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WO2026016000A1 - Procédés et appareils de communication sans fil, et dispositif, puce et support de stockage - Google Patents

Procédés et appareils de communication sans fil, et dispositif, puce et support de stockage

Info

Publication number
WO2026016000A1
WO2026016000A1 PCT/CN2024/105501 CN2024105501W WO2026016000A1 WO 2026016000 A1 WO2026016000 A1 WO 2026016000A1 CN 2024105501 W CN2024105501 W CN 2024105501W WO 2026016000 A1 WO2026016000 A1 WO 2026016000A1
Authority
WO
WIPO (PCT)
Prior art keywords
information
model
terminal
network
qos
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2024/105501
Other languages
English (en)
Chinese (zh)
Inventor
卢前溪
付喆
林雪
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Guangdong Oppo Mobile Telecommunications Corp Ltd
Original Assignee
Guangdong Oppo Mobile Telecommunications Corp Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Guangdong Oppo Mobile Telecommunications Corp Ltd filed Critical Guangdong Oppo Mobile Telecommunications Corp Ltd
Priority to PCT/CN2024/105501 priority Critical patent/WO2026016000A1/fr
Publication of WO2026016000A1 publication Critical patent/WO2026016000A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L1/00—Arrangements for detecting or preventing errors in the information received
    • H04L1/08—Arrangements for detecting or preventing errors in the information received by repeating transmission, e.g. Verdan system

Definitions

  • This application relates to the field of communication technology, specifically to a wireless communication method, apparatus, device, chip, and storage medium.
  • Hybrid Automatic Repeat Request In wireless communication systems, to overcome the impact of time-varying characteristics of wireless channels and multipath fading on signal transmission, Hybrid Automatic Repeat Request (HARQ) technology is commonly used.
  • HARQ is a technique that combines Forward Error Correction (FEC) and ARQ methods. FEC adds redundant information, enabling the receiver to correct some errors, thereby reducing the number of retransmissions. For errors that FEC cannot correct, the receiver requests the transmitter to retransmit the data according to the ARQ mechanism.
  • Current HARQ retransmissions can be divided into two categories: active retransmission and passive retransmission.
  • proactive retransmission can help reduce service latency, it also leads to higher resource waste/consumption; while passive retransmission can avoid unnecessary resource consumption, feedback-based retransmission results in higher latency.
  • This application provides a wireless communication method, apparatus, device, chip, and storage medium.
  • embodiments of this application provide a wireless communication method, the method comprising: a first device determining a retransmission decision corresponding to first information based on a first artificial intelligence (AI) model; and, if the retransmission decision is to retransmit, the first device sending retransmitted data to a second device.
  • AI artificial intelligence
  • embodiments of this application provide a wireless communication method, the method comprising: a second device determining feedback information and/or first indication information corresponding to fourth information based on a third AI model; wherein the feedback information is feedback information for data transmitted by a first device; the first indication information is used to indicate whether to retransmit the data; and the second device sending the feedback information and/or the first indication information to the first device.
  • embodiments of this application provide a wireless communication device, the device comprising: a first determining unit configured to determine a retransmission decision corresponding to first information based on a first AI model; and a first communication unit configured to send retransmission data to a second device when the retransmission decision is to retransmit.
  • embodiments of this application provide a wireless communication device, the device comprising: a second determining unit configured to determine feedback information and/or first indication information corresponding to fourth information based on a third AI model; wherein the feedback information is feedback information for data transmitted by a first device; the first indication information is used to indicate whether to retransmit the transmitted data; and a second communication unit configured to send the feedback information and/or the first indication information to the first device.
  • embodiments of this application provide a communication device, including: a memory for storing a computer program; a processor connected to the memory for calling and running the computer program from the memory to implement the method described in the first or second aspect; and a transceiver for receiving and sending information during the process of sending and receiving information with other devices.
  • the chip includes: a processor for retrieving and running a computer program from a memory, causing a device on which the chip is installed to perform the method described in the first or second aspect; and a transceiver for receiving and sending information during the exchange of information with the device or the chip.
  • embodiments of this application provide a computer-readable storage medium for storing a computer program that causes a computer to perform the methods described in the first or second aspect.
  • embodiments of this application provide a computer program product including computer program instructions that cause a computer to perform the method described in the first or second aspect.
  • embodiments of this application provide a computer program that, when run on a computer, causes the computer to perform the method described in the first or second aspect.
  • the first device determines the retransmission decision corresponding to the first information based on the first AI model; if the retransmission decision is to retransmit, the first device sends retransmission data to the second device; thus, by using the intelligence of the AI model to determine the retransmission decision corresponding to the first information, a retransmission decision that is more consistent with and appropriate to the first information can be obtained, thereby helping to save transmission resource overhead.
  • Figure 1 is a schematic diagram of an application scenario of an embodiment of this application.
  • FIG. 2 is a flowchart illustrating the wireless communication method provided in an embodiment of this application.
  • FIG. 3 is a schematic flowchart of the wireless communication method provided in an embodiment of this application.
  • FIG. 4 is a schematic flowchart of the wireless communication method provided in an embodiment of this application.
  • Figure 5 is a flowchart illustrating the wireless communication method provided in an embodiment of this application.
  • Figure 6 is a flowchart illustrating the wireless communication method provided in an embodiment of this application.
  • Figure 7 is a flowchart illustrating the wireless communication method provided in an embodiment of this application.
  • Figure 8 is a flowchart illustrating the wireless communication method provided in an embodiment of this application.
  • Figure 9 is a flowchart illustrating the wireless communication method provided in an embodiment of this application.
  • Figure 10 is a flowchart illustrating the wireless communication method provided in an embodiment of this application.
  • Figure 11 is a flowchart illustrating the wireless communication method provided in an embodiment of this application.
  • Figure 12 is a schematic diagram of the structural composition of the wireless communication device provided in an embodiment of this application.
  • Figure 13 is a schematic diagram of the structure of the wireless communication device provided in an embodiment of this application.
  • Figure 14 is a schematic structural diagram of a communication device provided in an embodiment of this application.
  • Figure 15 is a schematic structural diagram of a chip according to an embodiment of this application.
  • Figure 16 is a schematic block diagram of a communication system provided in an embodiment of this application.
  • Figure 1 is a schematic diagram of an application scenario of an embodiment of this application.
  • the communication system 100 may include a terminal device 110 and a network device 120.
  • the network device 120 can communicate with the terminal device 110 via an air interface. Multi-service transmission is supported between the terminal device 110 and the network device 120.
  • LTE Long Term Evolution
  • TDD LTE Time Division Duplex
  • UMTS Universal Mobile Telecommunication System
  • IoT Internet of Things
  • NB-IoT Narrow Band Internet of Things
  • eMTC enhanced Machine-Type Communications
  • 5G communication system also known as New Radio (NR) communication system
  • 6G communication system or future communication systems, etc.
  • network device 120 may be an access network device that communicates with terminal device 110.
  • the access network device can provide communication coverage for a specific geographical area and can communicate with terminal device 110 (e.g., UE) located within that coverage area.
  • terminal device 110 e.g., UE
  • Network device 120 may be an evolved Node B (eNB or eNodeB) in a Long Term Evolution (LTE) system, a Next Generation Radio Access Network (NG RAN) device, a base station (gNB) in an NR system, a base station in a 6G system, a radio controller in a Cloud Radio Access Network (CRAN), or a relay station, access point, vehicle-mounted device, wearable device, hub, switch, bridge, router, or network device in a future evolved Public Land Mobile Network (PLMN), etc.
  • eNB evolved Node B
  • NG RAN Next Generation Radio Access Network
  • gNB base station
  • CRAN Cloud Radio Access Network
  • PLMN Public Land Mobile Network
  • Terminal device 110 can be any terminal device, including but not limited to terminal devices that are connected to network device 120 or other terminal devices via wired or wireless connections.
