one set of RS resources for channel measurement and at least one set of RS resources for interference measurement.

As an embodiment, the first set of resources comprises at least one set of RS resources for interference measurement.

As a sub-embodiment of the above embodiment, one set of RS resources for channel measurement includes one or more RS resources.

As a sub-embodiment of the above embodiment, one set of RS resources for interference measurement includes one or more RS resources.

As an embodiment, one set of RS resources for channel measurement includes one or more RS resources, and any one of the one set of RS resources for channel measurement is a CSI-RS resource or a synchronization signal resource.

As an embodiment, one set of RS resources for interference measurement includes one or more RS resources, and any one of the one set of RS resources for interference measurement is a CSI-IM resource or an NZP (non-zero power) CSI-RS resource for interference measurement.

As an embodiment, the first set of resources includes one or more RS resources, any RS resource in the first set of resources being a CSI-RS (CHANNEL STATE Information REFERENCE SIGNAL) resource or a synchronization signal resource.

As an embodiment, the synchronization signal resources comprise at least resources occupied by synchronization signals.

As an embodiment, the synchronization signal resource is an SSB (Synchronization Signal Block ).

As an embodiment, the synchronization signal resource is an SS/PBCH (synchronization signal/physical broadcast channel ) block (block) resource.

As one embodiment, the first set of resources consists of at least one aperiodic CSI-RS resource for channel measurement.

As an embodiment, the first set of resources consists of at least one aperiodic CSI-RS resource for channel measurement, at least one aperiodic CSI-IM resource for interference measurement, or at least one aperiodic NZP CSI-RS resource for interference measurement.

The first set of resources is comprised of at least one of one or more aperiodic RS resources, at least one aperiodic CSI-IM resource for interference measurement, or at least one aperiodic NZP CSI-RS resource for interference measurement.

As an embodiment, the first set of resources consists of at least one CSI-RS resource.

As an embodiment, the first set of resources includes at least one of CSI-RS resources or SS/PBCH block resources.

As an embodiment, the reference resource is a CSI reference resource.

As an embodiment, the reference resource is a CSI reference resource of the target CSI.

As one example, the benefits of the above approach include reduced complexity along with existing standards and system designs.

As an embodiment, the target CSI reporting configuration indicates at least one resource configuration, the at least one resource configuration indicating the first set of resources.

As an embodiment, the target CSI reporting configuration comprises at least one resource configuration, the at least one resource configuration indicating the first set of resources.

As one embodiment, one resource configuration is used to configure CSI resources.

As an embodiment, one resource configuration is one IE CSI-ResourceConfig.

As one embodiment, one resource configuration is carried by an RRC IE.

As an embodiment, one resource configuration is carried by CSI-ResourceConfig IE.

As an embodiment, the target CSI reporting configuration indicates configuration information of the first set of resources.

As an embodiment, the target CSI reporting configuration indicates an identity of the first set of resources.

As an embodiment, the first DCI (Downlink Control Information ) triggers the target CSI on a first PUSCH (Physical uplink SHARED CHANNEL ).

As an embodiment, the first DCI triggers reporting of at least one CSI on a first PUSCH, where the reporting of the at least one CSI includes the target CSI.

As an embodiment, the first DCI triggers reporting of at least one CSI on a first PUSCH, where the reporting of the at least one CSI includes the target CSI, and the target CSI reporting configuration is used to configure reporting of the target CSI.

As an embodiment, the receiving the first DCI on the first PDCCH includes receiving a second signal on the first PDCCH, the second signal carrying the first DCI.

As an embodiment, the first PDCCH includes a plurality of REs (Resource elements).

Typically, one RE occupies one symbol in the time domain and one subcarrier in the frequency domain.

As an embodiment, the first PDCCH occupies at least one symbol in the time domain, and the first PDCCH occupies at least one subcarrier in the frequency domain.

As an embodiment, the first PDCCH occupies at least one symbol in a time domain, and the first PDCCH occupies at least one RB (resource block) in a frequency domain.

As an embodiment, the symbol is a single carrier symbol.

As an embodiment, the symbol is a multicarrier symbol.

As an embodiment, the multi-carrier symbol is an OFDM (Orthogonal Frequency Division Multiplexing ) symbol.

As an embodiment, the symbol is obtained after the output of the conversion precoder (transform precoding) has undergone OFDM symbol Generation (Generation).

As an embodiment, the multi-carrier symbol is an SC-FDMA (SINGLE CARRIER-Frequency Division Multiple Access, single carrier frequency division multiple access) symbol.

As an embodiment, the multi-carrier symbol is a DFT-S-OFDM (Discrete Fourier Transform Spread OFDM, discrete fourier transform orthogonal frequency division multiplexing) symbol.

As an embodiment, the multi-carrier symbol is an FBMC (Filter Bank Multi Carrier, filter bank multi-carrier) symbol.

As an embodiment, the multicarrier symbol includes CP (Cyclic Prefix).

As an embodiment, the first DCI includes a CSI request field, where the CSI request field in the first DCI triggers at least one CSI on a first PUSCH.

As an embodiment, the first DCI includes a CSI request field, where the CSI request field in the first DCI indicates at least one CSI reporting configuration, and the at least one CSI reporting configuration is used to configure reporting of the at least one CSI.

As an embodiment, the first DCI includes a CSI request field, where the CSI request field in the first DCI indicates at least one CSI reporting configuration, a target CSI reporting configuration is one of the at least one CSI reporting configuration, the at least one CSI reporting configuration is used to configure reporting of the at least one CSI, and the target CSI reporting configuration is used to configure reporting of the target CSI.

As an embodiment, the generation of the target CSI relies on measurements obtained based on the first set of resources.

As an embodiment, the generation of the target CSI relies on channel measurements and/or interference measurements obtained based on the first set of resources.

As one embodiment, measurements based on the first set of resources are used to generate the target CSI.

As an embodiment, channel measurements and/or interference measurements based on the first set of resources are used to generate the target CSI.

As one embodiment, measurements based on one or more RS resources in the first set of resources are used to generate the target CSI.

As one embodiment, a measurement based on a transmission occasion no later than a reference resource in one or more RS resources in the first set of resources is used to generate the target CSI.

As one embodiment, a measurement based on a most recent one or more transmission occasions of one or more RS resources of the first set of resources that are no later than a reference resource is used to generate the target CSI.

As an embodiment, the channel measurements obtained based on the first set of resources refers to channel measurements obtained based on at least one reference signal transmitted in the first set of resources.

As an embodiment, the channel measurements obtained based on the first set of resources refers to channel measurements obtained in the first set of resources.

As one embodiment, the channel measurements obtained based on the first set of resources include at least one of a channel matrix (channel matrix), an original channel matrix (RAW CHANNEL matrix), a eigenvector, and an eigenvalue (eigenvalue).

As one embodiment, the channel measurements obtained based on the first set of resources include one or more of BLER, delay spread, doppler shift, average delay, average gain, path loss, and RSRP.

As an embodiment, the interference measurement obtained based on the first set of resources refers to an interference measurement obtained based on at least one reference signal transmitted in the first set of resources.

As an embodiment, the interference measurement obtained based on the first set of resources refers to an interference measurement obtained in the first set of resources.

As one embodiment, the interference measurement obtained based on the first set of resources comprises at least one of interference power, interference variance, or interference power spectral density.

As an embodiment, the interference measurement obtained based on the first set of resources comprises at least one of an interference channel matrix, an interference covariance matrix, an interference eigenvector, an interference eigenvalue, an interference beam.

As one embodiment, the measurement based on the first set of resources includes a channel matrix obtained based on the measurement for the first set of resources.

As an embodiment, the measurement based on the first set of resources comprises a matrix or vector obtained by preprocessing a channel matrix obtained based on the measurement for the first set of resources.

As an embodiment, the channel matrix is a spatial-frequency domain (spatial-frequency domain).

As an example, the channel matrix is angular-extended (angular-delay domain projection).

As one embodiment, the preprocessing includes one or more of quantization, DFT (Discrete Fourier Transform ), matrix decomposition, matrix transformation or projection, spatial to angular domain transformation, angular domain to spatial domain transformation, frequency to time domain transformation and time to frequency domain transformation, puncturing, padding, mapping, and labeling.

As an embodiment, the at least one CSI is the target CSI, or the at least one CSI includes a plurality of CSI, the target CSI being one of the plurality of CSI.

As an embodiment, the at least one CSI includes only one CSI, the at least one CSI is the target CSI, and the at least one CSI reporting configuration is the target CSI reporting configuration.

As an embodiment, the at least one CSI includes a plurality of CSI, and the at least one CSI reporting configuration includes a plurality of CSI reporting configurations, which are used to configure the plurality of CSI, respectively.

As an embodiment, the at least one CSI includes a plurality of CSI, the at least one CSI reporting configuration includes a plurality of CSI reporting configurations, the plurality of CSI reporting configurations are respectively used to configure the plurality of CSI, and the target CSI is any CSI of the plurality of CSI.

As an embodiment, the target CSI includes at least one CSI report amount.

As an embodiment, the target CSI includes one or more of PMI (Precoding Matrix Indicator, precoding indication), CRI (CSI-RS Resource Indicator, channel state information reference signal resource Indicator), SS/PBCH block resource Indicator (SS/PBCH Block Resource Indicator, SSBRI), beam indication, resource indication, CQI (Channel quality Indicator, channel quality indication), RI (Rank Indicator, rank indication), layer indication (Layer Indicator, LI), RSRP (REFERENCE SIGNAL RECEIVED power ), SINR (signal-to-noise AND INTERFERENCE ratio), capability Index (Capability Index), or TDCP (Time Domain Channel Properties, time domain channel characteristics).

As an embodiment, the target CSI includes one or more of a channel matrix, eigenvectors, eigenvalues, or a precoding matrix.

As an embodiment, the target CSI includes one or more of beam indication, CRI (CSI-RS Resource Indicator, channel state information reference signal resource indicator), SS/PBCH block resource indicator (SS/PBCH Block Resource indicator, SSBRI), or RSRP (REFERENCE SIGNAL RECEIVED power ).

As one embodiment, the target CSI includes one or more of a beam indication, a number of beams, CRI, SS/PBCH block resource indicator, number of CRI or SSBRI, RSRP, differential (differential) RSRP, probability information (Probability information), or confidence information (confidence information).

As one embodiment, the probability information indicates a probability that the corresponding beam is the optimal beam or beams.

As an embodiment, the probability information represents a probability that the corresponding RS resource is the optimal one or more RS resources.

As one embodiment, the confidence information indicates the accuracy of the RSRP.

As one embodiment, the confidence information indicates the accuracy of the differential RSRP.

As one embodiment, the essence of the above method includes monitoring (monitoring) the AI model based on performance parameters associated with CSI reporting of the AI.

As one embodiment, the method has the advantages of improving the performance of the AI-based CSI reporting scheme and improving the overall performance of the system.

As an embodiment, the target CSI includes RSRP.

As an embodiment, the target CSI comprises at least one resource indicator and RSRP.

As an embodiment, the target CSI comprises at least one resource indicator.

As an embodiment, the target CSI comprises at least one resource indicator, one of the resource indicators in the target CSI being used to indicate a beam or RS resource.

As an embodiment, the target CSI comprises at least one resource indicator, one of the target CSI being used to indicate a beam, CSI-RS resource or SS/PBCH block resource.

As an embodiment, the target CSI comprises at least one resource indicator, one of the target CSI is used to indicate a beam, or one of the target CSI is CRI (CSI-RS Resource Indicator, channel state information reference signal resource indicator) or SS/PBCH block resource indicator (SS/PBCH Block Resource indicator, SSBRI).

As one embodiment, the target CSI comprises predicted CSI.

As one embodiment, the target CSI comprises CSI for a future period of time.

As one embodiment, the target CSI includes predicted beam information.

As one embodiment, the target CSI includes beam information for a future period of time.

As a sub-embodiment of the above embodiment, the future period of time comprises at least one time domain resource subsequent to the current time domain resource.

As a sub-embodiment of the above embodiment, the future period of time comprises at least one time unit after the current time unit.

As a sub-embodiment of the above embodiment, the future period of time comprises at least one time slot after the current time slot (slot).

As a sub-embodiment of the above embodiment, the future period of time comprises at least one symbol after the current symbol (symbol).

As one embodiment, the benefits of the above method include reduced channel measurement overhead.

As one embodiment, the method has the advantages of improving the accuracy and instantaneity of the CSI reporting and enhancing the overall performance of the system.

As one embodiment, the target CSI comprises compressed CSI.

As an embodiment, the compressed CSI is non-codebook based.

As an embodiment, the compressed CSI does not belong to the CSI defined by 3GPP Rel-18, nor to the CSI defined by the earlier release of 3GPP Rel-18.

As one embodiment, the target receiver of the compressed CSI is unknown to the sender of the compressed CSI based on the channel parameters recovered by the compressed CSI.

As one embodiment, the method has the advantages of saving CSI feedback overhead and improving the overall performance of the system.

As one embodiment, the target CSI is AI-based.

As one embodiment, the target CSI is not AI-based.

As an embodiment, the reporting amount included in the target CSI depends on whether the generation of the target CSI is AI-based.

As one embodiment, the target CSI is based on whether the generation of the non-codebook dependent target CSI is based on AI, when the generation of the target CSI is based on AI, the target CSI is based on non-codebook, and when the generation of the target CSI is not based on AI, the target CSI is based on codebook.

As an embodiment, the target CSI belongs to CSI defined by 3GPP Rel-18 when the generation of the target CSI is not AI-based.

As an embodiment, when the generation of the target CSI is AI-based, the target CSI does not belong to CSI defined by 3GPP Rel-18 and earlier releases.

As one embodiment, whether the target CSI includes confidence information (confidence information) depends on whether the generation of the target CSI is AI-based, and the target CSI includes confidence information only when the generation of the target CSI is AI-based.

As one embodiment, when the generation of the target CSI is AI-based, the target CSI includes predicted CSI, or predicted beam information, or compressed CSI.

As one embodiment, the benefits of the above method include supporting an AI-based CSI reporting scheme.

As one example, the benefits of the above method include small changes to existing systems and standards.

As an embodiment, the target CSI indicates at least one RS resource in the first set of resources.

As an embodiment, the target CSI indicates at least one resource in a second set of resources, the second set of resources including resources not belonging to the first set of resources.

As an embodiment, when the generation manner of the target CSI is AI-based, the target CSI indicates at least one resource in a second set of resources, the second set of resources including resources not belonging to the first set of resources.

As an embodiment, when the generation mode of the target CSI is based on AI, the target CSI indicates at least one resource in a second resource set, where the second resource set includes resources not belonging to the first resource set, and when the generation mode of the target CSI is not based on AI, the target CSI indicates at least one RS resource in the first resource set.

As an embodiment, for the case where the generation of the target CSI is not AI-based, how to generate the target CSI is determined by the manufacturer of the first node or is implementation dependent. A typical but non-limiting embodiment is described below:

The first node firstly measures the first resource set to obtain a channel parameter matrix H _r×t, wherein r and t are the number of receiving antennas and the number of antenna ports of the target CSI-RS resource respectively, and the power of the channel parameter matrix H _r×t is adjusted to obtain an adjusted channel parameter matrix which is Wherein P is the ratio of the assumed PDSCH EPRE to the target CSI-RS EPRE (namely the first power control offset), and the channel parameter matrix after precoding is as follows under the condition of adopting a precoding matrix W _t×1 Where 1 is the rank (rank) or the number of layers, 1 is a positive integer not greater than t in one case, and the precoding matrix is an identity matrix in another case, where t=1, and calculating the equivalent channel capacity of H _r×t·W_t×1 using, for example, SINR (SIGNAL INTERFERENCE Noise Ratio, signal to interference and Noise Ratio), EESM (Exponential EFFECTIVE SINR MAPPING ), or RBIR (Received Block mean mutual Information Ratio, block average mutual information rate) criteria, and then determining the CQI included in the target CSI report by means of table look-up from the equivalent channel capacity. In general, the calculation of equivalent channel capacity requires the first node to estimate interference (including noise), which can be obtained by the first node with the measurement of the second set of opportunities in the present application. In general, the direct mapping of the equivalent channel capacity to the CQI value depends on the receiver performance, or hardware related factors such as the modulation scheme.

As an embodiment, for the case where the generation of the target CSI is AI-based, how to generate the target CSI is determined by the manufacturer of the first node or is implementation dependent. Without loss of generality, the AI model or parameters employed to generate the target CSI are determined by the manufacturer of the first node.

As one embodiment, the determining whether to transmit the target CSI on the first PUSCH includes determining whether to transmit the target CSI on the first PUSCH and the target CSI is valid.

As one embodiment, the determining whether to transmit the target CSI on the first PUSCH includes determining whether to transmit the target CSI on the first PUSCH or ignore the first DCI.