  • terminal equipment 110 can refer to an access terminal, user equipment (UE), user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication equipment, user agent, or user device.
  • Access terminals can be cellular phones, cordless phones, Session Initiation Protocol (SIP) phones, IoT devices, satellite handheld terminals, Wireless Local Loop (WLL) stations, Personal Digital Assistants (PDAs), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to a wireless modem, in-vehicle devices, wearable devices, terminal equipment in 5G networks, terminal equipment in 6G networks, or terminal equipment in future evolved networks, etc.
  • SIP Session Initiation Protocol
  • WLL Wireless Local Loop
  • PDAs Personal Digital Assistants
  • Terminal device 110 can be used for device-to-device (D2D) communication.
  • D2D device-to-device
  • the communication system 100 may further include a core network device 130 that communicates with the network device 120.
  • This core network device 130 may be a 5G core network (5G Core, 5GC) device, such as an Access and Mobility Management Function (AMF), an Authentication Server Function (AUSF), a User Plane Function (UPF), or a Session Management Function (SMF).
  • the core network device 130 may also be an Evolved Packet Core (EPC) device for an LTE network, such as a Session Management Function + Core Packet Gateway (SMF+PGW-C) device.
  • EPC Evolved Packet Core
  • SMF+PGW-C Session Management Function + Core Packet Gateway
  • SMF+PGW-C can simultaneously implement the functions of both SMF and PGW-C.
  • the aforementioned core network equipment may also be called by other names, or new network entities may be formed by dividing the functions of the core network. This application does not impose any restrictions on this.
  • the various functional units in the communication system 100 can also communicate with each other through a next-generation (NG) interface.
  • NG next-generation
  • terminal devices establish air interface connections with access network devices through the NR interface for transmitting user plane data and control plane signaling; terminal devices can establish control plane signaling connections with the AMF through NG interface 1 (N1); access network devices, such as next-generation radio access base stations (gNB), can establish user plane data connections with the UPF through NG interface 3 (N3); access network devices can establish control plane signaling connections with the AMF through NG interface 2 (N2); the UPF can establish control plane signaling connections with the SMF through NG interface 4 (N4); the UPF can interact with the data network for user plane data through NG interface 6 (N6); the AMF can establish control plane signaling connections with the SMF through NG interface 11 (N11); and the SMF can establish control plane signaling connections with the PCF through NG interface 7 (N7).
  • N1 next-generation radio access base stations
  • gNB next-generation radio access base stations
  • N3 next-generation radio access base stations
  • access network devices can establish control plane signaling connections with the AMF through NG interface
  • Figure 1 exemplarily illustrates a network device, a core network device, and two terminal devices.
  • the communication system 100 may include multiple network devices, and each network device may include other numbers of terminal devices within its coverage area. This application embodiment does not limit this.
  • Figure 1 is merely an example illustrating the system to which this application applies.
  • the method shown in the embodiments of this application can also be applied to other systems.
  • system and “network” are often used interchangeably in this document.
  • the term “and/or” in this document merely describes the relationship between related objects, indicating that three relationships can exist.
  • a and/or B can represent: A existing alone, A and B existing simultaneously, or B existing alone.
  • the character "/" in this document generally indicates that the preceding and following related objects have an "or” relationship.
  • "instruction” mentioned in the embodiments of this application can be a direct instruction, an indirect instruction, or an indication of a related relationship.
  • a instructing B can mean that A directly instructs B, for example, B can be obtained through A; it can also mean that A indirectly instructs B, for example, A instructs C, B can be obtained through C; or it can mean that there is a related relationship between A and B.
  • "correspondence" mentioned in the embodiments of this application can indicate a direct or indirect correspondence between two things, or an related relationship between two things, or a relationship of instruction and being instructed, configuration and being configured, etc.
  • predefined or “predefined rules” mentioned in the embodiments of this application can be implemented by pre-storing corresponding codes, tables, or other means that can be used to indicate relevant information in the device (e.g., including terminal devices and network devices), and this application does not limit the specific implementation method.
  • predefined can refer to those defined in a protocol.
  • protocol can refer to standard protocols in the field of communication, such as LTE protocol, NR protocol, and related protocols applied to future communication systems, and this application does not limit this.
  • HARQ is a technique that combines FEC and ARQ methods.
  • FEC adds redundant information, enabling the receiver to correct some errors and thus reduce the number of retransmissions.
  • the receiver requests the sender to retransmit the data according to the ARQ mechanism.
  • Table 1 current HARQ retransmissions can be divided into two categories:
  • AM Acknowledgement Mode
  • DL-SCH Downlink Shared Channel
  • UL-SCH Uplink Shared Channel
  • the AM entity's transmitter supports retransmission of RLC SDUs or RLC SDU segments (ARQ): the RLC PDU sent to the MAC layer is simultaneously placed in a retransmission buffer to support potential retransmissions. If an ACK corresponding to the entire RLC SDU is received, the corresponding RLC PDU is removed from the retransmission buffer; if a NACK corresponding to the entire or partial RLC SDU is received, the entire or partial SDU is retransmitted. As shown in Table 2, current HARQ retransmissions can be divided into two categories:
  • PDCP retransmission is currently limited to mobile processes.
  • the sending end actively retransmits, and on the other hand, the receiving end avoids unnecessary retransmissions through SR.
  • the receiving end can be compared to RLC ARQ.
  • PDCP Duplication is currently more based on active type mechanisms.
  • Release 19 will define a comprehensive framework for the application of artificial intelligence/machine learning in the air interface and will conduct standardization work for related use cases such as positioning and beam management. Furthermore, 3GPP will continue to explore the availability of artificial intelligence/machine learning in other use cases, such as mobility.
  • an AI-assisted retransmission mechanism is established, and the specific implementation includes:
  • This application provides a wireless communication method, apparatus, device, chip, and storage medium.
  • a first device determines a retransmission decision corresponding to first information based on a first AI model; if the retransmission decision is to retransmit, the first device sends retransmitted data to a second device.
  • a retransmission decision that is more consistent and appropriate to the first information can be obtained, thereby helping to save transmission resource overhead.
  • the second device determines the feedback information and/or the first indication information corresponding to the fourth information based on the third AI model; the second device sends the feedback information and/or the first indication information to the first device; wherein, the feedback information is feedback information for the data sent by the first device; the first indication information is used to indicate whether to retransmit the data; in this way, by utilizing the intelligence of the AI model, feedback information and/or the first indication information that are more consistent with the fourth information can be obtained, thereby helping to reduce service transmission latency.
  • FIG. 2 is a flowchart illustrating a wireless communication method provided in an embodiment of this application. As shown in Figure 2, the method may include the following steps:
  • the first device determines the retransmission decision corresponding to the first information based on the first AI model
  • the first device sends the retransmission data to the second device.
  • the first device sends new data to the second device.
  • the wireless communication method shown in Figure 2 is an active retransmission mechanism, which is applicable to both downlink and uplink data transmission.
  • the retransmission decision includes whether to retransmit and/or the number of retransmissions.
  • the retransmission decision includes one or more of the following decisions:
  • the first information is not limited; it refers to information that affects the determination of the retransmission decision, i.e., information related to the retransmission decision.
  • the first information is QoS-related information.
  • the first information includes one or more of the following:
  • Second information sent by the second device includes one or more of the following: feedback information on the data sent by the first device, time-related information on the transmission of the feedback information, first indication information on the data sent by the first device, and time-related information on the transmission of the first indication information; the first indication information is used to indicate whether to retransmit the data.
  • Third information is information determined based on one or more of the second information, QoS performance, QoS requirement information, channel measurement information and buffer information.