As one embodiment, the determining whether to transmit the target CSI on the first PUSCH includes determining whether to transmit the target CSI on the first PUSCH or to forgo transmitting the target CSI on the first PUSCH.

Typically, the target CSI is transmitted on the first PUSCH only when a first condition is met, wherein the target CSI transmitted on the first PUSCH is valid.

As one embodiment, the target CSI being valid includes the target CSI being updated (updated) CSI.

As one embodiment, the target CSI being valid includes the target CSI being different from a CSI configured for the target CSI reporting earlier than a most recent one on the first PUSCH.

As an embodiment, the target CSI being valid includes that the target CSI may be different from a CSI configured for the target CSI reporting earlier than a most recent one on the first PUSCH.

As an embodiment, the target CSI being valid includes that the target CSI is not necessarily the same as the most recent CSI configured for the target CSI reporting on the first PUSCH earlier.

As one embodiment, the target CSI being valid includes whether the target CSI differs from a CSI that is earlier than a last one on the first PUSCH configured for the target CSI reporting by a measurement of a last RS occasion of a CSI reference resource in the first set of resources that is no later than the target CSI.

As one embodiment, the target CSI being valid includes the target CSI being generated based on at least a measurement of a most recent RS occasion of CSI reference resources in the first set of resources that is no later than the target CSI.

As one embodiment, the target CSI being valid includes the target CSI being CSI updated based on at least a measurement of a most recent RS occasion of a CSI reference resource in the first set of resources that is no later than the target CSI.

As one embodiment, the first set of resources consists of one or more aperiodic RS resources; the target CSI being valid includes the target CSI being generated based on measurements of aperiodic RS resources in the first set of resources triggered by the first DCI.

The target CSI is effective, as one embodiment, including the target CSI being updated based on measurements of aperiodic RS resources in the first set of resources triggered by the first DCI.

As an embodiment, the first PUSCH includes a plurality of REs (Resource elements).

As an embodiment, the first PUSCH occupies at least one symbol in the time domain, and the first PUSCH occupies at least one subcarrier in the frequency domain.

As an embodiment, the first PUSCH occupies at least one symbol in a time domain, and the first PUSCH occupies at least one RB (resource block) in a frequency domain.

As an embodiment, the sending the target CSI on the first PUSCH includes sending a first signal on the first PUSCH, where the first signal carries the target CSI.

As an embodiment, the first signal comprises a baseband signal.

As one embodiment, the first signal comprises a wireless signal.

As an embodiment, the first signal comprises a radio frequency signal.

As an embodiment, the transmitting the target CSI on the first PUSCH includes the target CSI being used to generate a signal transmitted on the first PUSCH after being channel coded.

As an embodiment, the transmitting the target CSI on the first PUSCH includes the target CSI being used to generate a signal transmitted on the first PUSCH after being channel coded, modulated.

As an embodiment, the transmitting the target CSI on the first PUSCH includes the target CSI being used to generate a signal transmitted on the first PUSCH after being subjected to bit sequence generation, channel coding.

As an embodiment, the transmitting the target CSI on the first PUSCH includes the target CSI being used to generate a signal transmitted on the first PUSCH after being subjected to bit sequence generation, channel coding, modulation.

As one embodiment, the transmitting the target CSI on the first PUSCH includes the target CSI being used to generate a signal transmitted on the first PUSCH after bit sequence generation (bit sequence genertion), code block segmentation (code block segmentation), and CRC addition (attachment), channel coding (channel coding), rate matching (RATE MATCHING), code block concatenation (code block concatenation).

As an embodiment, the transmitting the target CSI on the first PUSCH includes the target CSI being multiplexed to the first PUSCH after undergoing bit sequence generation, code block segmentation and CRC addition, channel coding, rate matching, code block concatenation.

Typically, the first symbol takes into account a timing advance (TIMING ADVANCE).

Typically, the first symbol and the first reference symbol both take into account a timing advance (TIMING ADVANCE).

As an embodiment, the first symbol is a first uplink symbol in the first PUSCH for carrying the target CSI.

As an embodiment, the first reference symbol is Z _ref, and the specific meaning of Z _ref is referred to in section 5.4 of 3gpp ts 38.214.

Typically, the next uplink symbol is the one that is earliest in time.

Typically, the last symbol of the first PDCCH refers to the latest symbol occupied by the first PDCCH.

Typically, the first reference symbol being the next uplink symbol (the next uplink symbol) of the CP starting (starting) a first time interval after the end of the last symbol of the first PDCCH (AFTER THE END of the last symbol of THE FIRST PDCCH) comprises that the first reference symbol is the earliest uplink symbol later than the last symbol of the first PDCCH and meeting a reference condition, the reference condition comprising that the time interval between the end of the last symbol of the first PDCCH and the end of the last symbol of the first PDCCH is not less than the first time interval.

As an embodiment, the first time interval is a real number or an integer.

As an embodiment, the first time interval is in milliseconds (ms).

As an embodiment, the unit of the first time interval is a symbol.

As an embodiment, the first time interval is T _proc,CSI, and the specific meaning of T _proc,CSI is referred to section 5.4 of 3gpp ts 38.214.

Typically, the determining whether to transmit the target CSI on the first PUSCH depends on whether the first condition is met.

As one embodiment, the first condition is not satisfied when the first symbol is earlier than the first reference symbol, and the first condition is satisfied when the first symbol is not earlier than the first reference symbol.

As one embodiment, the first set of resources consists of one or more periodic or semi-persistent RS resources, the first condition is not satisfied when the first symbol is earlier than the first reference symbol, and the first condition is satisfied when the first symbol is not earlier than the first reference symbol.

As an embodiment, the transmitting the target CSI on the first PUSCH only when a first condition is satisfied includes ignoring the first DCI when the first condition is not satisfied.

As an embodiment, the transmitting the target CSI on the first PUSCH only when the first condition is satisfied includes relinquishing transmitting the target CSI on the first PUSCH when the first condition is not satisfied.

As an embodiment, the transmitting the target CSI on the first PUSCH only when a first condition is satisfied includes transmitting the target CSI on the first PUSCH and the target CSI being valid when the first condition is satisfied, and transmitting the target CSI on the first PUSCH and the target CSI being not updated when the first condition is not satisfied.

As an embodiment, no HARQ-ACK or transport block is multiplexed on the first PUSCH, and the transmitting the target CSI on the first PUSCH only when a first condition is satisfied includes ignoring the first DCI when the first condition is not satisfied.

As an embodiment, no HARQ-ACK or transport block is multiplexed on the first PUSCH, and the transmitting the target CSI on the first PUSCH only when a first condition is satisfied comprises discarding transmitting the target CSI on the first PUSCH when the first condition is not satisfied.

As an embodiment, there is a HARQ-ACK or transport block multiplexed on the first PUSCH, the transmitting the target CSI on the first PUSCH only when a first condition is satisfied includes transmitting the target CSI on the first PUSCH and the target CSI being valid when the first condition is satisfied, and transmitting the target CSI on the first PUSCH and the target CSI being not updated when the first condition is not satisfied.

Typically, no HARQ-ACK or transport block is multiplexed on the first PUSCH.

As one example, benefits of the above approach include improved system flexibility and overall performance.

As one example, the benefits of the above-described method include small changes to existing systems and standards.

As one embodiment, the generation mode of the target CSI is based on AI, and the generation mode of the target CSI is not based on AI, and comprises that the generation of the target CSI does not use an AI model.

As one embodiment, the generation mode of the target CSI is based on AI, wherein the target CSI comprises information based on artificial intelligence or machine learning, and the generation mode of the target CSI is not based on AI, wherein the target CSI does not comprise information based on artificial intelligence or machine learning.

As one embodiment, the generation of the target CSI is based on AI including information generated based on a neural network (Neural Network), and the generation of the target CSI is not based on AI including no information generated based on a neural network (Neural Network).

As one embodiment, the generation mode of the target CSI is based on AI, wherein the target CSI comprises information generated based on CNN (Conventional Neural Networks, convolutional neural network), and the generation mode of the target CSI is not based on AI, wherein the target CSI does not comprise information generated based on CNN.

As one embodiment, the generation of the target CSI is based on AI, and the generation of the target CSI comprises the sender of the target CSI executing a first operation, wherein the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation, and the generation of the target CSI is not based on AI, and the generation of the target CSI does not comprise the sender of the target CSI executing the first operation.

As one embodiment, the generation mode of the target CSI is based on AI, and the generation mode of the target CSI is not based on AI, and the generation mode of the target CSI comprises that the target CSI reporting configuration does not indicate the first type of identification.

As one embodiment, the generation of the target CSI is based on AI, which includes that the generation of the target CSI is associated with a first type identifier, and the generation of the target CSI is not based on AI, which includes that the generation of the target CSI is not associated with the first type identifier.

As one embodiment, the benefits of the above approach include simplified system design and reduced implementation complexity.

As an embodiment, when the generation manner of the target CSI is not AI-based, the first time interval is (Z) (2048+144) ·k ^-μ·T_C+T_switch, wherein the specific meanings of Z, k, μ, T _C and T _switch refer to section 5.4 of 3gpp ts 38.214.

As an embodiment, when the generation mode of the target CSI is not AI-based, the first time interval is T _proc,CSI, and the specific meaning of T _proc,CSI refers to section 5.4 of 3gpp ts 38.214.

As one example, the benefits of the above method include small changes to existing standards and systems.

As an embodiment, whether the first time interval depends on the generation mode of the target CSI is based on AI includes whether the determination method of the first time interval depends on the generation mode of the target CSI is based on AI.

As an embodiment, whether the first time interval depends on the generation mode of the target CSI is based on AI includes whether the calculation formula of the first time interval depends on the generation mode of the target CSI is based on AI.

As an embodiment, the first time interval depending on whether the generation mode of the target CSI is based on AI includes that the calculation formulas of the first time interval are different in the case that the generation mode of the target CSI is based on AI and not based on AI.

As one embodiment, the calculation formula of the first time interval depends on whether the generation mode of the target CSI is based on AI or not, wherein the calculation formula of the first time interval is a first calculation formula when the generation mode of the target CSI is based on AI, and is a second calculation formula when the generation mode of the target CSI is not based on AI, and the first calculation formula and the second calculation formula are different.

As an embodiment, the calculation formula of the first time interval depends on whether the generation mode of the target CSI is based on AI or not, which includes that when the generation mode of the target CSI is based on AI, the first time interval is (Z) (2048+144) ·k ^-μ·T_C+T_switch +w, wherein W is an integer greater than zero or a real number, and when the generation mode of the target CSI is not based on AI, the first time interval is (Z) (2048+144) ·k ^-μ·T_C+T_switch, wherein the specific meanings of Z, k, μ, T _C and T _switch refer to section 5.4 of 3gpp ts 38.214.

As an embodiment, the calculation formula of the first time interval depends on whether the generation mode of the target CSI is AI-based or not, including that when the generation mode of the target CSI is AI-based, the first time interval is a· [ (Z) (2048+144) ·k ^-μ·T_C+T_switch ], wherein a is an integer or a real number greater than 1, when the generation mode of the target CSI is not AI-based, the first time interval is (Z) (2048+144) ·k ^-μ·T_C+T_switch, wherein the specific meanings of Z, k, μ, T _C and T _switch refer to section 5.4 of 3gpp ts 38.214.

As one embodiment, the method has the advantages that larger CSI calculation time is reserved for the CSI reporting based on the AI, an AI model and calculation are better supported, and the accuracy and the effectiveness of the CSI reporting are improved.

As one embodiment, the first time interval depending on whether the generation manner of the target CSI is based on AI comprises the first time interval being a first reference interval when the generation manner of the target CSI is based on AI and the first time interval being a second reference interval when the generation manner of the target CSI is not based on AI.

As an embodiment, the first reference interval is not equal to the second reference interval.

As one embodiment, the first reference interval is a real number and the second reference interval is a real number.

As an embodiment, the first reference interval is an integer and the second reference interval is an integer.

As one embodiment, the first reference interval is in units of milliseconds (ms) and the second reference interval is in units of milliseconds.

As an embodiment, the unit of the first reference interval is a symbol and the unit of the second reference interval is a symbol.

As an embodiment, the first reference interval comprises a plurality of candidate values.

As an embodiment, the first reference interval is calculated by a formula.

As an embodiment, the first reference interval is fixed.

As an embodiment, the first reference interval is configurable.

As an embodiment, the first reference interval is configured by higher layer signaling.

As one embodiment, the generation of the target CSI uses an AI model, and the first reference interval depends on the AI model.

As an embodiment, the target CSI depends on an output of the first operation, and the first reference interval depends on the first operation.

As one embodiment, the generation of the target CSI is associated with a first type of identity, and the first reference interval is dependent on the first type of identity.

As an embodiment, the first reference interval depends on UE capability information.

As an embodiment, the second reference interval comprises a plurality of candidate values.

As an embodiment, the second reference interval is calculated by a formula.

As an embodiment, the second reference interval is fixed.

As an embodiment, the second reference interval is configurable.

As an embodiment, the second reference interval is configured by higher layer signaling.

As an embodiment, the second reference interval is (Z) (2048+144) ·k2 ^-μ·T_C+T_switch, wherein the specific meaning of Z, the k, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214.

As an example, the second reference interval is T _proc,CSI, and the specific meaning of T _proc,CSI is referred to section 5.4 of 3gpp ts 38.214.

As one embodiment, the essence of the above method includes setting different CSI calculation times for AI-based CSI reporting and CSI reporting that is not AI-based.

As one embodiment, the method has the advantages of improving the flexibility of the system and adapting to the transmission and application of different scenes and requirements.

As an embodiment, whether the first time interval depends on the generation mode of the target CSI is based on AI includes whether the parameter that the first time interval depends on the generation mode of the target CSI is based on AI.

As an embodiment, whether the first time interval depends on the generation mode of the target CSI is based on AI includes whether the parameter dependent on the calculation formula of the first time interval depends on the generation mode of the target CSI is based on AI.

As an embodiment, whether the first time interval depends on the generation mode of the target CSI is based on AI includes that the calculation formula of the first time interval depends on a first parameter, and the first parameter depends on whether the generation mode of the target CSI is based on AI.

As one embodiment, whether the first time interval depends on the generation mode of the target CSI is based on AI includes whether the first time interval calculation formula depends on a second parameter, and whether the first time interval calculation formula depends on the second parameter only when the target CSI generation mode is based on AI.

As an embodiment the calculation formula of the first time interval depends on the second parameter comprising that the first time interval and the second parameter are linear.

As an embodiment the calculation formula of the first time interval depends on the second parameter comprising that the first time interval and the second parameter are non-linear.

As an embodiment, the calculation formula of the first time interval depends on the second parameter, wherein when the generation mode of the target CSI is AI-based, the first time interval is (Z) (2048+144) ·k ^-μ·T_C+T_switch +w, wherein the specific meanings of Z, k, μ, T _C and T _switch refer to section 5.4 of 3gpp ts38.214, and W is the second parameter.

As an embodiment, the unit of the second parameter is milliseconds (ms).

As an embodiment, the unit of the second parameter is a symbol.

As an embodiment, the second parameter is an integer greater than 0 or a real number.

As an embodiment, the calculation formula of the first time interval depends on the second parameter, wherein when the generation mode of the target CSI is AI-based, the first time interval is a· [ (Z) (2048+144) ·k ^-μ·T_C+T_switch ], wherein the specific meanings of Z, k, μ, T _C and T _switch refer to section 5.4 of 3gpp ts38.214, and a is the second parameter.

As an embodiment, the second parameter is an integer greater than 1 or a real number.

As one embodiment, the essence of the above method includes reserving a longer CSI calculation time for AI-based CSI reporting.

As one embodiment, the method has the advantages of better supporting AI models and calculation and improving the accuracy and effectiveness of CSI reporting.

As one embodiment, whether the first time interval depends on the generation mode of the target CSI is based on AI comprises whether the first time interval depends on a first capability parameter, and whether the generation mode of the target CSI depends on AI or not, and only when the generation mode of the target CSI is based on AI, the first time interval depends on the first capability parameter.

As one embodiment, whether the first time interval depends on the generation mode of the target CSI is based on AI or not includes that the first time interval depends on a first capability parameter when the generation mode of the target CSI is based on AI, the first time interval depends on a second capability parameter when the generation mode of the target CSI is not based on AI, and the first capability parameter and the second capability parameter are both capability parameters reported by the first node.

As one embodiment, whether the first time interval depends on the generation mode of the target CSI is based on AI or not includes that when the generation mode of the target CSI is based on AI, the first time interval depends on a first capability parameter and a second capability parameter, when the generation mode of the target CSI is not based on AI, the first time interval depends on only the second capability parameter, and the first capability parameter and the second capability parameter are both the capability parameters reported by the first node.

As an embodiment, the first capability parameter and the second capability parameter represent different capability parameters reported by the first node.