  • QoS requirement-related information includes terminal-side QoS requirement-related information and/or network-side QoS requirement-related information.
  • the first device is a terminal device and the second device is a network device.
  • the QoS requirements related to the terminal side may include the uplink service pattern; the QoS requirements related to the network side may include latency and/or rate, etc.
  • the first device is a network device and the second device is a terminal device.
  • the QoS requirements related to the terminal side may include latency and/or rate, etc.; the QoS requirements related to the network side may include downlink service patterns.
  • the channel measurement information includes uplink channel measurement information and/or downlink channel measurement information.
  • the QoS performance in the first information can be understood as QoS performance-related information, which may include terminal-side QoS performance (i.e., terminal-side QoS performance-related information) and/or network-side QoS performance (i.e., network-side QoS performance-related information).
  • QoS performance-related information may include terminal-side QoS performance (i.e., terminal-side QoS performance-related information) and/or network-side QoS performance (i.e., network-side QoS performance-related information).
  • the QoS performance in the first information includes QoS performance generated based on the second information (such as terminal-side and/or network-side QoS performance), and/or QoS performance generated based on retransmission decisions (such as terminal-side and/or network-side QoS performance).
  • the first device is a terminal device and the second device is a network device.
  • the QoS performance on the terminal side may include one or more of power consumption and resource consumption; the QoS performance on the network side may include latency and/or rate, etc.
  • the first device is a network device and the second device is a terminal device.
  • the QoS performance on the terminal side may include latency and/or rate, etc.; the QoS performance on the network side may include one or more of power consumption and resource consumption.
  • the QoS performance (such as terminal-side and/or network-side QoS performance) generated based on the second information can be information obtained by the second device based on the third AI model, or information obtained based on other methods (such as retransmission mechanisms such as MAC HARQ, RLC ARQ, and/or PDCP ARQ mentioned in the related technologies above).
  • the QoS performance (such as terminal-side and/or network-side QoS performance) generated based on the retransmission decision can be information obtained by the first device based on the first AI model, or information obtained based on other methods (such as retransmission mechanisms such as MAC HARQ, RLC ARQ, and/or PDCP ARQ mentioned in the related technologies above).
  • the third information may include one or more of the following:
  • the third information may also include a normalized value of one or more of the following: second information, QoS performance, QoS requirement-related information, channel measurement information, and cache-related information.
  • the third information may also include a weighted average of one or more of the following: second information, QoS performance, QoS requirement-related information, channel measurement information, and buffer-related information.
  • the aforementioned first information can be used as input information for the first AI model, which then determines the retransmission decision corresponding to the first information based on the input first information.
  • the model parameters of the first AI model are obtained by iteratively updating the first information at different times.
  • the basis for updating the model parameters of the first AI model is the QoS performance generated based on the retransmission decision.
  • the cutoff condition for iterative updates is that the QoS performance generated based on the retransmission decision or the parameter value determined based on the QoS performance meets the predefined QoS performance index.
  • the model parameters of the first AI model are updated based on the QoS performance generated by the retransmission decision, wherein the retransmission decision includes the retransmission decision corresponding to the first information of the first AI model.
  • the wireless communication method provided in this embodiment further includes: the first device sending a retransmission decision to the second device.
  • the first device sends a retransmission decision to the second device for two reasons. First, it is to facilitate the second device (for example, in the case of model management of the first AI model on the second device side) to decide whether to replace the first AI model with the second AI model. Second, it can also serve as one of the bases for the second device to determine the passive retransmission decision.
  • the first device sends a retransmission decision to the second device via one or more of the following signaling:
  • the first device periodically sends a retransmission decision to the second device. In other embodiments, the first device sends a retransmission decision to the second device when a first condition is met.
  • the first condition includes one or more of the following:
  • the retransmission decision is different from the previous retransmission decision
  • a retransmission decision that differs from a previous retransmission decision can be that the current retransmission decision is different from the previous retransmission decision.
  • the difference could be that the current retransmission decision is to retransmit (or not to retransmit), while the previous retransmission decision was to not retransmit (or to retransmit); and/or, the number of retransmissions in the current retransmission decision is different from the number of retransmissions in the previous retransmission decision.
  • FIG 3 is a schematic flowchart of the wireless communication method provided in an embodiment of this application. As shown in Figure 3, the method may include the following steps:
  • the second device determines the feedback information and/or the first indication information corresponding to the fourth information based on the third AI model; wherein, the feedback information is feedback information for the data sent by the first device; the first indication information is used to indicate whether to retransmit the data;
  • the second device sends feedback information and/or first instruction information to the first device.
  • the feedback information determined by the third AI model regarding the data transmitted by the first device can be for data that the first device has not transmitted, data that has not been received during transmission, or data that has been received during transmission. Furthermore, for data that has been received during transmission, this feedback information can be determined before the verification result of the transmitted data is obtained.
  • the wireless communication method shown in Figure 3 is a passive retransmission mechanism, which is applicable to both downlink and uplink data transmission.
  • S302 includes: the second device sending feedback information and/or first indication information to the first device according to the transmission time related information; wherein, the transmission time related information includes the first transmission time of the feedback information and/or the second transmission time of the first indication information.
  • both the first transmission time and/or the second transmission time occur before the verification result of the transmitted data is obtained. This helps to reduce service transmission latency.
  • the method for determining the first transmission time and/or the second transmission time is not limited.
  • the first transmission time and/or the second transmission time are pre-configured.
  • the first transmission time and/or the second transmission time are information determined by a third AI model using fourth information.
  • a third AI model can be used to determine the advance transmission of feedback information and/or the first indication information. Time; thus, it helps to reduce business transmission latency.
  • the first transmission time and/or the second transmission time satisfy one or more of the following:
  • the timer is used to trigger the sending of feedback information
  • the feedback information includes ACK information or NACK information, wherein the NACK information refers to feedback information for data loss or receiving incorrect data; the ACK information can be feedback information for receiving correct data, or it can be feedback information for data loss or receiving incorrect data (i.e., false ACK information).
  • a third AI model can identify false ACK messages (i.e., messages indicating incorrect data reception), and sending false ACK messages can avoid meaningless retransmissions when latency is too high, thereby reducing service transmission latency.
  • sending NACK messages in advance can help the sender perform rapid retransmissions.
  • the feedback information includes MAC HARQ-ACK information and/or RLC ARQ SR; and/or, the first indication information includes MAC HARQ retransmission scheduling and/or PDCP duplication retransmission configuration.
  • MAC HARQ-ACK information is an acknowledgment message regarding the reception of transmitted data.
  • This acknowledgment message includes ACK information or NACK information.
  • the ACK information can be feedback information for receiving correct data, or it can be feedback information for data loss or receiving incorrect data (i.e., false ACK information).
  • the NACK information can be information for data loss or receiving incorrect data.
  • the RLC ARQ SR includes ACK information or NACK information, wherein the NACK information refers to feedback information for data loss or receiving incorrect data; the ACK information can be feedback information for receiving correct data, or it can be feedback information for data loss or receiving incorrect data (i.e., false ACK information).
  • the fourth information is not limited; this information is information that affects the determination result of the feedback information and/or the first indication information.
  • the fourth information is QoS-related information.
  • the fourth information includes one or more of the following:
  • Fifth information is information determined based on one or more of the following: QoS requirement-related information, channel measurement information, buffer-related information, retransmission decision sent by the first device, QoS performance, and downlink data reception status.
  • QoS requirement-related information includes terminal-side QoS requirement-related information and/or network-side QoS requirement-related information.
  • the first device is a terminal device and the second device is a network device.
  • the QoS requirements related to the terminal side may include the uplink service pattern; the QoS requirements related to the network side may include latency and/or rate, etc.
  • the first device is a network device and the second device is a terminal device.