As an embodiment, the first capability parameter represents an AI-related capability parameter reported by the first node.

As an embodiment, the second capability parameter represents an AI-independent capability parameter reported by the first node.

As an embodiment, the second capability parameter includes beamReportTiming IE.

As an embodiment, the second capability parameter includes beamSwitchTiming IE.

As an embodiment, the second capability parameter includes codebookType IE.

As an embodiment, the second capability parameter includes beamReportTiming IE and beamSwitchTiming IE.

As an embodiment, the second capability parameter comprises at least one of codebookType IE, beamReportTiming IE, and beamSwitchTiming IE.

As one embodiment, the essence of the above method includes considering UE capability information when determining the first time interval, and considering different UE capability information for AI-based and non-AI-based CSI reporting.

As one embodiment, the benefits of the above-described method include enhanced reliability and robustness of the system.

As an embodiment, the first time interval dependent first capability parameter comprises that the calculation formula of the first time interval depends on the first capability parameter.

As an embodiment the first time interval being dependent on a first capability parameter comprises that the first time interval and the first capability parameter are linear.

As one embodiment, the first time interval dependent first capability parameter comprises a nonlinear relationship between the first time interval and the first capability parameter

As an embodiment the first time interval dependent first capability parameter comprises the first time interval dependent first parameter being dependent on the first capability parameter.

As one embodiment, the first time interval dependent first capability parameter comprises that a calculation formula of the first time interval depends on the first parameter, and the value of the first parameter depends on the first capability parameter.

As an embodiment the first time interval being dependent on a second capability parameter comprises that the calculation formula of the first time interval is dependent on the second capability parameter.

As an embodiment the first time interval being dependent on a second capability parameter comprises that the first time interval and the second capability parameter are linear.

As one embodiment, the first time interval dependent on the second capability parameter comprises the first time interval and the second capability parameter being non-linearly related

As an embodiment the first time interval dependent second capability parameter comprises the first time interval dependent first parameter being dependent on the second capability parameter.

As one embodiment, the first time interval dependence on the second capability parameter comprises that the calculation formula of the first time interval depends on a first parameter, and the value of the first parameter depends on the second capability parameter.

Example 2

Embodiment 2 illustrates a schematic diagram of a network architecture according to one embodiment of the application, as shown in fig. 2.

Fig. 2 illustrates a network architecture 200 of LTE (Long-Term Evolution), LTE-a (Long-Term Evolution Advanced, enhanced Long-Term Evolution) and future 5G systems. The network architecture 200 of LTE, LTE-a and future 5G systems is referred to as EPS (Evolved PACKET SYSTEM) 200. The 5G NR or LTE network architecture 200 may be referred to as a 5GS (5G System)/EPS (Evolved PACKET SYSTEM) 200 or some other suitable terminology. The 5GS/EPS200 may include one or more UEs (User Equipment) 201, one UE241 in sidelink (Sidelink) communication with the UE201, NG-RAN (next generation radio access network) 202,5GC (5G CoreNetwork)/EPC (Evolved Packet Core, evolved packet core) 210, hss (Home Subscriber Server )/UDM (Unified DATA MANAGEMENT) 220, and internet service 230. The 5GS/EPS200 may interconnect with other access networks, but these entities/interfaces are not shown for simplicity. As shown in fig. 2, the 5GS/EPS200 provides packet switched services, however, those skilled in the art will readily appreciate that the various concepts presented throughout this disclosure may be extended to networks providing circuit switched services. The NG-RAN202 includes an NR (New Radio), node B (gNB) 203 and other gnbs 204. The gNB203 provides user and control plane protocol termination towards the UE 201. The gNB203 may be connected to other gnbs 204 via an Xn interface (e.g., backhaul). The gNB203 may also be referred to as a base station, a base transceiver station, a radio base station, a radio transceiver, a transceiver function, a Basic Service Set (BSS), an Extended Service Set (ESS), TRP (transmit-receive point), or some other suitable terminology. The gNB203 provides the UE201 with an access point to the 5GC/EPC 210. Examples of UE201 include a cellular telephone, a smart phone, a Session Initiation Protocol (SIP) phone, a laptop, a Personal Digital Assistant (PDA), a satellite radio, a global positioning system, a multimedia device, a video device, a digital audio player (e.g., MP3 player), a camera, a game console, an drone, an aircraft, a narrowband physical network device, a machine-type communication device, a land vehicle, an automobile, a wearable device, or any other similar functional device. Those of skill in the art may also refer to the UE201 as a mobile station, a subscriber station, a mobile unit, a subscriber unit, a wireless unit, a remote unit, a mobile device, a wireless communication device, a remote device, a mobile subscriber station, an access terminal, a mobile terminal, a wireless terminal, a remote terminal, a handset, a user agent, a mobile client, a client, or some other suitable terminology. gNB203 is connected to 5GC/EPC210 through an S1/NG interface. the 5GC/EPC210 includes MME (Mobility MANAGEMENT ENTITY )/AMF (Authentication MANAGEMENT FIELD, authentication management domain)/SMF (Session Management Function ) 211, other MME/AMF/SMF214, S-GW (SERVICE GATEWAY, serving Gateway)/UPF (User Plane Function), 212, and P-GW (PACKET DATE Network Gateway)/UPF 213. The MME/AMF/SMF211 is a control node that handles signaling between the UE201 and the 5GC/EPC 210. The MME/AMF/SMF211 generally provides bearer and connection management. All user IP (Internet Protocal, internet protocol) packets are transported through the S-GW/UPF212, which S-GW/UPF212 itself is connected to the P-GW/UPF213. The P-GW provides UE IP address assignment as well as other functions. The P-GW/UPF213 is connected to the internet service 230. Internet services 230 include operator-corresponding internet protocol services, which may include, in particular, internet, intranet, IMS (IP Multimedia Subsystem ) and packet-switched (PACKET SWITCHING) services.

As an embodiment, the first node in the present application includes the UE201.

As an embodiment, the second node in the present application includes the gNB203.

As an embodiment, the UE 201 includes a mobile phone.

As one example, the UE 201 includes a vehicle including an automobile.

As one example, the gNB203 is a macro Cell (Marco Cell) base station.

As one example, the gNB203 is a Micro Cell (Micro Cell) base station.

As an example, the gNB203 is a Pico Cell (Pico Cell) base station.

As an example, the gNB203 is a home base station (Femtocell).

As an embodiment, the gNB203 is a base station device supporting a large delay difference.

As an embodiment, the gNB203 is a flying platform device.

As one embodiment, the gNB203 is a satellite device.

As an example, the gNB203 is a test device (e.g., a transceiver device that simulates a base station part function, a signaling tester).

As an embodiment, the radio link from the UE 201 to the gNB203 is an uplink, which is used to perform uplink transmission.

As an embodiment, the radio link from the gNB203 to the UE 201 is a downlink, which is used to perform downlink transmission.

As one embodiment, the wireless link between the UE 201 and the gNB203 comprises a cellular network link.

As an embodiment, the UE 201 and the gNB203 are connected through a Uu air interface.

As an embodiment, the sender of the at least one CSI reporting configuration includes the gNB203.

As an embodiment, the receiver of the at least one CSI reporting configuration comprises the UE 201.

As an embodiment, the sender of the target CSI reporting configuration includes the gNB203.

As an embodiment, the receiver of the target CSI reporting configuration includes the UE 201.

As an embodiment, the sender of the first DCI includes the gNB203.

As an embodiment, the receiver of the first DCI includes the UE201.

As an embodiment, the sender of the first set of resources includes the gNB203.

As an embodiment, the receiver of the first set of resources comprises the UE201.

As an embodiment, the sender of the at least one CSI comprises the UE201.

As an embodiment, the receiver of the at least one CSI includes the gNB203.

As an embodiment, the sender of the target CSI includes the UE201.

As an embodiment, the receiver of the target CSI includes the gNB203.

As an embodiment, the UE 201 supports a 6G system.

As one embodiment, the gNB203 supports a 6G system.

As an embodiment, the UE 201 supports at least a 5G system.

As an embodiment, the gNB203 supports at least 5G systems.

As an embodiment, the UE 201 supports AI.

As an embodiment, the gNB203 supports AI.

Example 3

Embodiment 3 illustrates a schematic diagram of an embodiment of a radio protocol architecture for a user plane and a control plane according to one embodiment of the present application, as shown in fig. 3.

Embodiment 3 shows a schematic diagram of an embodiment of a radio protocol architecture of a user plane and a control plane according to the application, as shown in fig. 3. Fig. 3 is a schematic diagram illustrating an embodiment of a radio protocol architecture for a user plane 350 and a control plane 300, fig. 3 shows the radio protocol architecture for the control plane 300 between a first communication node device (RSU in UE, gNB or V2X) and a second communication node device (RSU in gNB, UE or V2X) or between two UEs, layer 1, layer 2 and layer 3, in three layers. Layer 1 (L1 layer) is the lowest layer and implements various PHY (physical layer) signal processing functions. The L1 layer will be referred to herein as PHY301. Layer 2 (L2 layer) 305 is above PHY301 and is responsible for the link between the first communication node device and the second communication node device, or between two UEs. The L2 layer 305 includes a MAC (Medium Access Control ) sublayer 302, an RLC (Radio Link Control, radio link layer control protocol) sublayer 303, and a PDCP (PACKET DATA Convergence Protocol ) sublayer 304, which terminate at the second communication node device. The PDCP sublayer 304 provides multiplexing between different radio bearers and logical channels. The PDCP sublayer 304 also provides security by ciphering the data packets and handover support for the first communication node device between second communication node devices. The RLC sublayer 303 provides segmentation and reassembly of upper layer data packets, retransmission of lost data packets, and reordering of data packets to compensate for out of order reception due to HARQ. The MAC sublayer 302 provides multiplexing between logical and transport channels. The MAC sublayer 302 is also responsible for allocating the various radio resources (e.g., resource blocks) in one cell among the first communication node devices. The MAC sublayer 302 is also responsible for HARQ operations. The RRC (Radio Resource Control ) sublayer 306 in layer 3 (L3 layer) in the control plane 300 is responsible for obtaining radio resources (i.e., radio bearers) and configuring the lower layers using RRC signaling between the second communication node device and the first communication node device. The radio protocol architecture of the user plane 350 includes layer 1 (L1 layer) and layer 2 (L2 layer), the radio protocol architecture in the user plane 350 for the first communication node device and the second communication node device being substantially the same for the physical layer 351, the PDCP sublayer 354 in the L2 layer 355, the RLC sublayer 353 in the L2 layer 355 and the MAC sublayer 352 in the L2 layer 355 as the corresponding layers and sublayers in the control plane 300, but the PDCP sublayer 354 also providing header compression for upper layer data packets to reduce radio transmission overhead. Also included in the L2 layer 355 in the user plane 350 is an SDAP (SERVICE DATA Adaptation Protocol ) sublayer 356, the SDAP sublayer 356 being responsible for mapping between QoS flows and data radio bearers (DRBs, data Radio Bearer) to support diversity of traffic. Although not shown, the first communication node apparatus may have several upper layers above the L2 layer 355, including a network layer (e.g., IP layer) that terminates at the P-GW on the network side and an application layer that terminates at the other end of the connection (e.g., remote UE, server, etc.).

As an embodiment, the radio protocol architecture in fig. 3 is applicable to the first node in the present application.

As an embodiment, the radio protocol architecture in fig. 3 is applicable to the second node in the present application.

As an embodiment, the higher layer in the present application refers to a layer above the physical layer.

As an embodiment, the at least one CSI reporting configuration is generated in the RRC306.

As an embodiment, the target CSI reporting configuration is generated in the RRC306.

As an embodiment, the reference signals in the first set of resources are generated at the PHY301 or the PHY351.

As an embodiment, the at least one CSI is generated at the PHY301 or the PHY351.

As an embodiment, the at least one CSI is generated at the MAC302 or the MAC352.

As an embodiment, the target CSI is generated at the PHY301 or the PHY351.

As an embodiment, the target CSI is generated at the MAC302 or the MAC352.

Example 4

Embodiment 4 illustrates a schematic diagram of a first communication device and a second communication device according to an embodiment of the present application, as shown in fig. 4. Fig. 4 is a block diagram of a first communication device 410 and a second communication device 450 in communication with each other in an access network.

The first communication device 410 includes a controller/processor 475, a memory 476, a receive processor 470, a transmit processor 416, a multi-antenna receive processor 472, a multi-antenna transmit processor 471, a transmitter/receiver 418, and an antenna 420.

The second communication device 450 includes a controller/processor 459, a memory 460, a data source 467, a transmit processor 468, a receive processor 456, a multi-antenna transmit processor 457, a multi-antenna receive processor 458, a transmitter/receiver 454, and an antenna 452.

In the transmission from the first communication device 410 to the second communication device 450, upper layer data packets from the core network are provided to a controller/processor 475 at the first communication device 410. The controller/processor 475 implements the functionality of the L2 layer. In DL, the controller/processor 475 provides header compression, encryption, packet segmentation and reordering, multiplexing between logical and transport channels, and radio resource allocations to the second communication device 450 based on various priority metrics. The controller/processor 475 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the second communication device 450. The transmit processor 416 and the multi-antenna transmit processor 471 implement various signal processing functions for the L1 layer (i.e., physical layer). The transmit processor 416 performs coding and interleaving to facilitate Forward Error Correction (FEC) at the second communication device 450, as well as constellation mapping based on various modulation schemes, e.g., binary Phase Shift Keying (BPSK), quadrature Phase Shift Keying (QPSK), M-phase shift keying (M-PSK), M-quadrature amplitude modulation (M-QAM). The multi-antenna transmit processor 471 digitally space-precodes the coded and modulated symbols, including codebook-based precoding and non-codebook-based precoding, and beamforming processing, to generate one or more parallel streams. A transmit processor 416 then maps each parallel stream to a subcarrier, multiplexes the modulated symbols with a reference signal (e.g., pilot) in the time and/or frequency domain, and then uses an Inverse Fast Fourier Transform (IFFT) to produce a physical channel carrying the time-domain multicarrier symbol stream. The multi-antenna transmit processor 471 then performs transmit analog precoding/beamforming operations on the time domain multi-carrier symbol stream. Each transmitter 418 converts the baseband multicarrier symbol stream provided by the multiple antenna transmit processor 471 to a radio frequency stream and then provides it to a different antenna 420.

In a transmission from the first communication device 410 to the second communication device 450, each receiver 454 receives a signal at the second communication device 450 through its respective antenna 452. Each receiver 454 recovers information modulated onto a radio frequency carrier and converts the radio frequency stream into a baseband multicarrier symbol stream that is provided to a receive processor 456. The receive processor 456 and the multi-antenna receive processor 458 implement various signal processing functions for the L1 layer. A multi-antenna receive processor 458 performs receive analog precoding/beamforming operations on the baseband multi-carrier symbol stream from the receiver 454. The receive processor 456 converts the baseband multicarrier symbol stream after receiving the analog precoding/beamforming operation from the time domain to the frequency domain using a Fast Fourier Transform (FFT). In the frequency domain, the physical layer data signal and the reference signal are demultiplexed by the receive processor 456, wherein the reference signal is to be used for channel estimation, and the data signal is subjected to multi-antenna detection in the multi-antenna receive processor 458 to recover any parallel streams destined for the second communication device 450. The symbols on each parallel stream are demodulated and recovered in a receive processor 456 and soft decisions are generated. The receive processor 456 then decodes and deinterleaves the soft decisions to recover the upper layer data and control signals that were transmitted by the first communication device 410 on the physical channel. The upper layer data and control signals are then provided to the controller/processor 459. The controller/processor 459 implements the functions of the L2 layer. The controller/processor 459 may be associated with a memory 460 that stores program codes and data. Memory 460 may be referred to as a computer-readable medium. In DL (DownLink), a controller/processor 459 provides demultiplexing between transport and logical channels, packet reassembly, decryption, header decompression, control signal processing to recover upper layer data packets from the core network. The upper layer packets are then provided to all protocol layers above the L2 layer. Various control signals may also be provided to L3 for L3 processing. The controller/processor 459 is also responsible for error detection using Acknowledgement (ACK) and/or Negative Acknowledgement (NACK) protocols to support HARQ operations.

In the transmission from the second communication device 450 to the first communication device 410, a data source 467 is used at the second communication device 450 to provide upper layer data packets to a controller/processor 459. Data source 467 represents all protocol layers above the L2 layer. Similar to the transmit function at the first communication device 410 described in DL, the controller/processor 459 implements header compression, encryption, packet segmentation and reordering, and multiplexing between logical and transport channels based on radio resource allocations of the first communication device 410, implementing L2 layer functions for the user and control planes. The controller/processor 459 is also responsible for HARQ operations, retransmission of lost packets, and signaling to the first communication device 410. The transmit processor 468 performs modulation mapping, channel coding, and digital multi-antenna spatial precoding, including codebook-based precoding and non-codebook-based precoding, and beamforming, with the multi-antenna transmit processor 457 then modulating the resulting parallel streams into multi-carrier/single-carrier symbol streams, which are analog precoded/beamformed in the multi-antenna transmit processor 457 before being provided to the different antennas 452 via the transmitter 454. Each transmitter 454 first converts the baseband symbol stream provided by the multi-antenna transmit processor 457 into a radio frequency symbol stream and provides it to an antenna 452.

In the transmission from the second communication device 450 to the first communication device 410, the function at the first communication device 410 is similar to the receiving function at the second communication device 450 described in the transmission from the first communication device 410 to the second communication device 450. Each receiver 418 receives radio frequency signals through its corresponding antenna 420, converts the received radio frequency signals to baseband signals, and provides the baseband signals to a multi-antenna receive processor 472 and a receive processor 470. The receive processor 470 and the multi-antenna receive processor 472 collectively implement the functions of the L1 layer. The controller/processor 475 implements L2 layer functions. The controller/processor 475 may be associated with a memory 476 that stores program codes and data. Memory 476 may be referred to as a computer-readable medium. The controller/processor 475 provides demultiplexing between transport and logical channels, packet reassembly, deciphering, header decompression, control signal processing to recover upper layer data packets from the second communication device 450. Upper layer packets from the controller/processor 475 may be provided to the core network. The controller/processor 475 is also responsible for error detection using an ACK and/or NACK protocol to support HARQ operations.

The second communication device 450, as one embodiment, includes at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to be used with the at least one processor. The second communication device 450 at least receives at least one CSI reporting configuration; receiving first DCI on a first PDCCH, wherein the first DCI triggers the reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used for configuring the reporting of the at least one CSI; the method comprises the steps of determining whether target CSI is transmitted on a first PUSCH, transmitting the target CSI on the first PUSCH only when a first condition is met, wherein a target CSI reporting configuration is used for configuring reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, the target CSI is one of the at least one CSI, the target CSI reporting configuration indicates a first resource set which is used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set comprises one or more RS resources, the first condition comprises a first symbol which is not earlier than a first reference symbol, the first symbol is a first uplink symbol in the first PUSCH and used for bearing the at least one CSI, the first reference symbol is a next uplink symbol of a first time interval after the end of a last symbol of the first PDCCH, and the first time interval depends on whether the generation mode of the target CSI is based on AI.

The second communication device 450, as one embodiment, includes a memory storing a program of computer-readable instructions that, when executed by at least one processor, cause actions including receiving at least one CSI reporting configuration; receiving first DCI on a first PDCCH, wherein the first DCI triggers the reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used for configuring the reporting of the at least one CSI; the method comprises the steps of determining whether target CSI is transmitted on a first PUSCH, transmitting the target CSI on the first PUSCH only when a first condition is met, wherein a target CSI reporting configuration is used for configuring reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, the target CSI is one of the at least one CSI, the target CSI reporting configuration indicates a first resource set which is used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set comprises one or more RS resources, the first condition comprises a first symbol which is not earlier than a first reference symbol, the first symbol is a first uplink symbol in the first PUSCH and used for bearing the at least one CSI, the first reference symbol is a next uplink symbol of a first time interval after the end of a last symbol of the first PDCCH, and the first time interval depends on whether the generation mode of the target CSI is based on AI.

The first communication device 410, as one embodiment, includes at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to be used with the at least one processor. The first communication device 410 means at least sends at least one CSI reporting configuration; the method comprises the steps of sending first DCI on a first PDCCH, triggering the reporting of at least one CSI on a first PUSCH, wherein the at least one CSI reporting configuration is used for configuring the reporting of the at least one CSI, determining whether to send target CSI on the first PUSCH by a target receiver of the first DCI, sending the target CSI on the first PUSCH by the target receiver of the first DCI only when a first condition is met, configuring the reporting of the target CSI by the target receiver of the first DCI, wherein the target CSI reporting configuration is one of the at least one CSI reporting configuration, the target CSI is one of the at least one CSI, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set comprises one or more Reference Symbols (RS) and comprises first symbols which are not earlier than first reference symbols (reference symbols) and the first CSI is one of the first reference symbols (first) and the first time-dependent symbols (CQI) in the first PUSCH is one of the first symbols, and generating the first time interval (CQI) is based on whether the first time interval (CP) is finished or not at least one of the first time interval (first time-dependent symbol) which is finished after the first time interval).

The first communication device 410, as one embodiment, includes a memory storing a program of computer-readable instructions that, when executed by at least one processor, cause actions comprising transmitting at least one CSI reporting configuration; the method comprises the steps of sending first DCI on a first PDCCH, triggering the reporting of at least one CSI on a first PUSCH, wherein the at least one CSI reporting configuration is used for configuring the reporting of the at least one CSI, determining whether to send target CSI on the first PUSCH by a target receiver of the first DCI, sending the target CSI on the first PUSCH by the target receiver of the first DCI only when a first condition is met, configuring the reporting of the target CSI by the target receiver of the first DCI, wherein the target CSI reporting configuration is one of the at least one CSI reporting configuration, the target CSI is one of the at least one CSI, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set comprises one or more Reference Symbols (RS) and comprises first symbols which are not earlier than first reference symbols (reference symbols) and the first CSI is one of the first reference symbols (first) and the first time-dependent symbols (CQI) in the first PUSCH is one of the first symbols, and generating the first time interval (CQI) is based on whether the first time interval (CP) is finished or not at least one of the first time interval (first time-dependent symbol) which is finished after the first time interval).

As an embodiment, the first node in the present application includes the second communication device 450.

As an embodiment, the second node in the present application comprises the first communication device 410.

As an embodiment at least one of { the antenna 452, the receiver 454, the receive processor 456, the multi-antenna receive processor 458, the controller/processor 459, the memory 460, the data source 467} is used for receiving the at least one CSI reporting configuration }, at least one of { the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller/processor 475, the memory 476} is used for transmitting the at least one CSI reporting configuration.

As an embodiment, at least one of { the antenna 452, the receiver 454, the receive processor 456, the multi-antenna receive processor 458, the controller/processor 459, the memory 460, the data source 467} is used to receive the first DCI, { the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller/processor 475, the memory 476} is used to transmit the first DCI.

As an embodiment at least one of { the antenna 452, the receiver 454, the receive processor 456, the multi-antenna receive processor 458, the controller/processor 459, the memory 460, the data source 467} is used to receive RS resources in the first set of resources } { the antenna 420, the transmitter 418, the transmit processor 416, the multi-antenna transmit processor 471, the controller/processor 475, the memory 476} is used to transmit RS resources in the first set of resources.

As an embodiment, at least one of { the antenna 420, the receiver 418, the receive processor 470, the multi-antenna receive processor 472, the controller/processor 475, the memory 476} is used to receive the target CSI on the first PUSCH, { the antenna 452, the transmitter 454, the transmit processor 468, the multi-antenna transmit processor 457, the controller/processor 459, the memory 460, the data source 467} is used to transmit the target CSI on the first PUSCH.

Example 5

Embodiment 5 illustrates a flow chart of wireless transmission according to one embodiment of the application, as shown in fig. 5. In fig. 5, the second node U1 and the first node U2 are communication nodes transmitting over the air interface. In fig. 5, the steps in blocks F51 to F55 are optional, respectively.

For the second node U1, a second operation is deployed in step S511, at least one CSI reporting configuration is transmitted in step S512, a first DCI is transmitted on the first PDCCH in step S513, a signal is transmitted in the first set of resources in step S514, a target CSI is received on the first PUSCH only when the first condition is met in step S515, and the second operation is performed in step S516.

For the first node U2, a first operation is deployed in step S521, at least one CSI reporting configuration is received in step S522, a first DCI is received on the first PDCCH in step S523, a signal is received in the first set of resources in step S524, the first operation is performed in step S525, it is determined whether or not to transmit the target CSI on the first PUSCH in step S526, and the target CSI is transmitted on the first PUSCH only when the first condition is satisfied in step S527.

In embodiment 5, the first DCI triggers reporting of at least one CSI on a first PUSCH, the at least one CSI reporting configuration is used for configuring reporting of the at least one CSI, a target CSI reporting configuration is used for configuring reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, the target CSI is one of the at least one CSI, the target CSI reporting configuration indicates a first resource set used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set comprises one or more RS resources, the first condition comprises a first symbol not earlier than a first reference symbol, the first symbol is a first uplink symbol in the first PUSCH for bearing the at least one CSI, the first reference symbol is a next uplink symbol starting from a first time interval after the end of a last symbol of the first PDCCH, and the target CSI is generated based on whether the first time interval depends on the AI.

As an embodiment, the first node U2 is the first node in the present application.

As an embodiment, the second node U1 is the second node in the present application.

As an embodiment, the air interface between the second node U1 and the first node U2 comprises a radio interface between a base station device and a user equipment.

As an embodiment, the air interface between the second node U1 and the first node U2 comprises a wireless interface between a relay node device and a user device.

As an embodiment, the air interface between the second node U1 and the first node U2 comprises a wireless interface between user equipment and user equipment.

As an embodiment, the second node U1 is a serving cell maintenance base station of the first node U2.

As an embodiment the step in block F53 of fig. 5 is present, and the method in the first node for wireless communication comprises receiving a signal in the first set of resources.

As an embodiment the step in block F53 of fig. 5 is present and the method in the second node for wireless communication comprises transmitting a signal in the first set of resources.

Transmitting signals in the first set of resources means, as one embodiment, transmitting wireless signals in the first set of resources.

As an embodiment, transmitting signals in the first set of resources means transmitting reference signals in the first set of resources.

Receiving signals in the first set of resources means, as one embodiment, receiving wireless signals in the first set of resources.

As an embodiment, receiving a signal in the first set of resources means receiving a reference signal in the first set of resources.

As an embodiment, in fig. 5, when the generation manner of the target CSI is AI-based, the step in block F54 exists.

As an example, the steps in block F52 of fig. 5 exist.

As an embodiment, in fig. 5, when the generation manner of the target CSI is AI-based, the step in the block F54 exists, the step in the block F55 exists, and the first node and the second node adopt a two-sided (two-sided) AI model.

As an embodiment, in fig. 5, when the generation manner of the target CSI is AI-based, the step in the block F54 exists, the step in the block F55 does not exist, and the first node adopts a single-side (SINGLE SIDE) AI model.

As an embodiment, where the step in block F51 of fig. 5 exists, the method in the second node used for wireless communication described above includes deploying the second operation.

As an embodiment, the deployment of the second operation is earlier than the transmission of the at least one CSI reporting configuration.

As an embodiment, the deployment of the second operation is later than the transmission of the at least one CSI reporting configuration.

As an embodiment, where the step in block F55 of fig. 5 exists, the method in the second node for wireless communication described above comprises performing the second operation.

As an embodiment, in fig. 5, when the generation manner of the target CSI is AI-based, the steps in the block F54 exist, the steps in the block F55 exist, the first operation is used for CSI compression, the second operation is used for CSI recovery, and the first node and the second node adopt a two-sided (two-sided) AI model.

As an embodiment, in fig. 5, when the generation manner of the target CSI is AI-based, the step in the block F54 exists, the step in the block F55 does not exist, the first operation is used for beam prediction, and the first node adopts a single-side (SINGLE SIDE) AI model.

As an embodiment, the deployment of the first operation is earlier than the reception of the at least one CSI reporting configuration.

As an embodiment, the deployment of the first operation is later than the reception of the at least one CSI reporting configuration.

As one embodiment, the output of the first operation includes the target CSI and the input of the second operation includes the target CSI.

As an embodiment, the first node is a user (consumer).

As an embodiment, the first node is a user (consumer) of AI functionality (function).

As one embodiment, the first node is a user of AI inference (inference).

As one embodiment, the first node is an AI-trained user.

As an embodiment, the first node is a MnS (Management Service) user.

As one embodiment, the first node is a producer (producer) of AI inferences (inference).

As one embodiment, the first node is an AI-trained producer (producer).

Example 6

Embodiment 6 illustrates a schematic diagram of a calculation formula of a first time interval according to an embodiment of the present application, as shown in fig. 6.

In embodiment 6, the calculation formula of the first time interval depends on a first parameter, and the first parameter depends on whether the generation manner of the target CSI is AI-based.

As an embodiment, the first parameter is a real number or an integer.

As an embodiment, the first parameter is a real number or an integer greater than 0.

As an embodiment, the first parameter is a real number or an integer greater than 1.

As an embodiment, the first parameter is in milliseconds (ms).

As an embodiment, the first parameter is in seconds(s).

As an embodiment, the unit of the first parameter is a symbol.

As an embodiment, the unit of the first parameter is a slot (slot).

As an embodiment, the unit of the first parameter is a subframe (subframe).

As an embodiment, the value of the first parameter is configurable.

As an embodiment, the value of the first parameter is fixed.

As an embodiment, the first parameter comprises one or more candidate values.

As an embodiment, the value of the first parameter depends on UE capability information.

As an embodiment, the value of the first parameter depends on UE capability information, which comprises at least one of codebookType IE, beamReportTiming IE, and beamSwitchTiming IE.

As one embodiment, the essence of the above method comprises taking UE capability information into account when determining the first time interval.

As an embodiment, the first parameter is Z.

As an embodiment, the first parameter is Z (m).

As an embodiment, the first parameter is one of Z ₁、Z₂、Z₃.

As an embodiment, the first parameter is T _switch.

As an embodiment, the specific meaning of the Z, the Z (m), the Z ₁, the Z ₂, the Z ₃, the T _switch is referred to in section 5.4 of 3gpp ts 38.214.

As one embodiment, the benefits of the above approach include reduced implementation complexity along with existing standards and system designs.

As an embodiment, the first time interval and the first parameter are a functional relationship.

As an embodiment, the first time interval and the first parameter are a mapping relation.

As an embodiment, the first time interval and the first parameter are linear.

As an embodiment, the first time interval and the first parameter are non-linear.

As an embodiment, the first time interval is (Z) (2048+144) ·k2 ^-μ·T_C+T_switch, wherein the specific meaning of the Z, the k, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts38.214, and the first parameter is the Z.

As an embodiment, the first time interval is (Z) (2048+144) ·k2 ^-μ·T_C+T_switch, wherein the specific meaning of the Z, the k, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts38.214, and the first parameter is the T _switch.

As an embodiment, the first time interval is (Z) (2048+144) ·k ^-μ·T_C+T_switch +w, wherein the specific meaning of the Z, the k, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts38.214, wherein the W is an integer or real number greater than 0, and wherein the first parameter is the Z.

As an embodiment, the first time interval is (Z) (2048+144) ·k ^-μ·T_C+T_switch +w, wherein the specific meaning of Z, the k, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts38.214, wherein W is an integer or real number greater than 0, and wherein the first parameter is the T _switch.

As an embodiment, the first time interval is a [ (Z) (2048+144) ·k2 ^-μ·T_C+T_switch ], wherein the specific meaning of Z, the k, the μ, the T _C and the T _switch are referred to section 5.4 of 3gpp ts38.214, wherein a is an integer or real number greater than 1, and wherein the first parameter is the Z.

As an embodiment, the first time interval is a· [ (Z) (2048+144) ·k2 ^-μ·T_C+T_switch ], wherein the specific meaning of Z, the k, the μ, the T _C and the T _switch are referred to section 5.4 of 3gpp ts38.214, wherein a is an integer or real number greater than 1, and wherein the first parameter is the T _switch.

As an embodiment, the first time interval isWherein the specific meaning of Z (m), kappa, mu, T _C and T _switch is referred to in section 5.4 of 3GPP TS38.214, and the first parameter is Z (m).

As an embodiment, the first time interval isWherein the specific meaning of Z (m), kappa, mu, T _C and T _switch is referred to in section 5.4 of 3GPP TS38.214, and the first parameter is T _switch.

As an embodiment, the first time interval is (Z ₁)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₁, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the Z ₁.

As an embodiment, the first time interval is (Z ₁)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₁, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the T _switch.

As an embodiment, the first time interval is (Z ₂)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₂, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the Z ₂.

As an embodiment, the first time interval is (Z ₂)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₂, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the T _switch.

As an embodiment, the first time interval is (Z ₃)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₃, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the Z ₃.

As an embodiment, the first time interval is (Z ₃)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₃, the kappa, the mu, the T _C and the T _switch is referred to section 5.4 of 3GPP TS 38.214; the first parameter is the T _switch

As an embodiment, the first time interval is (Z ₂+Z′₂)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₂, the Z' ₂, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the Z ₂.

As an embodiment, the first time interval is (Z ₂+Z′₂)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₂, the Z' ₂, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the T _switch.

As an embodiment, the first time interval is (Z ₂+14(K-1)m)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₂, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the Z ₂.

As an embodiment, the first time interval is (Z ₂+14(K-1)m)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₂, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the T _switch.