  • the QoS requirements related to the terminal side may include latency and/or rate, etc.; the QoS requirements related to the network side may include downlink service patterns.
  • the channel measurement information includes uplink channel measurement information and/or downlink channel measurement information.
  • the QoS performance in the fourth information can be understood as QoS performance-related information, which may include terminal-side QoS performance (i.e., terminal-side QoS performance-related information) and/or network-side QoS performance (i.e., network-side QoS performance-related information).
  • QoS performance-related information may include terminal-side QoS performance (i.e., terminal-side QoS performance-related information) and/or network-side QoS performance (i.e., network-side QoS performance-related information).
  • the QoS performance in the fourth information includes QoS performance generated based on the second information (such as terminal-side and/or network-side QoS performance), and/or QoS performance generated based on retransmission decisions (such as terminal-side and/or network-side QoS performance).
  • the first device is a terminal device and the second device is a network device.
  • the QoS performance on the terminal side may include one or more of power consumption and resource consumption; the QoS performance on the network side may include latency and/or rate, etc.
  • the first device is a network device and the second device is a terminal device.
  • the QoS performance on the terminal side may include latency and/or rate, etc.; the QoS performance on the network side may include one or more of power consumption and resource consumption.
  • the QoS performance based on the second information can be information obtained by the second device based on the third AI model, or information obtained based on other methods (such as retransmission mechanisms such as MAC HARQ, RLC ARQ, and/or PDCP ARQ mentioned in the related technologies above).
  • the QoS performance based on the retransmission decision can be information obtained by the first device based on the first AI model, or information obtained based on other methods (such as retransmission mechanisms such as MAC HARQ, RLC ARQ, and/or PDCP ARQ mentioned in the related technologies above).
  • the fifth information may include one or more of the following:
  • the fifth information may also include a normalized value of one or more of the following: retransmission decision, QoS performance, downlink data reception status, QoS requirement-related information, channel measurement information, and buffer-related information.
  • the downlink data reception status includes one or more of the following reception statuses:
  • the fifth information may also include a weighted average of one or more of the following: retransmission decision, QoS performance, downlink data reception status, QoS requirement-related information, channel measurement information, and buffer-related information.
  • the aforementioned fourth information can be used as input information for the third AI model, which then uses the input fourth information to determine the corresponding feedback information and/or first indication information.
  • the wireless communication method provided in this embodiment further includes: a second device sending second information to a first device; wherein the second information includes one or more of the following:
  • the second device sends the second information to the first device.
  • this is to facilitate the first device to decide whether to replace the third AI model with the fourth AI model.
  • it can also serve as one of the bases for the first device to make an active retransmission decision.
  • the model parameters of the third AI model are obtained by iteratively updating the fourth information at different times.
  • the basis for updating the model parameters of the third AI model is the QoS performance generated based on the second information.
  • the cutoff condition for iterative updates is that the QoS performance generated based on the second information or the parameter value determined based on the QoS performance meets the predefined QoS performance index.
  • the update of the model parameters of the third AI model is based on the QoS performance generated by the second information, wherein the second information includes the second information corresponding to the fourth information of the third AI model.
  • the second device sends second information to the first device via one or more of the following signaling methods:
  • the second device periodically sends second information to the first device. In other embodiments, the second device sends the second information to the first device when a fourth condition is met.
  • the fourth condition includes one or more of the following:
  • identical feedback information refers to feedback information for the same sent data/data packets, and the content of these feedback information is the same, such as all being ACK information or all being NACK information.
  • the same first indication information refers to the first indication information for the same transmitted data/data packet, and the content of these indication information is the same, such as the same PDCP duplication retransmission configuration or the same MAC HARQ retransmission schedule.
  • the first AI model can be applied on the first device, that is, for uplink (or downlink), a one-sided model based on the terminal device (or network device) can be used to optimize the active retransmission mechanism.
  • a third AI model can be used on the second device, that is, for uplink (or downlink), a one-sided model based on network device (or terminal device) can be used to optimize the passive retransmission mechanism.
  • a first AI model can be applied on the first device and a third AI model can be applied on the second device. That is, for uplink (or downlink), based on the dual-side models of terminal devices and network devices, the active retransmission mechanism and the passive retransmission mechanism can be optimized.
  • the above-described combination of one or more embodiments is applicable to communication systems where the first device is a terminal device and the second device is a network device; it is also applicable to communication systems where the first device is a network device and the second device is a terminal device.
  • the above-described combination of one or more embodiments' active retransmission mechanism is applicable to both uplink and downlink active retransmission mechanisms.
  • the above-described combination of one or more embodiments' passive retransmission mechanism is applicable to both uplink and downlink passive retransmission mechanisms. The following description does not distinguish between single-sided and double-sided models, but rather describes the relevant schemes for uplink and downlink active and passive retransmission mechanisms.
  • Example 1 and its further or additional embodiments describe an uplink-oriented active retransmission mechanism
  • Example 2 and its further or additional embodiments describe an uplink-oriented passive retransmission mechanism
  • Example 3 and its further or additional embodiments describe a downlink-oriented active retransmission mechanism
  • Example 4 and its further or additional embodiments describe a downlink-oriented passive retransmission mechanism.
  • Model inference refers to the model usage phase.
  • the terminal device uses a first AI model to determine the retransmission decision for uplink data; similarly, in Example 2, the network device uses a third AI model to determine second information such as feedback information and/or first indication information for uplink data.
  • Model updating refers to the model training phase.
  • the model parameters of the first AI model are updated; similarly, in Example 2, the model parameters of the third AI model are updated.
  • Model management refers to replacing the model.
  • Example 1 the first AI model is replaced with a second AI model, and the terminal device determines the retransmission decision based on the second AI model; similarly, in Example 2, the third AI model is updated to a fourth AI model, and the network device determines the second information based on the fourth AI model.
  • Embodiment 1 and its further or additional embodiments the embodiment corresponding to FIG2 and its further or additional embodiments are described, wherein the first device is a terminal device and the second device is an active retransmission mechanism of a network device.
  • FIG. 4 is a schematic flowchart of the wireless communication method provided in an embodiment of this application. As shown in Figure 4, the method may include the following steps:
  • the terminal device determines the retransmission decision corresponding to the first information based on the first AI model
  • the terminal device when the retransmission decision is not to retransmit, or the number of retransmissions in the retransmission decision is 0, the terminal device sends new uplink data to the network device.
  • the wireless communication method provided in this embodiment further includes: the terminal device receiving one or more of the following information sent by the network device:
  • the terminal device may obtain the above information from the network device according to per-flow, per-bearer, per-LCH, and/or per-LCG.
  • the above information may be information corresponding to per-flow, per-bearer, per-LCH, and/or per-LCG respectively.
  • the cache-related information includes the terminal-side uplink data cache information.
  • the first information includes information specific to the terminal device, or the first information includes information specific to the terminal device and information from other terminal devices different from the terminal device.
  • the type of information used to determine the retransmission decision is the same (both are primary information).
  • the retransmission decision at different times is based on the primary information at different times.
  • the primary information can be used as input information for the first AI model, and the first AI model obtains the corresponding retransmission decision based on the primary information.
  • the terminal device receiving one or more of the above-mentioned information sent by the network device does not limit the first information to including one or more of these information.
  • the first information includes one or more of the following:
  • QoS requirements related to the terminal side for example, uplink service pattern
  • Uplink channel measurement information for example, RSRP, RSRQ and/or SINR of uplink signals
  • Downlink channel measurement information for example, RSRP, RSRQ and/or SINR of downlink signals
  • Terminal-side uplink data cache information for example, BSR/DSR.
  • the terminal-side uplink data cache information may include information on the amount of cached data acquired for different data layers.