As an embodiment, the first time interval is (Z ₂+14(K-1)m+Z′₂)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₂, the Z' ₂, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the Z ₂.

As an embodiment, the first time interval is (Z ₂+14(K-1)m+Z′₂)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₂, the Z' ₂, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the T _switch.

As an embodiment, the first time interval is (Z ₂+w)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₂, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the Z ₂.

As an embodiment, the first time interval is (Z ₂+w)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₂, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the T _switch.

As an embodiment, the first time interval is (Z ₂+w+Z′₂)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₂, the Z' ₂, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the Z ₂.

As an embodiment, the first time interval is (Z ₂+w+Z′₂)(2048+144)·κ2^-μ·T_C+T_switch, wherein the specific meaning of the Z ₂, the Z' ₂, the κ, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts 38.214; the first parameter is the T _switch.

As an embodiment, the determining of the first parameter depends on whether the generation manner of the target CSI is AI-based.

As one embodiment, the first parameter is Z when the generation of the target CSI is not AI-based, and the first parameter is not Z when the generation of the target CSI is AI-based.

As an embodiment, when the generation mode of the target CSI is not AI-based, the first parameter is T _switch, and when the generation mode of the target CSI is AI-based, the first parameter is not T _switch.

As an embodiment, when the generation mode of the target CSI is not AI-based, the first parameter is Z ₁, and when the generation mode of the target CSI is AI-based, the first parameter is not Z ₁.

As an embodiment, when the generation mode of the target CSI is not AI-based, the first parameter is Z ₂, and when the generation mode of the target CSI is AI-based, the first parameter is not Z ₂.

As one embodiment, the reporting amount of the target CSI includes RSRP, the first parameter is Z ₃ when the generation mode of the target CSI is not AI-based, and the first parameter is not Z ₃ when the generation mode of the target CSI is AI-based.

As an embodiment, when the generation manner of the target CSI is AI-based, the first parameter is not any one of Z ₁、Z₂、Z₃.

As an embodiment, when the generation manner of the target CSI is AI-based, the first parameter is not Z (m) defined in release 18 and previous versions of 3gpp ts 38.214.

As one example, the essence of the above method includes the handling of AI-based and non-AI-based schemes by case.

As one embodiment, the method has the advantages of being better suitable for various application scenes and good in flexibility.

As an embodiment, the value of the first parameter depends on whether the generation mode of the target CSI is AI-based.

As an embodiment, when the generation mode of the target CSI is based on AI, the candidate value of the first parameter belongs to a first candidate value range, the first candidate value range includes one or more candidate values, and when the generation mode of the target CSI is not based on AI, the candidate value of the first parameter belongs to a second candidate value range, the first candidate value range includes one or more candidate values.

As an embodiment, either one of the first candidate value range and the second candidate value range is a real number or an integer.

As an embodiment, either one of the first candidate value range and the second candidate value range is a real number or an integer greater than 0.

As an embodiment, either one of the first candidate value range and the second candidate value range is a real number or an integer greater than 1.

As one embodiment, the unit of any one of the first candidate value range and the second candidate value range is milliseconds (ms).

As one embodiment, the unit of any one of the first candidate value range and the second candidate value range is seconds(s).

As an embodiment, the unit of any one of the first candidate value range and the second candidate value range is a symbol.

As an embodiment, the unit of any one of the first candidate value range and the second candidate value range is a slot (slot).

As one embodiment, the unit of any one of the first candidate value range and the second candidate value range is a subframe (subframe).

As an embodiment, the second candidate value range comprises 10, 13, 25, 43.

As an embodiment, the second candidate value range comprises 22, 33, 44, 97, 388, 776.

As an embodiment, the second candidate value range comprises 40, 72, 141, 152, 608, 1216.

As an embodiment, the second candidate value range comprises 22、33、min(44,X₂+KB₁)、min(97,X₃+KB₂)、min(388,X₅+KB₃)、min(776,X₆+KB₄); wherein the specific meaning of X ₂、X₃、X₅、X₆、KB₁、KB₂、KB₃、KB₄ is referred to section 5.4 of 3gpp ts 38.214.

As an embodiment, the first candidate value range and the second candidate value range are different.

As an embodiment, the first candidate value range and the second candidate value range comprise the same number of candidate values.

As one embodiment, the first candidate value range is ranked according to the candidate value from large to small, the second candidate value range is ranked according to the candidate value from large to small, and any candidate value in the ranked first candidate value range is larger than a candidate value in the same position in the ranked second candidate value range.

As one embodiment, whether the first parameter depends on a first capability parameter depends on whether the generation mode of the target CSI is based on AI, and the first parameter depends on the first capability parameter only when the generation mode of the target CSI is based on AI.

As one embodiment, when the generation mode of the target CSI is based on AI, the first parameter depends on a first capability parameter, when the generation mode of the target CSI is not based on AI, the first parameter depends on a second capability parameter, and the first capability parameter and the second capability parameter are both capability parameters reported by the first node.

As one embodiment, when the generation mode of the target CSI is based on AI, the first parameter depends on a first capability parameter and a second capability parameter, when the generation mode of the target CSI is not based on AI, the first parameter depends on only the second capability parameter, and the first capability parameter and the second capability parameter are both the capability parameters reported by the first node.

As an embodiment, the second capability parameter includes beamReportTiming IE.

As an embodiment, the second capability parameter includes beamSwitchTiming IE.

As an embodiment, the second capability parameter includes codebookType IE.

Example 7

Embodiment 7 illustrates a schematic diagram of N information blocks and N time units according to one embodiment of the application, as shown in fig. 7. In fig. 7, information block #1, &..the information block #n is N information blocks, and time unit #1, &..the time unit #n is N time units.

In embodiment 7, when the generation manner of the target CSI is AI-based, the target CSI includes N information blocks, where the N information blocks include channel information of N time units, respectively, N is a positive integer greater than 1, and the first time interval depends on at least one of the N time units.

As an embodiment, the target CSI comprises N information blocks, the N information blocks respectively comprise CSI of N time units, N is a positive integer greater than 1, and the generation of any one of the N information blocks depends on the measurement based on the first set of resources.

As an embodiment, the N information blocks include prediction CSI of N time units, respectively.

As an embodiment, the N information blocks respectively include predicted beam information of N time units.

As an embodiment, the N information blocks include compressed CSI of N time units, respectively.

As an embodiment, any one of the N information blocks indicates at least one resource of the first set of resources.

As one embodiment, the essence of the method described above includes supporting joint reporting of CSI for multiple time units.

As one embodiment, the essence of the above method includes supporting AI-based CSI prediction or compression schemes.

As one embodiment, the method has the advantages of reducing system overhead and information feedback delay and enhancing the transmission efficiency of the system.

As one embodiment, the method has the advantages of improving the accuracy and instantaneity of the CSI reporting and improving the overall performance of the system.

As an embodiment, the target CSI includes a plurality of information blocks, and the number of information blocks included in the target CSI is not less than the N.

As one embodiment, the target CSI includes a plurality of information blocks, and the plurality of information blocks included in the target CSI includes the N information blocks.

As an embodiment, a time unit comprises one or more slots (slots).

As an embodiment, a time unit includes one or more subframes (subframes).

As an embodiment, a time unit comprises a plurality of consecutive symbols.

As an embodiment, the N time units are mutually orthogonal.

As an embodiment, the N time units are different from each other.

As an embodiment, there are two time units of the N time units overlapping.

As an embodiment, the N time units are consecutive.

As an embodiment, the N time units are equally spaced.

As one embodiment, the interval between any two adjacent time units in the N time units is P time units, and P is a positive integer.

As one example, the benefits of the above-described method include the conservation of existing system designs and standards.

As one embodiment, the benefits of the above-described approach include enhanced system flexibility and robustness.

In general, how the first node determines the N or at least one of the N time units is determined by the hardware device manufacturer, and some non-limiting embodiments are described below:

as an embodiment, the first node determines the N time units based on a change condition of a channel.

As an embodiment, the first node determines the N time units based on a time correlation of a channel.

As one embodiment, the first node determines the N time units based on a movement speed.

As one embodiment, the first node determines the N time units based on at least one of channel variation conditions, time correlation of channels, or moving speed.

As an embodiment, the first node determines the N time units to be time units in which the channel changes faster (e.g., changes by more than a threshold).

As an embodiment, the first node determines the N time units based on its movement speed.

As an embodiment, the first node determines the N time units to be time units with low time correlation (e.g., below a threshold).

As an embodiment, the first time interval being dependent on at least one of the N time units comprises the first time interval being dependent on a target time unit of the N time units.

As one embodiment, the target time unit is an earliest one of the N time units.

As an embodiment, the target time unit is the latest one of the N time units.

As one embodiment, the target time unit is a time unit specified by one of the N time units.

As an embodiment, the first time interval dependent on a target time unit of the N time units includes the first time interval dependent on a time interval between the target time unit and a last symbol of the first PDCCH.

As an embodiment, the first time interval dependent on the time interval between the target time unit and the last symbol of the first PDCCH comprises the first time interval dependent on the time interval between the starting symbol of the target time unit and the last symbol of the first PDCCH.

As an embodiment, the first time interval being dependent on at least one of the N time units comprises the first time interval being dependent on a target symbol of the N time units.

As an embodiment, the target symbol is an earliest symbol of the N time units.

As an embodiment, the target symbol is the latest one of the N time units.

As an embodiment, the target symbol is a symbol specified by one of the N time units.

As an embodiment, the first time interval dependent on the target symbol in the N time units comprises the first time interval dependent on a time interval between the target symbol and a last symbol of the first PDCCH.

As one example, benefits of the above-described approach include enhanced system flexibility.

As one example, the benefits of the above method include small changes to existing standards and system designs.

As an embodiment, the first time interval being dependent on at least one of the N time units comprises the first time interval being dependent on the N.

As one embodiment, the first time interval depends on the N, wherein the N belongs to one of V1 candidate value ranges, any one of the V1 candidate value ranges comprises one or more positive integers, V1 is a positive integer greater than 1, the V1 time intervals are respectively in one-to-one correspondence with the V1 candidate value ranges, and the first time interval is one time interval corresponding to the candidate value range to which the N belongs in the V1 time intervals.

As an embodiment the first time interval being dependent on the N comprises the first time interval being dependent on a first parameter, the first parameter being dependent on the N.

As an embodiment the first time interval being dependent on the N comprises that the first time interval and the first parameter are linear, the first parameter being dependent on the N.

As an embodiment the first time interval being dependent on the N comprises the first time interval and the first parameter being linear, the first parameter and the N being linear.

As one embodiment, the first parameter depends on the N, wherein the N belongs to one of V1 candidate value ranges, any one of the V1 candidate value ranges comprises one or more positive integers, V1 is a positive integer greater than 1, the candidate values of the V1 first parameter are respectively in one-to-one correspondence with the V1 candidate value ranges, and the first parameter is one candidate value of the V1 candidate value ranges corresponding to the candidate value range to which the N belongs.

As an embodiment, the essence of the method includes that the CSI calculates the amount of information carried by the time-dependent CSI report or the number of time units for which the CSI report is directed.

As one embodiment, the method has the advantages that the CSI calculation time is set more accurately, the system resources are effectively utilized, and the accuracy and the instantaneity of CSI reporting are improved.

As one example, benefits of the above-described approach include enhanced system flexibility and overall system performance.

As an embodiment, the first time interval being dependent on at least one of the N time units comprises the first time interval being dependent on a length of the N time units.

As an embodiment, the first time interval being dependent on the length of the N time units comprises that the first time interval and the length of the N time units are linear.

As one embodiment, the first time interval depends on the length of the N time units, wherein the length of the N time units belongs to one of V1 candidate value ranges, any candidate value range in the V1 candidate value ranges comprises one or more real numbers or integers, V1 is a positive integer larger than 1, the V1 time intervals are respectively in one-to-one correspondence with the V1 candidate value ranges, and the first time interval is one time interval of the V1 time intervals, which corresponds to the candidate value range to which the length of the N time units belongs.

As an embodiment the first time interval being dependent on the length of the N time units comprises the first time interval being dependent on a first parameter being dependent on the length of the N time units.

As an embodiment the first time interval being dependent on the length of the N time units comprises that the first time interval and a first parameter are linear, and that the first parameter and the length of the N time units are linear.

As one embodiment, the essence of the method includes that the CSI calculates the time unit length for which the time dependent CSI report is intended.

Example 8

Embodiment 8 a schematic diagram of a second symbol and a second reference symbol according to an embodiment of the application is shown in fig. 8. In fig. 8, RS #1, the term, the number RS # n of the base station, a.i. is one or more aperiodic RS resources in the first set of resources.

In embodiment 8, when the first set of resources consists of one or more aperiodic RS resources, the first condition further includes a second symbol not earlier than a second reference symbol, the second reference symbol being a next uplink symbol of a CP starting at a second time interval after an end of a last symbol of the first RS in the first set of resources.

As an embodiment, the first RS is the aperiodic RS in the first set of resources that is the latest in time.

As an embodiment, the first RS is the earliest in time aperiodic RS in the first set of resources.

As an embodiment, the first RS is the latest or earliest in time aperiodic RS in the first set of resources.

As an embodiment, the first set of resources is composed of one or more aperiodic RS resources, and the first RS is one RS in the first set of resources triggered by the first DCI.

As an embodiment, the first set of resources consists of one or more aperiodic RS resources, the first RS being the latest in time RS in the first set of resources triggered by the first DCI.

As an embodiment, the first set of resources consists of one or more aperiodic RS resources, the first RS being the latest or earliest in time RS in the first set of resources triggered by the first DCI.

As one example, the benefits of the above approach include the maintenance of existing system designs and standards to enhance system consistency.

Typically, the second symbol takes into account a timing advance (TIMING ADVANCE).

Typically, the second symbol and the second reference symbol both take into account a timing advance (TIMING ADVANCE).

As an embodiment, the second symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI as an embodiment, and the second symbol is a first uplink symbol in the first PUSCH for carrying the target CSI.

As an embodiment, the at least one CSI includes only one CSI, the at least one CSI is the target CSI, the at least one CSI reporting configuration is the target CSI reporting configuration, and the first symbol is the second symbol.

As an embodiment, the at least one CSI includes a plurality of CSI, and the at least one CSI reporting configuration includes a plurality of CSI reporting configurations, which are used to configure the plurality of CSI, respectively, the first symbol and the second symbol being the same or the first symbol and the second symbol being different.

As an embodiment, the second reference symbol is Z '_ref (n), and the specific meaning of Z' _ref (n) refers to section 5.4 of 3gpp ts 38.214.

Typically, the second reference symbol being the next uplink symbol (the next uplink symbol) of a second time interval after the end of the last symbol of the first RS in the first set of resources (starting) by the CP comprises the second reference symbol being the earliest uplink symbol later than the last symbol of the first RS in the first set of resources and meeting a reference condition, the reference condition comprising a time interval with the end of the last symbol of the first RS in the first set of resources being no less than the second time interval.

As an embodiment, the second time interval is a real number or an integer.

As an embodiment, the unit of the second time interval is milliseconds (ms).

As an embodiment, the unit of the second time interval is a symbol.

As an embodiment, the second time interval is T '_proc,CSI, and the specific meaning of T' _proc,CSI is referred to in section 5.4 of 3GPPTS38.214.

As an embodiment, the second time interval is (Z ') (2048+144) ·k2 ^-μ·T_C, wherein the specific meaning of the Z', the k, the μ, the T _C is referred to section 5.4 of 3gpp ts 38.214;

As an embodiment, the first condition when not met comprises the first condition not being met when the first symbol is earlier than the first reference symbol or the second symbol is earlier than the second reference symbol, and the first condition being met when the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol.

As one embodiment, the first set of resources consists of one or more aperiodic RS resources, and the when the first condition is not met includes when the first symbol is earlier than the first reference symbol or the second symbol is earlier than the second reference symbol, the first condition is not met, and when the first symbol is not earlier than the first reference symbol and the second symbol is not earlier than the second reference symbol.

Example 9

Example 9 illustrates a schematic diagram of a first operation according to one embodiment of the present application, as shown in fig. 9.

In embodiment 9, the generation of the target CSI is based on AI comprising performing a first operation, an input of the first operation being dependent on a measurement based on the first set of resources, the target CSI being dependent on an output of the first operation.

As an embodiment, the first operation is training-based or AI-based.

As an embodiment, the first operation is obtained by training.

As one embodiment, the first operation is based on a neural network (Neural Network).

As one embodiment, the first operation includes an AI entity (entity).

As an embodiment, the first operation includes a portion of an AI entity.

As an embodiment, the first operation includes a portion of an AI entity for inference.

As one embodiment, the first operation is performed by an AI entity (entity).

As one embodiment, the first operation is performed by an AI function (function).

As one embodiment, the AI function includes at least one of an AI inference function, an AI training (training) function, and an AI management (management) function.

As an embodiment, the training for obtaining the first operation is performed by the first node.

As an embodiment, the training to obtain the first operation is performed by an MDA Function (MANAGEMENT DATA ANALYTICS Function).

As one embodiment, the training to obtain the first operation is performed by MDAS (Management Data Analytics Service) manufacturers (producer).

As an embodiment, the training for obtaining the first operation is performed by NWDAF (Network DATA ANALYTICS Function).

As an embodiment, the training for obtaining said first operation is performed by the core network.

As one embodiment, the training to obtain the first operation is performed by an AI training producer (producer).

As one embodiment, the first operation includes inference (inference).

As one embodiment, the first operation is AI inference.

As one embodiment, the first operation includes AI inference for CSI.

As one embodiment, the first operation is AI inference for CSI.

As one embodiment, the first operation includes AI inference for at least one of beam prediction, CSI estimation, or CSI compression.

As one embodiment, the benefits of the above method include improved performance of CSI (including beam) measurements and reporting, including more accurate CSI, lower reference signal overhead and reporting overhead, thereby improving overall system performance.

As one embodiment, the method has the advantages of more accurate and complete CSI, lower reference signal overhead and improved CSI instantaneity.

As an embodiment, the first operation is deployment (deployment) required.

As an embodiment, the first operation is obtained by loading (load).

As an embodiment, the first operation is obtained from a serving cell load of the first node.

As an embodiment, the first operation is obtained from a maintenance base station loading of a serving cell of the first node.

As an embodiment, the first operation is obtained from a core network load.

As one embodiment, the first operation includes one or more of convolution (concentration), pooling (pooling), concatenation, and activation.

As an embodiment, the first operation includes at least one of a full connection layer, a pooling layer, at least one convolution layer, at least one coding layer.

As an embodiment, one coding layer includes at least one convolutional layer and one pooling layer.

As one embodiment, at least one convolution kernel is used to convolve the input to generate a corresponding feature map, at least one feature map of the convolution layer output is reshaped (reshape) into one vector input to the full-join layer, which converts the one vector into an output.

As an embodiment, part or all of the convolution kernel size, the number of convolution layers, the convolution step size, the pooling kernel step size, the pooling function, the activation function and the feature map number of the first operation are obtained through training.

As an embodiment, part or all of the convolution kernel, the pooling function, the activation function, the parameters of the pooling function and the parameters of the activation function of the first operation are obtained by training.

As an embodiment, the first operation comprises preprocessing.

As one embodiment, the preprocessing includes one or more of matrix decomposition, matrix transformation, and projection.

As one embodiment, the preprocessing includes one or more of quantization, spatial domain to angular domain transformation, angular domain to spatial domain transformation, frequency domain to time domain transformation, and time domain to frequency domain transformation.

As an embodiment, the preprocessing includes at least one of puncturing and/or padding (padding), DFT (Discrete Fourier Transform), mapping, labeling (label).

As an embodiment, the first operation includes post-processing.

As one embodiment, the post-processing includes at least one of DFT (Discrete Fourier Transform), quantization, truncation, and/or padding (padding).

As one embodiment, the post-processing includes one or more of an angular domain to spatial domain transform, a spatial domain to angular domain transform, a time domain to frequency domain transform, and a frequency domain to time domain transform.

As an embodiment, the measurement based on the first set of resources comprises pre-compressed channel information, and the output of the first operation comprises compressed channel information.

As an embodiment, the method has the advantages of being suitable for channel compression and saving feedback overhead.

As one embodiment, the measurement based on the first set of resources comprises measurement-derived channel information, and the output of the first operation comprises predicted channel information.

As one embodiment, the measurement based on the first set of resources includes measurement-derived channel information, and the output of the first operation includes spatial beam prediction.

As one embodiment, the benefits of the above approach include reduced RS resource overhead and reduced feedback delay.

As one embodiment, the measurement based on the first set of resources includes historical (historic) channel information, and the output of the first operation includes predicted channel information.

As one embodiment, the measurement based on the first set of resources includes historical (historic) channel information, and the output of the first operation includes Temporal (Temporal) beam prediction.

As one embodiment, the method has the advantages of reducing the feedback delay of the channel information and improving the real-time performance of the channel information acquisition.

As an embodiment, the measurement based on the first set of resources comprises current channel information, and the output of the first operation comprises channel information after a period of time.

As one embodiment, the method has the advantages of improving the accuracy and the real-time performance of the CSI and reducing the spending of the RS.

As an embodiment, the measurement based on the first set of resources comprises incomplete channel information, and the output of the first operation comprises complete channel information.

As one embodiment, the benefits of the above approach include reduced RS overhead and improved CSI accuracy and integrity.

As an embodiment, the measurement based on the first resource set includes channel information of P1 antenna ports, the output of the first operation includes channel information of P2 antenna ports, P1 and P2 are positive integers greater than 1, respectively, and P1 is smaller than P2.

As a sub-embodiment of the above embodiment, the P1 antenna ports are a proper subset of the P2 antenna ports.

As a sub-embodiment of the above embodiment, the P2 antenna ports belong to the second resource set.

As an embodiment, the input of the first operation further comprises the second set of resources.

As an embodiment, the output of the first operation includes one or more of beam indication, CRI (CSI-RS Resource Indicator, channel state information reference signal resource indicator), SS/PBCH block resource indicator (SS/PBCH Block Resource indicator, SSBRI), or RSRP (REFERENCE SIGNAL RECEIVED power ).

As one embodiment, the output of the first operation includes one or more of PMI, CRI, CQI, RI, LI, SSBRI, RSRP, SINR, capability index and TDCP.

As one embodiment, the output of the first operation includes one or more of a channel impulse response, a small scale characteristic, a channel matrix.

As one embodiment, the output of the first operation includes one or more of delay spread, doppler shift, average delay and average gain.

As an embodiment, the output of the first operation includes the target CSI.

As an embodiment, the input of the first operation depends on a measurement based on the first set of resources.

As one embodiment, the generation of the target CSI is AI-based, and the input of the first operation is dependent on a measurement based on the first set of resources.

As an embodiment, the input of the first operation relying on the measurement based on the first set of resources comprises that the measurement (channel measurement and/or interference measurement) based on the first set of resources is used to generate the input of the first operation.

As an embodiment, the target CSI depends on the output of the first operation.

As an embodiment, the target CSI being dependent on the output of the first operation comprises the target CSI comprising the output of the first operation.

As an embodiment, the target CSI dependent on the output of the first operation comprises the target CSI comprising a post-processed output of the first operation.

As an embodiment, the target CSI being dependent on the output of the first operation comprises the output of the first operation being used to generate the target CSI.

As an embodiment, the target CSI being dependent on the output of the first operation comprises that the output of the first operation is post-processed and used to generate the target CSI.

As an embodiment, the input of the first operation depends on a measurement based on the first set of resources, and the target CSI depends on the output of the first operation.

As one embodiment, the generating of the target CSI comprises performing a first operation whose input depends on a measurement based on the first set of resources, the target CSI depends on an output of the first operation.

As one embodiment, the method has the advantages of supporting an AI-based scheme and improving the accuracy and instantaneity of information reporting.

As an embodiment, the first operation is associated to the first type identification.

As an embodiment, the target CSI reporting configuration indicates a first type of identity, and the first operation is associated with the first type of identity.

As one embodiment, the AI model employed by the first operation is identified by the first type of identification.

As an embodiment, the AI entity or AI function to which the first operation belongs is identified by the first type identification.

As one embodiment, the AI entity or AI functionality that performs the first operation is identified by the first type of identification.

As one embodiment, the benefits of the above method include identifying one AI model/entity/function by the first type of identification, simplifying the design and unifying understanding of different AI entities/functions among multiple nodes.

As an embodiment, the first type of identification is used to identify or indicate a reference set of resources for which measurements are used to obtain the training data set of the first operation.

As an embodiment, the first type of identification is used to identify configuration information of a reference resource set for which measurements are used to obtain a training data set for the first operation.

As an embodiment, the training for obtaining the first operation is identified by the first type of identification.

As an embodiment, the data set for training of the first operation is identified by the first type identification.

As an embodiment, the target CSI reporting configuration indicates the first operation by indicating the first type of identification.

As one embodiment, the benefits of the above-described method include creating a consensus among different AI functions by identifying an AI training or AI training data set to identify an inference of the AI training or AI training data set generation, further simplifying the design.

Example 10

Embodiment 10 illustrates a schematic diagram of a first type of identification according to one embodiment of the application, as shown in fig. 10.

In embodiment 10, the generation of the target CSI is based on AI including associating the generation of the target CSI with a first type identifier.

As an embodiment, the first type of identification is a non-negative integer.

As an embodiment, the first type of identification is a string.

As an embodiment, the first type of identification is a model identification.

For one embodiment, the first type identification is used to identify an AI model, AI entity, or AI function.

As an embodiment, the first class identification is used by the first node to determine an AI model, AI entity, or AI function.

As an embodiment, the target CSI reporting configuration indicates the use of AI models, AI entities, or AI functionalities by indicating the first type of identification.

As one embodiment, the benefits of the above method include identifying one AI model, AI entity, or AI function by the first type of identification, simplifying the system design, and unifying understanding of different AI models, AI entities, or AI functions among multiple nodes.

As an embodiment, the first type of identification is used to identify or indicate a set of resources.

As an embodiment, the first type of identification is used to identify or indicate a set of resources, the measurement of which is used to obtain the training data set.

As an embodiment, the first type of identification is used to identify or indicate a training data set.

As one embodiment, the benefits of the above-described method include further simplifying the system design by identifying one AI training or AI training data set to identify an inference that the AI training or AI training data set is generated, establishing a consensus between the different AI functions.

As one embodiment, the generation mode of the target CSI is associated with a first type identifier, wherein the generation of the target CSI is associated with the first type identifier through the target CSI reporting configuration.

As one embodiment, the generation mode of the target CSI is associated with a first type identifier, wherein the target CSI reporting configuration indicates the first type identifier, and the target CSI reporting configuration indicates the generation of the target CSI.

As an embodiment, the generation of the target CSI in association with the first class identifier comprises performing a first operation, an input of which depends on a measurement based on the first set of resources, and an output of which depends on the first operation, the first operation being associated with the first class identifier.

As one embodiment, the generation of the target CSI is associated with the first type identifier by using an AI model identified by the first type identifier.

As one embodiment, the generation of the target CSI in association with the first type identifier includes generating the target CSI by an AI entity identified by the first type identifier.

As an embodiment, the generation of the target CSI is associated with a first type of identification, which is used to identify an AI entity or function, and the target CSI is generated by the AI entity.

As one embodiment, the generation mode of the target CSI is associated with a first type identifier, wherein the generation mode of the target CSI belongs to an AI function, and the first type identifier is used for identifying the AI function.

As one embodiment, the generation mode of the target CSI is not associated with the first type identifier, and the generation mode of the target CSI includes that a target receiver of the target CSI reporting configuration performs a first operation, an input of the first operation depends on a measurement based on the first resource set, the target CSI depends on an output of the first operation, and the first operation is not associated with the first type identifier.

As one embodiment, the generation of the target CSI is not associated with the first type identifier, including that the generation of the target CSI does not use the AI model identified by the first type identifier.

As one embodiment, the generation of the target CSI is not associated with the first type of identification includes that the AI entity identified by the first type of identification is not used to generate the target CSI.

As an embodiment, the generation of the target CSI is not associated with a first type of identification, which is not used to identify the AI entity or function, including the generation of the target CSI by the AI entity.

As one embodiment, the generation mode of the target CSI is not associated with a first type identifier, wherein the generation mode of the target CSI belongs to an AI function, and the first type identifier is not used for identifying the AI function.

As one embodiment, the method has the advantages of improving the flexibility of the system and adapting to the transmission and application of different scenes.

Example 11

Embodiment 11 illustrates a schematic diagram of a first time interval and a first type of identification relationship according to one embodiment of the present application, as shown in fig. 11.

In embodiment 11, when the generation manner of the target CSI is AI-based, the first time interval depends on the first type identifier to which the generation manner of the target CSI is associated.

As an embodiment, the first type identifier is a first type identifier associated with the generation mode of the target CSI, the first type identifier belongs to one of V identifier sets, any one identifier set in the V identifier sets comprises one or more first type identifiers, V is a positive integer greater than 1, and the first reference symbol depends on the identifier set to which the first type identifier belongs.

As one embodiment, the first type identifier is a first type identifier associated with the generation mode of the target CSI, the first type identifier belongs to one of V identifier sets, any one identifier set in the V identifier sets comprises one or more first type identifiers, V is a positive integer greater than 1, and the first time interval depends on the identifier set to which the first type identifier belongs.

As an embodiment, the calculation formula of the first time interval depends on the first type identifier associated with the generation mode of the target CSI.

As an embodiment, the first type identifier is a first type identifier associated with the generation mode of the target CSI, the first type identifier belongs to one of V identifier sets, any one identifier set in the V identifier sets comprises one or more first type identifiers, V is a positive integer greater than 1, and the calculation formula of the first time interval depends on the identifier set to which the first type identifier belongs.

As an embodiment, the first type identifier is a first type identifier associated with the generation mode of the target CSI, the first type identifier belongs to one of V identifier sets, any one identifier set of the V identifier sets comprises one or more first type identifiers, V is a positive integer greater than 1, the calculation formula of the first time interval comprises V formulas, the V formulas are in one-to-one correspondence with the V identifier sets, and the calculation formula of the first time interval is a calculation formula corresponding to the identifier set to which the first type identifier belongs.

As an embodiment, the first time interval depends on the first parameter, and the first parameter depends on the first type identifier associated with the generation mode of the target CSI.

As an embodiment, the first time interval and the first parameter are linearly related, and the first parameter depends on the first type identifier to which the generation mode of the target CSI is related.

As one embodiment, the first time interval and the first parameter are in a linear relation, the first parameter belongs to one of V candidate value ranges, any one candidate value range of the V candidate value ranges comprises one or more integers or a real number V is a positive integer larger than 1, the first type identifier belongs to one of V identifier sets, any one identifier set of the V identifier sets comprises one or more first type identifiers, V is a positive integer larger than 1, the V candidate value ranges and the V identifier sets are in one-to-one correspondence, and the first parameter is one candidate value of the candidate value ranges of the identifier sets corresponding to the first type identifier in the V candidate value ranges.

As an embodiment, the first time interval is (Z) (2048+144) ·k2 ^-μ·T_C+T_switch, wherein the specific meaning of the Z, the k, the μ, the T _C and the T _switch is referred to section 5.4 of 3gpp ts38.214, and the Z depends on the first type identifier to which the generation manner of the target CSI is related.

As an embodiment, the first time interval is (Z) (2048+144) ·k ^-μ·T_C+T_switch +w, wherein the specific meaning of the Z, the k, the μ, the T _C and the T _switch refers to section 5.4 of 3gpp ts38.214, and the W depends on the first type identifier to which the generation manner of the target CSI is related.

As one embodiment, the essence of the method includes setting different CSI calculation time for CSI reporting associated with different first class identifications.

As an embodiment, the essence of the above method includes considering the influence of unused AI models, AI entities, or AI functions when setting CSI calculation time.

For one embodiment, the benefits of the above method include improving the overall performance of the system.

Example 12

Embodiment 12 illustrates a schematic diagram of a second set of resources according to one embodiment of the application, as shown in fig. 12. In fig. 12, resource #1, resource #m, is at least one resource of the second set of resources.

In embodiment 12, the generation of the target CSI is based on AI comprising the target CSI indicating at least one resource in a second set of resources, the second set of resources comprising resources not belonging to the first set of resources.

As an embodiment, the second set of resources includes the first set of resources and resources other than the first set of resources.

As an embodiment, the first set of resources includes one or more RS resources, the second set of resources includes one or more RS resources, and the second set of resources includes the first set of resources and RS resources other than the first set of resources.

As an embodiment, the first set of resources includes a smaller number of resources than the second set of resources.

As an embodiment, the second set of resources includes resources not belonging to the first set of resources, and the resources in the second set of resources include at least one of antenna ports, TCI status, QCL information, frequency resources, time-frequency code resources, beams, RS resources, vectors, or matrices.

As an embodiment, the second set of resources comprises at least one training data set.

As an embodiment, the second set of resources includes one or more RS (Reference Signal) sets of resources, and one set of RS resources includes one or more RS resources.

As an embodiment, the second set of resources comprises at least one of at least one CSI-RS set of resources, at least one CSI-SSB set of resources, or at least one CSI-IM set of resources.

As an embodiment, the second set of resources includes one or more RS resources, and any RS resource in the second set of resources is a CSI-RS resource or a synchronization signal resource.

As an embodiment, the target CSI reporting configuration comprises at least one resource configuration, the at least one resource configuration indicating the first set of resources and the second set of resources.

As an embodiment, the target CSI reporting configuration indicates one resource configuration, the one resource configuration indicating the first set of resources and the second set of resources.