  • Second information sent by the network device includes one or more of the following: feedback information for data sent to the terminal device, time-related information for the transmission of feedback information, first indication information for data sent to the terminal device, and time-related information for the transmission of the first indication information; the first indication information is used to indicate whether to retransmit the data.
  • Terminal-side QoS performance for example, power consumption and/or resource consumption, etc.
  • Network-side QoS performance for example, latency and/or speed
  • the task of updating the model parameters of the first AI model used by the terminal device can be performed on either the network side or the terminal side.
  • the network device configures them to the terminal device via instruction information.
  • the wireless communication method provided in this embodiment further includes: the terminal device receiving second indication information sent by the network device, the second indication information being used to indicate that the model parameters of the first AI model be updated to new model parameters.
  • the network device receives one or more of the following information sent by the terminal device:
  • QoS requirements related to the terminal side for example, uplink service pattern
  • Terminal-side uplink data cache information for example, BSR/DSR.
  • the terminal-side uplink data cache information may include information on the amount of cached data acquired for different data layers.
  • Downlink channel measurement information for example, RSRP, RSRQ and/or SINR of downlink signals.
  • the network device may obtain the above information from the terminal device side according to per-flow, per-bearer, per-LCH, and/or per-LCG.
  • the above information may be information corresponding to per-flow, per-bearer, per-LCH, and/or per-LCG respectively.
  • the network device receiving one or more of the aforementioned information sent by the terminal device does not limit the first information to including only one or more of these information.
  • the first information may include one or more of the enumerated information above.
  • the network device determines the retransmission decision corresponding to the first information based on the first AI model, and sends the retransmission decision to the terminal device through the eighth indication information.
  • the terminal device executes the retransmission decision indicated by the eighth indication information.
  • the network device determines the first difference of the first AI model, and determines the new model parameters of the first AI model based on the first difference.
  • the first difference could be the difference between the terminal-side QoS performance and/or the network-side QoS performance and a predefined learning target.
  • the first AI model is a reinforcement learning model.
  • the network device can determine the corresponding reward based on the terminal-side QoS performance and/or network-side QoS performance generated by the retransmission decision, as well as the predefined learning objectives (such as terminal-side QoS performance indicators and/or network-side QoS performance indicators). Based on the reward, new model parameters of the first AI model are determined.
  • the wireless communication method provided in this embodiment further includes: the terminal device sending the terminal-side QoS performance generated based on the retransmission decision to the network device. This facilitates the network device in determining new model parameters for the first AI model based on the terminal-side QoS performance generated by the retransmission decision.
  • the new model parameters are related to the QoS performance resulting from retransmission decisions; the QoS performance resulting from retransmission decisions includes the terminal-side QoS performance and/or the network-side QoS performance based on retransmission decisions.
  • the network device may determine new model parameters for the first AI model if the terminal-side QoS performance resulting from the retransmission decision exceeds or falls below a corresponding threshold, and/or if the network-side QoS performance resulting from the retransmission decision exceeds or falls below a corresponding threshold.
  • the update/training task of the model parameters of the first AI model used by the terminal device is completed by the network device. This is mainly because the network device has stronger capabilities and storage capacity than the terminal device, making it easier to update or train the model.
  • the terminal device can also update the model parameters of the first AI model independently, that is, the task of updating/training the model parameters of the first AI model can also be done on the terminal side.
  • the wireless communication method provided in this embodiment further includes: the terminal device updating the model parameters of the first AI model to new model parameters based on the QoS performance generated by the retransmission decision; wherein the QoS performance generated based on the retransmission decision includes terminal-side QoS performance and/or network-side QoS performance.
  • the terminal device updates the model parameters of the first AI model to new model parameters based on the QoS performance generated by the retransmission decision, including: the terminal device determining a first difference of the first AI model based on the QoS performance generated by the retransmission decision; and the first device updating the model parameters of the first AI model to new model parameters based on the first difference.
  • the terminal device automatically updates or trains the model parameters of the first AI model.
  • the amount of information that the network device needs to transmit is less, which helps to save the signaling overhead incurred in obtaining the first information.
  • scenario mainly refers to physical environment, wireless channel conditions, etc. Therefore, in this application...
  • model management mechanism can be set on the network side or the terminal side. The model management is aimed at whether to replace the first AI model with the second AI model.
  • the wireless communication method provided in this embodiment further includes: a terminal device receiving third indication information sent by a network device, the third indication information being used to indicate that the first AI model is replaced with a second AI model.
  • the wireless communication method provided in this embodiment further includes: the terminal device reporting one or more of the following second conditions to the network device:
  • the uplink data cache information on the terminal side exceeds or falls below the corresponding threshold
  • the terminal device will report one or more of the information that meets the second condition to the network device, so that the network device can monitor the usage of the first AI model and replace the first AI model in a timely manner.
  • the wireless communication method provided in this embodiment further includes: the terminal device replacing the first AI model with the second AI model when one or more of the following second conditions are met:
  • the uplink data cache information on the terminal side exceeds or falls below the corresponding threshold
  • the wireless communication method provided in this embodiment further includes: the terminal device receiving fourth indication information sent by the network device, the fourth indication information being used to indicate the second condition.
  • the wireless communication method provided in this embodiment further includes: the terminal device reporting model replacement-related information for the first AI model to the network device.
  • model replacement-related information includes a second condition that triggers the replacement of the first AI model with the second AI model.
  • Embodiment 2 and its further or additional embodiments the embodiment corresponding to FIG3 and its further or additional embodiments are described, in which the first device is a terminal device and the second device is a passive retransmission mechanism of a network device.
  • Embodiments 1 and 2 both optimize the QoS performance of the uplink services of the terminal device.
  • FIG. 5 is a schematic flowchart of the wireless communication method provided in an embodiment of this application. As shown in Figure 5, the method may include the following steps:
  • the network device determines the feedback information and/or the first indication information corresponding to the fourth information based on the third AI model; wherein, the feedback information is feedback information for the data sent by the terminal device; the first indication information is used to indicate whether to retransmit the data;
  • the network device sends feedback information and/or first instruction information to the terminal device.
  • the wireless communication method provided in this embodiment further includes: the network device receiving one or more of the following information sent by the terminal device:
  • the network device may obtain the above information from the terminal device side according to per-flow, per-bearer, per-LCH, and/or per-LCG.
  • the above information may be information corresponding to per-flow, per-bearer, per-LCH, and/or per-LCG respectively.
  • the type of information used to determine the feedback information and/or the first indication information is the same (all are fourth information).
  • the feedback information and/or the first indication information at different times are based on the fourth information at different times.
  • the fourth information can be used as the input information of the third AI model, and the third AI model obtains the corresponding feedback information and/or the first indication information based on the fourth information.
  • the network device receiving one or more of the above-mentioned information sent by the terminal device does not limit the fourth information to including one or more of these information.
  • the fourth information includes one or more of the following:
  • QoS requirements related to the terminal side for example, uplink service pattern
  • Uplink channel measurement information for example, RSRP, RSRQ and/or SINR of uplink signals
  • Downlink channel measurement information for example, RSRP, RSRQ and/or SINR of downlink signals
  • Terminal-side uplink data cache information for example, BSR/DSR.
  • the terminal-side uplink data cache information may include information on the amount of cached data acquired for different data layers.
  • Terminal-side QoS performance for example, power consumption and/or resource consumption, etc.
  • Network-side QoS performance for example, latency and/or speed
  • the task of updating the model parameters of the third AI model used by the network device can be performed on the network side or on the terminal side.
  • the network device updates the model parameters of the third AI model to new model parameters based on the QoS performance generated by the second information; wherein, the QoS performance generated by the second information includes terminal-side QoS performance and/or network-side QoS performance.