As an embodiment, the target CSI reporting configuration indicates two resource configurations, where the two resource configurations indicate the first resource set and the second resource set respectively.

As an embodiment, the target CSI reports configuration information indicating the second set of resources.

As an embodiment, the target CSI reporting configuration indicates an identity of the second set of resources.

As an embodiment, the target CSI reporting configuration indicates a first type identifier, and the second set of resources depends on the first type identifier.

As one embodiment, the second set of resources being dependent on the first type of identification includes the first type of identification being used to identify the second set of resources.

As one embodiment, the second set of resources being dependent on the first type of identification includes the first type of identification being used to identify a reference set of resources that includes the second set of resources.

As an embodiment, the second set of resources is dependent on the first type of identification comprises that the first type of identification is used to identify a reference set of resources, the reference set of resources comprising the second set of resources, and the target CSI reporting configuration is used to indicate the second set of resources from the reference set of resources.

As an embodiment, the information outside the target CSI reporting configuration indicates the second set of resources.

As an embodiment, the information indicating that the target CSI reporting configuration of the second set of resources is outside comprises higher layer parameters.

As an embodiment, the information indicating that the target CSI reporting configuration of the second set of resources is outside comprises RRC parameters.

As an embodiment, the information indicating that the target CSI reporting configuration of the second set of resources is outside comprises a MAC CE.

As an embodiment, the information indicating that the target CSI reporting configuration of the second resource set is outside includes DCI (downlink control information ).

As an embodiment, the target CSI is generated based on AI, and the first node is not required to measure the second set of resources.

As an embodiment, the generation manner of the target CSI is based on AI, the first set of resources is used for measurement, and the second set of resources is used for prediction.

As an embodiment, the generation manner of the target CSI is based on AI, and only the first resource set of the first resource set and the second resource set is used for measurement.

As an embodiment, the only first set of resources of the first set of resources and the second set of resources being used for measurement comprises the only first set of resources of the first set of resources and the second set of resources being used for measurement by the first node.

As an embodiment, only the first set of resources of the first set of resources and the second set of resources is used for measurement comprises the first set of resources being used for measurement by the first node, which is not required to measure some or all of the resources of the second set of resources.

As an embodiment, the first node not being required to measure the second set of resources comprises the first node not measuring some or all of the resources in the second set of resources.

As an embodiment, the first node not being required to measure the second set of resources comprises whether the first node measures some or all of the resources in the second set of resources is implementation dependent or self-determining by the first node.

Example 13

Example 13 illustrates a schematic diagram in which the first condition is not satisfied according to an embodiment of the present application, as shown in fig. 13.

In embodiment 13, the first DCI is ignored when the first condition is not met, wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

As an embodiment, the when the first condition is not met comprises when the first symbol is earlier than the first reference symbol.

As an embodiment, the when the first condition is not met comprises when the second symbol is earlier than the second reference symbol.

As an embodiment, the when the first condition is not met comprises when the first symbol is earlier than the first reference symbol or the second symbol is earlier than the second reference symbol.

As an embodiment, no HARQ-ACK or transport block is multiplexed on the first PUSCH, and the first DCI is ignored when the first condition is not satisfied.

As an embodiment, the first set of resources consists of one or more aperiodic RS resources, no HARQ-ACK or transport block is multiplexed on the first PUSCH, and the first node ignores the first DCI when the first condition is not satisfied.

As an embodiment, the first set of resources consists of one or more periodic or semi-persistent RS resources, the first node ignores the first DCI when the first condition is not met, wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

As an embodiment, the first set of resources consists of one or more periodic or semi-persistent RS resources, the first processor ignores the first DCI when the first condition is not met, wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

As an embodiment, there is a HARQ-ACK or transport block multiplexed on the first PUSCH, and the target CSI is transmitted on the first PUSCH and not updated when the first condition is not satisfied.

As an embodiment, the first set of resources consists of one or more aperiodic RS resources, HARQ-ACKs or transport blocks are multiplexed on the first PUSCH, and the target CSI is transmitted on the first PUSCH and not updated when the first condition is not satisfied.

As an embodiment, when the first condition is not satisfied, the first node ignores the first DCI or transmits the target CSI on the first PUSCH and the target CSI is not updated.

As an embodiment, the first processor ignores the first DCI or transmits the target CSI on the first PUSCH and the target CSI is not updated when the first condition is not satisfied.

As one embodiment, the ignoring the first DCI includes refraining from transmitting a signal on the first PUSCH.

As one embodiment, the ignoring the first DCI includes relinquishing transmission of the target CSI on the first PUSCH.

As one embodiment, the ignoring the first DCI includes relinquishing transmission of the at least one CSI on the first PUSCH.

As one embodiment, the transmitting the target CSI on the first PUSCH and the target CSI being not updated includes the first node not expecting to transmit the target CSI on the first PUSCH and the target CSI being valid.

As one embodiment, the transmitting the target CSI on the first PUSCH and the target CSI being not updated includes the first node not expecting to transmit the target CSI on the first PUSCH and the target CSI being updated.

As one embodiment, the target CSI being not updated includes the first node not being expected to update the target CSI.

As an embodiment, the target CSI being not updated includes whether the target CSI is actually updated is self-determined or implementation dependent by the first node.

As one embodiment, the target CSI being not updated includes the target CSI not being valid.

As an embodiment, the target CSI being not updated includes the target CSI being the same as CSI configured for the target CSI reporting earlier than the last one on the first PUSCH.

As one embodiment, the target CSI not being updated includes the target CSI being independent of measurements of a most recent RS occasion of CSI reference resources in the first set of resources that are no later than the target CSI.

As one embodiment, the target CSI being not updated includes the target CSI being independent of measurements based on a most recent RS occasion of CSI reference resources in the first set of resources that are no later than the target CSI.

As one embodiment, the target CSI being not updated includes that the target CSI is not CSI that is updated based on at least a measurement of a most recent RS occasion of CSI reference resources in the first set of resources that is no later than the target CSI.

The first set of resources is comprised of one or more aperiodic RS resources, and the target CSI is not updated including that the target CSI is independent of measurements based on aperiodic RS resources in the first set of resources triggered by the first DCI.

The target CSI is not updated including, as one embodiment, the target CSI is not generated based on measurements of aperiodic RS resources in the first set of resources triggered by the first DCI.

The target CSI is not updated including, as one embodiment, the target CSI is not updated based on measurements of aperiodic RS resources in the first set of resources triggered by the first DCI.

Example 14

Example 14 illustrates a schematic diagram of a second operation according to one embodiment of the application, as shown in fig. 14.

In embodiment 14, the output of the first operation includes a first CSI, the target CSI carrying the first CSI, the first CSI being used as an input to a second operation by a target receiver of the target CSI to generate a second CSI.

As an embodiment, the first operation is used for CSI compression, the second operation is used for CSI recovery, and the first node and the second node employ a two-sided AI model.

As an embodiment, the target CSI includes the first CSI.

As an embodiment, the first CSI is post-processed and then used to generate the target CSI.

As an embodiment, the first CSI includes N sub-CSI, and the N information blocks respectively carry the N sub-CSI.

As an embodiment, the first CSI comprises an output of the first operation.

As an embodiment, the second CSI comprises a restoration of at least part of the input of the first operation.

As an embodiment, the second CSI includes one or more of PMI (Precoding Matrix Indicator, precoding indication), CRI (CSI-RS Resource Indicator, channel state information reference signal resource Indicator), SS/PBCH block resource Indicator (SS/PBCH Block Resource Indicator, SSBRI), beam indication, resource indication, CQI (Channel quality Indicator, channel quality indication), RI (Rank Indicator, rank indication), layer indication (Layer Indicator, LI), RSRP (REFERENCE SIGNAL RECEIVED power ), SINR (signal-to-noise AND INTERFERENCE ratio), capability Index (Capability Index), or TDCP (Time Domain Channel Properties, time domain channel characteristics).

As an embodiment, the second CSI includes one or more of a channel matrix, eigenvectors, eigenvalues, or precoding matrix.

As an embodiment, the second operation is an inverse operation of the first operation.

As an embodiment, the second operation is training based.

As one embodiment, the training to obtain the second operation is performed by the target receiver of the target CSI.

As an embodiment, the training to obtain the second operation is performed by an MDA function.

As one embodiment, training to obtain the second operation is performed by an MDAS producer (producer).

As an embodiment, the training for obtaining said second operation is performed by NWDAF.

As an embodiment, the training for obtaining said second operation is performed by the core network.

As one embodiment, the training to obtain the second operation is performed by an AI (ARTIFICIAL INTELLIGENCE ) training producer (producer).

As an embodiment, the first operation and the second operation are obtained by different training.

As an embodiment, the first operation and the second operation are obtained by training independently of each other.

As one embodiment, the method has the advantages of saving air interface overhead, having better flexibility, being suitable for different terminals and having better forward compatibility.

As an embodiment, the first operation and the second operation are obtained by joint training.

As one example, benefits of the above method include optimizing system performance.

As an embodiment, the training of the second operation depends on the first operation.

As one embodiment, a producer (producer) of the second operation trains the second operation according to the output of the first operation.

Example 15

Embodiment 15 illustrates a schematic diagram of a first operation according to another embodiment of the present application, as shown in fig. 15. In embodiment 15, the first operation includes K1 sub-operations, where K1 is a positive integer not greater than 1.

In example 15, the K1 sub-operations are denoted as sub-operation #0,..sub-operation # (K1-1), respectively.

As an embodiment, each of the K1 sub-operations is training based.

As one embodiment, at least one of the K1 sub-operations is training based.

As one embodiment, each of the K1 sub-operations is based on the same performer of the training on which the training is based.

As one embodiment, the executors of the training on which two of the K1 sub-operations are based are different.

As an embodiment, at least one of the K1 sub-operations is to be deployed.

As an embodiment, at least one of the K1 sub-operations is to be loaded.

As an embodiment, all sub-operations that need to be loaded in the K1 sub-operations are loaded from the same producer.

As an embodiment, two of the K1 sub-operations need to be loaded are loaded from different producers.

As one embodiment, at least one of the K1 sub-operations is not training based.

As an embodiment, at least one of the K1 sub-operations is based on a codebook for precoding defined by 3gpp r18 or a version prior to 3gpp r 18.

As one embodiment, one or more of the K1 sub-operations are AI-based.

As one embodiment, one or more of the K1 sub-operations includes inference (inference).

As one embodiment, one or more of the K1 sub-operations includes AI inference (inference).

As one embodiment, one or more of the K1 sub-operations include AI inference for CSI.

As one embodiment, one or more of the K1 sub-operations includes preprocessing.

As one embodiment, one or more of the K1 sub-operations include post-processing.

As an example, there are two sub-operations among the K1 sub-operations that are serial, such as all sub-operations in fig. 15 (a), sub-operations #2 through # sub-operations (K1-1) in 15 (b), and sub-operations #0 through # sub-operations (K1-4) in 15 (c).

As an embodiment, two sub-operations are serial, meaning that the output of one of the two sub-operations is used for the input of the other of the two sub-operations.

As an example, there are two sub-operations in the K1 sub-operations in parallel, such as sub-operation #0 and sub-operation #1 in FIG. 15 (b), sub-operation # (K1-3) and sub-operation # (K1-2) in FIG. 15 (c).

As an embodiment, two sub-operations are parallel, meaning that the outputs of the two sub-operations are commonly used for the input of another sub-operation.

As one embodiment, the K1 sub-operations include one or more of convolution (con-version), pooling (pooling), concatenation, or activation.

As an embodiment, there is one sub-operation of the K1 sub-operations including a full connection layer.

As an embodiment, there is one sub-operation of the K1 sub-operations including a pooling layer.

As an embodiment, one sub-operation of the K1 sub-operations includes at least one convolution layer.

As an embodiment, there is one sub-operation of the K1 sub-operations including at least one coding layer.

As an embodiment, two sub-operations among the K1 sub-operations include a full connection layer and at least one coding layer, respectively.

Example 16

Embodiment 16 illustrates a first operational schematic of a deployment according to an embodiment of the present application, as shown in fig. 16.

In embodiment 16, the first processor deploys the first operation.

As one embodiment, the deploying (deployment) includes obtaining the first operation.

As one embodiment, the deploying includes obtaining an AI entity.

As one embodiment, the deploying includes obtaining an AI entity that performs the first operation.

As one embodiment, the deploying includes obtaining an AI entity that includes performing AI functionality of the first operation.

As one embodiment, the deploying includes loading (load) the first operation.

As one embodiment, the deploying includes making a request to load the first operation.

As an example, the request in fig. 16 is a request by the first node to load the first operation.

As an example, the response in fig. 16 is a response to a request by the first node to load the first operation.

As an embodiment, the first operation is obtained from loading at a serving cell of the first node.

As an embodiment, the first operation is obtained from loading at a maintenance base station of a serving cell of the first node.

As an embodiment, the first operation is obtained from loading at the core network.

As one embodiment, the first operation is obtained from a first producer (producer) at a load.

As an example, the first producer provides the first operation to the first node via the response in fig. 16.

As one embodiment, the deployment is done by AI functionality.

As one embodiment, the deployment is accomplished by an AI deployment function (deployment function).

As one embodiment, the deployment is accomplished by AI inference (inference) functionality.

As one embodiment, the deployment is done by an AI entity (entity).

As one embodiment, the deploying includes obtaining the first operation from a first producer (producer).

As one embodiment, the deploying includes making a request to the first producer to load the first operation.

As one embodiment, the deploying includes loading (load) the first operation from a first producer.

As one embodiment, the first producer generates and provides at least one of an AL entity and an AL function.

As an embodiment, the first producer is a producer of the first operation.

As an embodiment, the sender of the target CSI reporting configuration is the first producer.

As an embodiment, the sender of the target CSI reporting configuration is different from the first producer.

As an embodiment, the training for obtaining the first operation is performed by the first producer.

As one embodiment, the executor of the training for obtaining the first operation is different from the first producer.

Example 17

Embodiment 17 illustrates a schematic diagram of an artificial intelligence or machine learning based processing system according to one embodiment of the application, as shown in fig. 17. Fig. 17 (a) includes a third processor, a fourth processor and a fifth processor, and fig. 17 (b) includes the third processor, the fourth processor, the fifth processor and the sixth processor.

In the embodiment 17 (a), the third processor sends a first data set to the fourth processor and sends a second data set to the fifth processor, the fourth processor generates a target first type parameter set according to the first data set, the fourth processor sends the generated target first type parameter set to the fifth processor, and the fifth processor processes the second data set by using the target first type parameter set to obtain a first type output. In fig. 17 (a), the first type of feedback is optional.

In the embodiment 17 (b), the third processor sends a first data set to the fourth processor and sends a second data set to the fifth processor, the fourth processor generates a target first type parameter set according to the first data set, the fourth processor sends the generated target first type parameter set to the fifth processor, the fifth processor processes the second data set by using the target first type parameter set to obtain a first type output, and the fifth processor sends the first type output to the sixth processor. In fig. 17 (b), the first type of feedback and the second type of feedback are optional.

As an example, in fig. 17 (a), the fifth processor sends the first type of output to the second node in the present application.

As an example, fig. 17 (a) uses a single-sided (SINGLE SIDE) AI model for beam prediction or channel information prediction, and the fifth processor performs the first operation for beam prediction or channel information prediction.

As an example, fig. 17 (b) uses a two-sided AI model for CSI compression, the first operation is for compressing CSI, the second operation is for recovering CSI, the fifth processor performs the first operation, and the sixth processor includes the second operation.

As one embodiment, the AI includes ML (Machine Learning) inference (inference).

As one embodiment, the fifth processor performs the first operation.

As an embodiment, the sixth processor comprises the second operation.

As an embodiment, the fifth processor sends a first type of feedback to the fourth processor, the first type of feedback being used to trigger a recalculation or update of the target first type of parameter set.

As an embodiment, the sixth processor sends a second type of feedback to the third processor, the second type of feedback being used to generate the first data set or the second data set, or the second type of feedback being used to trigger the sending of the first data set or the sending of the second data set.

As an embodiment, the third processor generates the first data set and the second data set from measurements of a first type of wireless signals, the first type of wireless signals comprising downlink RSs.

As an embodiment, the fifth processor belongs to the first node, and the sixth processor belongs to the second node.

As an embodiment, the target CSI belongs to the first class of output.

As an embodiment, the second data set comprises the input of the first operation.

As an embodiment, the second data set includes information obtained based on the target CSI reporting configuration and the M1 configurations.

As one embodiment, the first data set includes training data (TRAINING DATA).

As an embodiment, the fourth processor belongs to the producer of the first operation.

As one embodiment, the fourth processor includes an AI training producer (producer).

As an embodiment, the fourth processor includes an AI training function (function).

As an embodiment, the fourth processor is configured to perform Model Training (Model Training), and the trained Model is described by the target first class parameter set.