  • the network device updates the model parameters of the third AI model to new model parameters based on the QoS performance generated by the second information, including: the network device determining a second difference of the third AI model based on the QoS performance generated by the second information; and the network device updating the model parameters of the third AI model to new model parameters based on the second difference.
  • the second difference could be the difference between the terminal-side QoS performance and/or the network-side QoS performance and a predefined learning target.
  • the third AI model is a reinforcement learning model.
  • the network device can determine the corresponding reward based on the terminal-side QoS performance and/or network-side QoS performance generated by the second information, as well as the predefined learning objectives (such as terminal-side QoS performance indicators and/or network-side QoS performance indicators). Based on the reward, new model parameters of the third AI model are determined.
  • the wireless communication method provided in this embodiment further includes: the network device receiving terminal-side QoS performance generated based on second information sent by the terminal device. This facilitates the network device in determining new model parameters for the third AI model based on the terminal-side QoS performance generated by the second information.
  • model management can be set on the network side or the terminal side. Model management is aimed at: whether to replace the third AI model with the fourth AI model.
  • the wireless communication method provided in this embodiment further includes: the network device replacing the third AI model with the fourth AI model when the second condition and/or the fifth condition are met.
  • the second condition includes one or more of the following:
  • the uplink data cache information on the terminal side exceeds or falls below the corresponding threshold
  • the wireless communication method provided in this embodiment further includes: a network device sending fourth indication information to a terminal device, the fourth indication information being used to indicate relevant information for reporting the second condition.
  • terminal devices For terminal devices, they can send relevant information that meets the second condition to network devices if one or more of the second conditions are met.
  • the fifth condition includes one or more of the following:
  • the first device is a network device and the second device is an active retransmission mechanism of the terminal device.
  • Figure 6 is a flowchart illustrating the wireless communication method provided in an embodiment of this application. As shown in Figure 6, the method may include the following steps:
  • the network device determines the retransmission decision corresponding to the first information based on the first AI model
  • the wireless communication method provided in this embodiment further includes: the network device receiving one or more of the following information sent by the terminal device:
  • the network device may obtain the above information from the terminal device side according to per-flow, per-bearer, per-LCH, and/or per-LCG.
  • the above information may be information corresponding to per-flow, per-bearer, per-LCH, and/or per-LCG respectively.
  • the cache-related information includes network-side downlink data cache information.
  • the type of information used to determine the retransmission decision is the same (both are primary information).
  • the retransmission decision at different times is based on the primary information at different times.
  • the primary information can be used as input information for the first AI model, and the first AI model obtains the corresponding retransmission decision based on the primary information.
  • the network device receiving one or more of the above-mentioned information sent by the terminal device does not limit the first information to including one or more of these information.
  • the first information includes one or more of the following:
  • QoS requirements related to the terminal side for example, downlink service pattern
  • Uplink channel measurement information for example, RSRP, RSRQ and/or SINR of uplink signals
  • Downlink channel measurement information for example, RSRP, RSRQ and/or SINR of downlink signals
  • Downlink data caching information on the network side for example, data volume information can be obtained for caching at different data layers;
  • Second information sent by the terminal device includes one or more of the following: feedback information for data sent by the network device, time-related information for the feedback information, first indication information for data sent by the network device, and time-related information for the first indication information; the first indication information is used to indicate whether to retransmit the data.
  • Terminal-side QoS performance for example, power consumption and/or resource consumption, etc.
  • Network-side QoS performance for example, latency and/or speed
  • the task of updating the model parameters of the first AI model used by the network device can be performed on either the network side or the terminal side.
  • the network device autonomously updates the model parameters of the first AI model.
  • the wireless communication method provided in this embodiment further includes: the network device updating the model parameters of the first AI model to new model parameters based on the QoS performance generated by the retransmission decision; wherein the QoS performance generated by the retransmission decision includes terminal-side QoS performance and/or network-side QoS performance.
  • the network device updates the model parameters of the first AI model to new model parameters based on the QoS performance generated by the retransmission decision, including: the network device determining a first difference of the first AI model based on the QoS performance generated by the retransmission decision; and the network device updating the model parameters of the first AI model to new model parameters based on the first difference.
  • the first difference could be the difference between the terminal-side QoS performance and/or the network-side QoS performance and a predefined learning target.
  • the first AI model is a reinforcement learning model.
  • the network device can determine the corresponding reward based on the terminal-side QoS performance and/or network-side QoS performance generated by the retransmission decision, as well as the predefined learning objectives (such as terminal-side QoS performance indicators and/or network-side QoS performance indicators). Based on the reward, new model parameters of the first AI model are determined.
  • model management can be set on the network side or the terminal side. Model management is aimed at: whether to replace the first AI model with the second AI model.
  • the wireless communication method provided in this embodiment further includes: the network device replacing the first AI model with the second AI model when the second condition and/or the third condition are met.
  • the second condition includes one or more of the following:
  • the network device can configure a reporting mechanism for the terminal device, which instructs the terminal device to report one or more pieces of information that meet the second condition to the network device.
  • the wireless communication method provided in this embodiment further includes: a network device sending fifth indication information to a terminal device, the fifth indication information being used to indicate a second condition.
  • the third condition includes one or more of the following:
  • the downlink data cache information on the network side exceeds or falls below the corresponding threshold
  • Embodiment 4 and its further or additional embodiments, the passive retransmission mechanism of the embodiment corresponding to FIG3 and its further or additional embodiments corresponding to FIG3 is described, wherein the first device is a network device and the second device is a terminal device.
  • Embodiments 3 and 4 both optimize the QoS performance of downlink services of the network device.
  • FIG. 7 is a schematic flowchart of the wireless communication method provided in an embodiment of this application. As shown in Figure 7, the method may include the following steps:
  • the terminal device determines the feedback information and/or the first indication information corresponding to the fourth information based on the third AI model; wherein, the feedback information is feedback information for the data sent by the network device; the first indication information is used to indicate whether to retransmit the data;
  • the terminal device sends feedback information and/or first instruction information to the network device.
  • the wireless communication method provided in this embodiment further includes: the terminal device receiving one or more of the following information sent by the network device:
  • the terminal device may obtain the above information from the network device according to per-flow, per-bearer, per-LCH, and/or per-LCG.
  • the above information may be information corresponding to per-flow, per-bearer, per-LCH, and/or per-LCG respectively.
  • the fourth information includes information specific to the terminal device, or the fourth information includes information specific to the terminal device and information from other terminal devices different from the terminal device.
  • the type of information used to determine the feedback information and/or the first indication information is the same (all are fourth information).
  • the feedback information and/or the first indication information at different times are based on the fourth information at different times.
  • the fourth information can be used as the input information of the third AI model, and the third AI model obtains the corresponding feedback information and/or the first indication information, etc., based on the fourth information.
  • the terminal device receiving one or more of the above-mentioned information sent by the network device does not limit the fourth information to including one or more of these information.
  • the fourth information includes one or more of the following:
  • QoS requirements related to the terminal side for example, downlink service pattern
  • Uplink channel measurement information for example, RSRP, RSRQ and/or SINR of uplink signals
  • Downlink channel measurement information for example, RSRP, RSRQ and/or SINR of downlink signals
  • Downlink data caching information on the network side for example, data volume information can be obtained for caching at different data layers;
  • Second information sent by the network device includes one or more of the following: feedback information for data sent to the terminal device, time-related information for the transmission of feedback information, first indication information for data sent to the terminal device, and time-related information for the transmission of the first indication information; the first indication information is used to indicate whether to retransmit the data.
  • Terminal-side QoS performance for example, power consumption and/or resource consumption, etc.
  • Network-side QoS performance for example, latency and/or speed
  • the update task of the model parameters of the third AI model used by the terminal device can be performed on either the network side or the terminal side.
  • the network device configures them to the terminal device via instruction information.
  • the wireless communication method provided in this embodiment further includes: the terminal device receiving a fifth indication information sent by the network device, the fifth indication information being used to indicate that the model parameters of the third AI model be updated to new model parameters.
  • the new model parameters are related to the QoS performance generated based on the second information.
  • the QoS performance generated based on the second information includes terminal-side QoS performance and/or network-side QoS performance generated based on the second information.
  • the terminal device sends one or more of the following information to the network device:
  • the terminal device updates the model parameters of the third AI model to new model parameters based on the QoS performance generated by the second information; wherein, the QoS performance generated by the second information includes terminal-side QoS performance and/or network-side QoS performance.
  • the terminal device updates the model parameters of the third AI model to new model parameters based on the QoS performance generated by the second information, including: the terminal device determining a second difference of the third AI model based on the QoS performance generated by the second information; and the terminal device updating the model parameters of the third AI model to new model parameters based on the second difference.
  • the second difference could be the difference between the terminal-side QoS performance and/or the network-side QoS performance and a predefined learning target.
  • the wireless communication method provided in this embodiment further includes: the terminal device receiving network-side QoS performance data generated based on second information sent by the network device. This facilitates the terminal device in determining new model parameters for the third AI model based on the network-side QoS performance generated by the second information.
  • model management can be set on the network side or the terminal side. Model management is aimed at: whether to replace the third AI model with the fourth AI model.
  • the wireless communication method provided in this embodiment further includes: the terminal device receiving a sixth indication information sent by the network device, the sixth indication information being used to indicate that the third AI model is replaced with the fourth AI model.
  • the wireless communication method provided in this embodiment further includes: the terminal device reporting one or more of the following sixth conditions to the network device:
  • the terminal device will report one or more of the information in the sixth condition to the network device, so that the network device can monitor the usage of the third AI model and replace the third AI model in a timely manner.
  • the wireless communication method provided in this embodiment further includes: the terminal device replacing the third AI model with the fourth AI model when one or more of the following sixth conditions are met:
  • the terminal device receives the seventh instruction information sent by the network device.
  • the seventh instruction information is used to indicate the sixth condition.
  • the wireless communication method provided in this embodiment further includes: the terminal device reporting model replacement-related information for the third AI model to the network device.
  • model replacement-related information includes a sixth condition that triggers the replacement of the third AI model with the fourth AI model.
  • the wireless communication method provided in the embodiments of this application has been described above. To facilitate understanding of the embodiments of this application, the following uses a UE and a network device as examples to introduce possible implementation schemes of the wireless communication method applicable to the embodiments of this application.
  • Option 1 Uplink-oriented UE-side model (see Figure 8), i.e., uplink-oriented active retransmission mechanism.
  • the model can be trained on the network side or on the terminal side.
  • the network needs to collect one or more of the following information from the UE side (per-flow, per-bearer, per-LCH, per-LCG, etc.) as input for model training:
  • Cache-related information such as BSR/DSR, and further, it can include information on the amount of cached data obtained for different data layers;
  • DL measurement information including RSRP, RSRQ, SINR and other information
  • the input for model training can be an indication based on at least one of the above-mentioned criteria.
  • the output of the model training (i.e., the retransmission decision) includes one or more of the following information:
  • the network needs to collect at least one of the following information from the UE side (per-flow, per-bearer, per-LCH, per-LCG, etc.) as a reward for model training:
  • QoS results/performance such as the QoS performance (i.e., QoS performance) obtained by the UE based on the above "MAC layer HARQ active retransmission decision, RLC layer ARQ active retransmission decision and/or PDCP duplication decision", such as latency, power consumption, resource consumption, etc., mainly refers to the QoS results on the UE side.
  • the reward for model training can be an indication based on one or more of the QoS results mentioned above.
  • the UE For model training on the UE side (including the server connected to the UE), the UE needs to collect one or more of the following information from the network side (per-flow, per-bearer, per-LCH, per-LCG, etc.) as input for model training:
  • Network-side QoS requirements related information (some of this information can be obtained from the UE itself, and this part of the information does not need to be obtained from the network side), such as UL service pattern and other information;
  • (2) UL measurement information including RSRP, RSRQ, SINR and other information.
  • the output of model training (i.e., retransmission decision) includes one or more of the following information:
  • the UE For model training on the UE side (including the server connected to the UE), the UE needs to collect at least one of the following information from the network side (per-flow, per-bearer, per-LCH, per-LCG, etc.) as a reward for model training:
  • QoS results/performance such as the QoS performance obtained by the UE based on the above "MAC layer HARQ active retransmission decision, RLC layer ARQ active retransmission decision, PDCP duplication decision", such as latency, power consumption, resource consumption, etc., mainly refers to the QoS results on the network side.
  • the reward for model training can be an indication based on one or more of the QoS results mentioned above.
  • model training can be iterated simultaneously with model inference, i.e., model training can be performed online.
  • model training is placed on the UE side, considering that the UE can obtain more information and the amount of information that needs to be transmitted is less, but it requires higher computing and storage capabilities from the UE.
  • the UE For a trained model, during the inference phase, the UE needs to collect the model's input information. In addition to the information obtainable by the UE itself, the UE also needs to obtain one or more of the following relevant information from the network side:
  • Network-side QoS requirements related information (some of this information can be obtained from the UE itself, and this part of the information does not need to be obtained from the network side), such as UL service pattern and other information;
  • the relevant information here may be specific to the UE or may include information about other UEs.
  • the relevant information here can be direct information, or information that is further determined/derived from the information mentioned above.
  • the UE uses a trained model to help it decide whether and how to perform autonomous retransmission.
  • the UE may report the results of the model inference to the network, including one or more of the following retransmission decisions (whether to retransmit, number of retransmissions, etc.):
  • the reporting methods can be:
  • Event-triggered reporting for example, when these decisions (whether to retransmit automatically, the number of retransmissions) change, or when the number of retransmissions is higher or lower than a certain threshold, a report is made.
  • this reporting allows the network to decide whether to adjust the model configuration (such as changing the model) and make decisions on passive retransmission based on the results of autonomous retransmission.
  • the network can be configured with relevant measurement reporting mechanisms to monitor model-permitted conditions, such as requiring the UE to report one or more of the following information:
  • cache-related information exceeds or falls below a certain threshold, such as BSR/DSR, further, data volume information can be obtained for caches of different data layers;
  • the QoS result exceeds or falls below a certain threshold, such as the QoS performance obtained by the UE based on the above "MAC layer HARQ active retransmission decision, RLC layer ARQ active retransmission decision, PDCP duplication decision", such as latency, power consumption, resource consumption, etc., this mainly refers to the QoS result on the UE side.
  • a certain threshold such as the QoS performance obtained by the UE based on the above "MAC layer HARQ active retransmission decision, RLC layer ARQ active retransmission decision, PDCP duplication decision", such as latency, power consumption, resource consumption, etc.
  • the network sends an instruction message to the UE.
  • model management scheme facilitates real-time network monitoring of model usage and timely adjustment of model parameters.
  • the network pre-configures the relevant triggering conditions for model configuration changes, including one or more of the following:
  • cache-related information exceeds or falls below a certain threshold, such as BSR/DSR, further, data volume information can be obtained for caches of different data layers;
  • the QoS result exceeds or falls below a certain threshold, such as the QoS performance obtained by the UE based on the above “MAC layer HARQ active retransmission decision, RLC layer ARQ active retransmission decision, PDCP duplication decision", such as latency, power consumption, resource consumption, etc.
  • a certain threshold such as the QoS performance obtained by the UE based on the above “MAC layer HARQ active retransmission decision, RLC layer ARQ active retransmission decision, PDCP duplication decision", such as latency, power consumption, resource consumption, etc. This mainly refers to the QoS result on the UE side.