As an embodiment, the fourth processor belongs to the first node.

The above embodiments avoid the transfer of the first data set to the second node.

As an embodiment, the fourth processor belongs to the second node.

The above embodiments support joint training to optimize system performance.

As an embodiment, the fourth processor belongs to a core network.

The embodiment supports full-network combined training, and further optimizes the system performance.

As one embodiment, the second data set includes inferred data (INFERENCE DATA).

As one embodiment, the fifth processor includes an AI inference producer (producer).

As an embodiment, the fifth processor includes an AI inference function (function).

As an embodiment, the fifth processor belongs to the first node.

As an embodiment, the fifth processor constructs a model from the target first class parameter set and then inputs the second data set into the constructed model to obtain the first class output.

As an embodiment, the first operation is described by the target first class parameter set.

As an embodiment, the target first class parameter set is used to construct the first operation.

As an embodiment, the fifth processor comprises the second operation.

As one embodiment, the fifth processor generates a recovery data set from the first type output, an error of the recovery data set from the second data set being used to generate the first type feedback.

As a sub-embodiment of the above embodiment, the generation of the recovery dataset employs a similar operation to the second operation.

As one embodiment, the first type of feedback is used to reflect the performance of the trained model, and the fourth process may recalculate the set of target first type parameters when the performance of the trained model fails to meet a requirement.

As an example, the performance of the trained model is considered unsatisfactory when the error is excessive or not updated for too long.

As one embodiment, the set of target first class parameters includes one or more of a convolution kernel size, a number of convolution layers, a convolution step size, a pooling kernel step size, a pooling function, an activation function, or a feature map number.

As an embodiment, the set of target first class parameters includes one or more of a convolution kernel, a pooling function, an activation function, parameters of a pooling function, or parameters of an activation function.

Example 18

Embodiment 18 illustrates a schematic diagram based on artificial intelligence or machine learning according to one embodiment of the application, as shown in fig. 18. Fig. 18 includes a third operation, a fourth operation, a fifth operation, a sixth operation, and a seventh operation, and the lines with arrows indicate the order of the flow.

In embodiment 18, the third operation and the fourth operation belong to a first stage, the fifth operation belongs to a second stage, the sixth operation belongs to a third stage, and the seventh operation belongs to a fourth stage.

As one embodiment, the third operation includes AI training, the fourth operation includes AI testing (testing), the fifth operation includes AI simulation (modeling), the sixth operation includes AI entity loading (loading), and the seventh operation includes AI inference (inference).

As one embodiment, the first stage includes a training stage (TRAINING PHASE), the second stage includes an emulation stage (simulation phase), the third stage includes a deployment stage (deployment phase), and the fourth stage includes an inference stage (simulation phase).

As one embodiment, the first stage includes at least one of model training (model training) and testing (testing).

As one embodiment, the AI model training includes an initial training (INITIAL TRAINING) and a retraining (re-training) of one or a group of AI entities.

As one embodiment, the AI model training includes AI entity verification (validation).

As one embodiment, the AI entity authentication is used to estimate (evaluate) the performance of the AI entity.

As one example, if the results of the AI entity verification are not expected, the AI model will be retrained.

As one embodiment, the AI test includes testing the validated AI entity to estimate performance of the trained AI model.

As one example, if the results of the AI test reach the expectations, the AI entity proceeds to the next stage, otherwise the AI model will be retrained.

As one embodiment, the second stage includes AI simulation that makes an inference of the AI entity in a simulation environment (inference).

As one embodiment, the AI simulation is to estimate the performance of AI entity inference in a simulation environment prior to using the AI entity.

As an embodiment, the second stage is optional.

As one embodiment, the third stage includes AI entity loading (loading) to obtain trained AI entities to obtain desired AI inference functions.

As an embodiment, the third stage is optional.

As an example, the third phase is no longer needed when the training function and the inference function are co-located (co-located).

As an embodiment, the fourth stage includes AI inference.

As one embodiment, the seventh operation includes the first operation.

As an embodiment, the seventh operation includes the second operation.

Example 19

Embodiment 19 illustrates a block diagram of a processing apparatus for use in a first node according to one embodiment of the application, as shown in fig. 19. In fig. 19, a processing device 1900 in a first node includes a first receiver 1901 and a first processor 1902.

The first receiver 1901 receives at least one CSI reporting configuration, receives a first DCI on a first PDCCH, the first DCI triggering reporting of at least one CSI on a first PUSCH, the at least one CSI reporting configuration being used to configure reporting of the at least one CSI;

a first processor 1902 that determines whether to transmit a target CSI on the first PUSCH, the target CSI being transmitted on the first PUSCH only if a first condition is met;

In embodiment 19, a target CSI reporting configuration is used to configure reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, the target CSI is one of the at least one CSI, the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of channel measurement or interference resource measurement of the target CSI, the first resource set includes one or more RS resources, the first condition includes a first symbol not earlier than a first reference symbol, the first symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI, the first reference symbol is a next uplink symbol of a first time interval after an end of a last symbol of the first PDCCH, and the first time interval depends on whether a generation manner of the target CSI is based on AI.

As an embodiment, the calculation formula of the first time interval depends on a first parameter, and the first parameter depends on whether the generation manner of the target CSI is AI-based or not.

As one embodiment, when the generation mode of the target CSI is AI-based, the target CSI includes N information blocks, where the N information blocks include channel information of N time units, N is a positive integer greater than 1, respectively, and the first time interval depends on at least one of the N time units.

As an embodiment, when the first set of resources consists of one or more aperiodic RS resources, the first condition further includes a second symbol not earlier than a second reference symbol, the second reference symbol being a next uplink symbol of a CP starting at a second time interval after an end of a last symbol of the first RS in the first set of resources.

As an embodiment, the generation of the target CSI is based on AI comprising performing a first operation, the input of which depends on the measurement based on the first set of resources, the target CSI depends on the output of the first operation.

As one embodiment, the generation of the target CSI is based on AI including associating the generation of the target CSI with a first type of identification.

As an embodiment, when the generation manner of the target CSI is AI-based, the first time interval depends on the first type identifier to which the generation manner of the target CSI is associated.

As an embodiment, the generation mode of the target CSI is based on AI, and the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources not belonging to the first resource set.

As an embodiment, any one of the N information blocks indicates at least one resource of a second set of resources, the second set of resources including resources not belonging to the first set of resources.

As an embodiment, the first processor 1902 ignores the first DCI when the first condition is not met, wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

As an embodiment, the output of the first operation includes a first CSI, the target CSI carrying the first CSI, the first CSI being used as an input to a second operation by a target receiver of the target CSI to generate a second CSI.

For one embodiment, the first processor 1902 deploys the first operation.

As an embodiment, the first operation is training-based or AI-based.

As an embodiment, the second operation is training-based or AI-based.

As an embodiment, the first node is a user equipment.

As an embodiment, the first node is a relay node device.

As an example, the first receiver 1901 includes at least one of { antenna 452, receiver 454, receive processor 456, multi-antenna receive processor 458, controller/processor 459, memory 460, data source 467} in example 4.

As an example, the first processor 1902 includes at least one of { antenna 452, receiver/transmitter 454, receive processor 456, transmit processor 468, multi-antenna receive processor 458, multi-antenna transmit processor 457, controller/processor 459, memory 460, and data source 467} in example 4.

Example 20

Embodiment 20 illustrates a block diagram of a processing arrangement for use in a second node according to an embodiment of the application, as shown in fig. 20. In fig. 20, the processing means 2000 in the second node comprises a second processor 2001.

A second processor 2001, configured to send at least one CSI reporting configuration, wherein the at least one CSI reporting configuration is configured to configure reporting of the at least one CSI;

In embodiment 20, a target receiver of the first DCI determines whether to transmit target CSI on the first PUSCH, the target receiver of the first DCI transmits the target CSI on the first PUSCH only when a first condition is met, a target CSI reporting configuration is used to configure reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, the target CSI is one of the at least one CSI, the target CSI reporting configuration indicates a first set of resources used for at least one of channel measurements or interference resource measurements of the target CSI, the first set of resources includes one or more RS resources, the first condition includes a first symbol not earlier than a first reference symbol, the first symbol is a first uplink symbol in the first PUSCH for carrying the at least one CSI, the first reference symbol is a downlink symbol that starts from a first time interval following a last symbol of the first PDCCH, and the first time interval is based on whether the target CSI is generated.

As an embodiment, the second processor 2001 determines whether to receive the target CSI on the first PUSCH only when a first condition is met.

As an embodiment, the generation mode of the target CSI is based on AI, and the generation mode of the target CSI comprises that the target receiver of the first DCI performs a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation.

As an embodiment, the second processor 2001, when the first condition is not satisfied, discards receiving a signal on the first PUSCH or discards receiving the target CSI on the first PUSCH;

as one embodiment, the target receiver of the first DCI ignores the first DCI when the first condition is not met, wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

The second processor 2001 performs a second operation, wherein the output of the first operation comprises the first CSI, the target CSI carrying the first CSI, the first CSI being used as an input to the second operation by a target receiver of the target CSI to generate a second CSI.

For one embodiment, the second processor 2001 deploys the second operation.

As an embodiment, the first operation is training-based or AI-based.

As an embodiment, the second operation is training-based or AI-based.

As an embodiment, the second node is a base station device.

As an embodiment, the second node is a user equipment.

As an embodiment, the second node is a relay node device.

As an example, the second processor 2001 includes at least one of { antenna 420, receiver/transmitter 418, receive processor 470, transmit processor 416, multi-antenna receive processor 472, multi-antenna transmit processor 471, controller/processor 475, memory 476} in example 4.

Those of ordinary skill in the art will appreciate that all or a portion of the steps of the above-described methods may be implemented by a program that instructs associated hardware, and the program may be stored on a computer readable storage medium, such as a read-only memory, a hard disk or an optical disk. Alternatively, all or part of the steps of the above embodiments may be implemented using one or more integrated circuits. Accordingly, each module unit in the above embodiment may be implemented in a hardware form or may be implemented in a software functional module form, and the present application is not limited to any specific combination of software and hardware. The user equipment, the terminal and the UE in the application comprise, but are not limited to, unmanned aerial vehicles, communication modules on unmanned aerial vehicles, remote control airplanes, aircrafts, mini-planes, mobile phones, tablet computers, notebooks, vehicle-mounted Communication equipment, wireless sensors, network cards, internet of things terminals, RFID terminals, NB-IOT terminals, MTC (MACHINE TYPE Communication) terminals, eMTC (ENHANCED MTC ) terminals, data cards, network cards, vehicle-mounted Communication equipment, low-cost mobile phones, low-cost tablet computers and other wireless Communication equipment. The base station or system device in the present application includes, but is not limited to, a macro cell base station, a micro cell base station, a home base station, a relay base station, a gNB (NR node B) NR node B, a TRP (TRANSMITTER RECEIVER Point, transmission/reception node), and other wireless communication devices.

The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the present application. Any changes and modifications made based on the embodiments described in the specification should be considered obvious and within the scope of the present application if similar partial or full technical effects can be obtained.

Claims

1. A method in a first node for wireless communication, comprising:

Receiving at least one CSI reporting configuration; receiving a first DCI on a first PDCCH, wherein the first DCI triggers reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used to configure reporting of the at least one CSI;

determining whether to send target CSI on the first PUSCH; and sending the target CSI on the first PUSCH only when a first condition is met;

Among them, the target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, and the target CSI is one of the at least one CSI; the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of the channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the first condition includes that the first symbol is not earlier than the first reference symbol, the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol of the CP starting at the first time interval after the end of the last symbol of the first PDCCH; the first time interval depends on whether the generation method of the target CSI is based on AI.

2. The method according to claim 1 is characterized in that the calculation formula of the first time interval depends on a first parameter, and the first parameter depends on whether the generation method of the target CSI is based on AI.

3. The method according to claim 1 or 2 is characterized in that when the target CSI is generated based on AI, the target CSI includes N information blocks, and the N information blocks respectively include channel information of N time units, N is a positive integer greater than 1; the first time interval depends on at least one of the N time units.

4. The method according to any one of claims 1 to 3 is characterized in that, when the first resource set consists of one or more non-periodic RS resources, the first condition also includes that the second symbol is not earlier than a second reference symbol, and the second reference symbol is the next uplink symbol whose CP starts at a second time interval after the end of the last symbol of the first RS in the first resource set.

5. The method according to any one of claims 1 to 4 is characterized in that the method for generating the target CSI is based on AI and includes: the method for generating the target CSI includes performing a first operation, an input of the first operation depends on a measurement based on the first resource set, and the target CSI depends on an output of the first operation.

6. The method according to any one of claims 1 to 5, characterized in that the target CSI generation method is based on AI and includes: the target CSI generation method is associated with a first type of identifier.

7. The method according to claim 6 is characterized in that, when the target CSI is generated based on AI, the first time interval depends on the first type of identifier associated with the target CSI generation method.

8. The method according to any one of claims 1 to 7 is characterized in that the target CSI is generated based on AI, including: the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

9. The method according to any one of claims 1 to 8, characterized in that it comprises:

When the first condition is not met, ignoring the first DCI;

Therein, no HARQ-ACK or transport block is multiplexed on the first PUSCH.

10. A terminal, characterized in that the terminal comprises: one or more processors and a memory;

The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions. The one or more processors call the computer instructions to enable the terminal to execute the method according to any one of claims 1 to 9.

11. A method in a second node for wireless communication, comprising:

Sending at least one CSI reporting configuration; sending a first DCI on a first PDCCH, wherein the first DCI triggers reporting of at least one CSI on a first PUSCH, and the at least one CSI reporting configuration is used to configure reporting of the at least one CSI;

Among them, the target recipient of the first DCI determines whether to send the target CSI on the first PUSCH; only when the first condition is met, the target recipient of the first DCI sends the target CSI on the first PUSCH; the target CSI reporting configuration is used to configure the reporting of the target CSI, the target CSI reporting configuration is one of the at least one CSI reporting configuration, and the target CSI is one of the at least one CSI; the target CSI reporting configuration indicates a first resource set, the first resource set is used for at least one of the channel measurement or interference resource measurement of the target CSI, and the first resource set includes one or more RS resources; the first condition includes that the first symbol is not earlier than the first reference symbol, the first symbol is the first uplink symbol in the first PUSCH for carrying the at least one CSI, and the first reference symbol is the next uplink symbol of the CP starting at the first time interval after the end of the last symbol of the first PDCCH; the first time interval depends on whether the generation method of the target CSI is based on AI.

12. The method according to claim 11, characterized in that it comprises:

Determine whether to receive target CSI on the first PUSCH; and receive the target CSI on the first PUSCH only when a first condition is met.

13. The method according to claim 11 or 12 is characterized in that the calculation formula of the first time interval depends on a first parameter, and the first parameter depends on whether the generation method of the target CSI is based on AI.

14. The method according to any one of claims 11 to 13 is characterized in that, when the target CSI is generated based on AI, the target CSI includes N information blocks, and the N information blocks respectively include channel information of N time units, N is a positive integer greater than 1; the first time interval depends on at least one of the N time units.

15. The method according to any one of claims 11 to 14 is characterized in that, when the first resource set consists of one or more non-periodic RS resources, the first condition also includes that the second symbol is not earlier than a second reference symbol, and the second reference symbol is the next uplink symbol of the second time interval after the end of the last symbol of the first RS in the first resource set with the CP starting.

16. The method according to any one of claims 11 to 15 is characterized in that the target CSI is generated based on AI and includes: the target CSI is generated by the target receiver of the first DCI performing a first operation, the input of the first operation depends on the measurement based on the first resource set, and the target CSI depends on the output of the first operation.

17. The method according to any one of claims 11 to 16, characterized in that the target CSI generation method is based on AI and includes: the target CSI generation method is associated with a first type of identifier.

18. The method according to claim 17 is characterized in that, when the target CSI is generated based on AI, the first time interval depends on the first type of identifier associated with the target CSI generation method.

19. The method according to any one of claims 11 to 18 is characterized in that the target CSI is generated based on AI and includes: the target CSI indicates at least one resource in a second resource set, and the second resource set includes resources that do not belong to the first resource set.

20. The method according to any one of claims 11 to 19, comprising:

When the first condition is not met, giving up receiving a signal on the first PUSCH, or giving up receiving the target CSI on the first PUSCH;

Therein, no HARQ-ACK or transport block is multiplexed on the first PUSCH.

21. The method according to any one of claims 11 to 19, characterized in that when the first condition is not met, the target receiver of the first DCI ignores the first DCI; wherein no HARQ-ACK or transport block is multiplexed on the first PUSCH.

22. A base station, characterized in that the base station comprises: one or more processors and a memory;

The memory is coupled to the one or more processors, and the memory is used to store computer program codes, where the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the base station to perform the method according to any one of claims 11 to 21.