  • the UE When a certain triggering condition is met, the UE reports model change information to the network.
  • the reporting includes the triggering condition for the model change.
  • model management is on the UE side allows for advance configuration of model parameter changes when network connectivity is poor, enabling the UE to adjust model parameters autonomously when relevant situations occur.
  • Option 2 Uplink-oriented network-side model (see Figure 9), i.e., uplink-oriented passive retransmission mechanism.
  • the network needs to collect one or more of the following information from the UE side (per-flow, per-bearer, per-LCH, per-LCG, etc.) as input for model training:
  • Cache-related information such as BSR/DSR, and further, data volume information can be obtained for different data layers;
  • (3) DL measurement information including RSRP, RSRQ, SINR and other information.
  • the input for model training can be an indication based on at least one of the above-mentioned criteria.
  • the network needs to collect one or more of the following information from the UE side (per-flow, per-bearer, per-LCH, per-LCG, etc.) as input information for the model:
  • the output of the model training includes one or more of the following information:
  • Send MAC HARQ retransmission schedule in advance (for example, send retransmission schedule and new transmission schedule simultaneously or consecutively, i.e., send retransmission schedule to UE before the new transmission reception result is available).
  • the network needs to collect at least one of the following information from the UE side (per-flow, per-bearer, per-LCH, per-LCG, etc.) as a reward for model training:
  • QoS results/performance such as the QoS performance (i.e., QoS performance) obtained by the UE based on the above configuration of "early sending MAC HARQ retransmission scheduling, early sending MAC HARQ-ACK information, early sending RLC ARQ SR, PDCP duplication retransmission", such as latency, power consumption, resource consumption, etc.
  • QoS performance i.e., QoS performance
  • the reward for model training can be an indication based on one or more of the QoS results mentioned above.
  • the network needs to collect model input information.
  • the network also needs to obtain one or more of the following relevant information from the user experience (UE):
  • Cache-related information such as BSR/DSR, and further, data volume information can be obtained for different data layers;
  • (3) DL measurement information including RSRP, RSRQ, SINR and other information.
  • the network uses a trained model to help it decide whether and how to perform passive retransmission.
  • the network may send the model derivation results to the UE, including at least one of the following (whether to retransmit, number of retransmissions, etc.):
  • Send MAC HARQ retransmission schedule in advance (for example, send retransmission schedule and new transmission schedule simultaneously or consecutively, i.e., send retransmission schedule to UE before the new transmission reception result is available).
  • sending a NACK in advance can help the sender retransmit quickly, while sending a fake ACK can avoid meaningless retransmissions when the latency is too high.
  • the network can be configured with relevant measurement reporting mechanisms to monitor model-permitted conditions, such as requiring the UE to report one or more of the following information:
  • cache-related information exceeds or falls below a certain threshold, such as BSR/DSR, further, data volume information can be obtained for caches of different data layers;
  • the QoS result exceeds or falls below a certain threshold, such as the QoS performance/performance obtained by the UE based on the above configuration of "early sending MAC HARQ retransmission scheduling, early sending RLC ARQ SR, PDCP duplication retransmission", such as latency, power consumption, resource consumption, etc., this mainly refers to the QoS result on the UE side.
  • a certain threshold such as the QoS performance/performance obtained by the UE based on the above configuration of "early sending MAC HARQ retransmission scheduling, early sending RLC ARQ SR, PDCP duplication retransmission", such as latency, power consumption, resource consumption, etc.
  • reporting the above information facilitates real-time network monitoring of model usage and timely adjustment of model parameters.
  • model described in Scheme 2 is either the first AI model or the second AI model, and describes the inference process, training process and model management process for the first AI model or the second AI model.
  • Option 3 Downlink-oriented network-side model (see Figure 10), i.e., downlink-oriented active retransmission mechanism.
  • the network needs to collect one or more of the following information from the UE side (per-flow, per-bearer, per-LCH, per-LCG, etc.) as input for model training:
  • QoS requirements related to the UE side (some of this information can be obtained from the CN side and does not need to be reported by the UE), such as UL service patterns and other information;
  • (2) DL measurement information including RSRP, RSRQ, SINR and other information.
  • the input for model training can be an indication based on at least one of the above-mentioned criteria.
  • the output of the model training includes one or more of the following information:
  • the network needs to collect at least one of the following information from the UE side (per-flow, per-bearer, per-LCH, per-LCG, etc.) as a reward for model training:
  • QoS results such as the QoS performance obtained by the UE based on the above "MAC layer HARQ active retransmission decision, RLC layer ARQ active retransmission decision, PDCP duplication decision", such as latency, power consumption, resource consumption, etc., mainly refer to the QoS results on the UE side.
  • the reward for model training can be an indication determined based on one or more of the QoS results mentioned above.
  • model training is done on the network side, considering that the network side currently has stronger computing power and storage than the UE side, it would be easier to do model training.
  • the network needs to collect model input information.
  • the network also needs to obtain one or more of the following relevant information from the user experience (UE):
  • QoS requirements related to the UE side (some of this information can be obtained from the CN side and does not need to be reported by the UE), such as UL service patterns and other information;
  • (2) DL measurement information including RSRP, RSRQ, SINR and other information.
  • the network may send the model derivation results to the UE, including at least one of the following (whether to retransmit, number of retransmissions, etc.):
  • this information can be reported through one or more of the following signaling methods:
  • the reporting methods can be:
  • Event-triggered indications such as when these decisions (whether to retransmit autonomously, the number of retransmissions) change, or when the number of retransmissions is higher or lower than a certain threshold.
  • this information allows the UE to use the results of autonomous retransmission as input to the dual-side model to derive the passive retransmission mode.
  • the network can be configured with relevant measurement reporting mechanisms to monitor model-permitted conditions, such as requiring the UE to report one or more of the following information:
  • the QoS result exceeds or falls below a certain threshold, such as the QoS performance obtained by the UE based on the above “MAC layer HARQ active retransmission decision, RLC layer ARQ active retransmission decision, PDCP duplication decision", such as latency, power consumption, resource consumption, etc., this mainly refers to the QoS result on the UE side.
  • a certain threshold such as the QoS performance obtained by the UE based on the above "MAC layer HARQ active retransmission decision, RLC layer ARQ active retransmission decision, PDCP duplication decision", such as latency, power consumption, resource consumption, etc.
  • reporting the above information facilitates real-time network monitoring of model usage and timely adjustment of model parameters.
  • Option 4 Downlink-oriented UE-side model (see Figure 11), i.e., downlink-oriented passive retransmission mechanism.
  • the network needs to collect one or more of the following information from the UE side (per-flow, per-bearer, per-LCH, per-LCG, etc.) as input for model training:
  • QoS requirements related to the UE side (some of this information can be obtained from the CN side and does not need to be reported by the UE), such as UL service patterns and other information;
  • (2) DL measurement information including RSRP, RSRQ, SINR and other information
  • reception status of DL data including the reception status of MAC layer HARQ, RLC layer ARQ, and PDCP layer PDU.
  • the input for model training can be an indication based on at least one of the above-mentioned criteria.
  • the output of the model training includes one or more of the following:
  • the network needs to collect at least one of the following information from the UE side (per-flow, per-bearer, per-LCH, per-LCG, etc.) as a reward for model training:
  • QoS results such as the QoS performance obtained by the UE based on the above configuration of "early sending MAC HARQ retransmission scheduling, early sending MAC HARQ-ACK information, early sending RLC ARQ SR, PDCP duplication retransmission", such as latency, power consumption, resource consumption, etc., mainly refer to the QoS results on the UE side.
  • the reward for model training can be an indication based on one or more of the QoS results mentioned above.
  • the UE For model training on the UE side (including the server connected to the UE), the UE needs to collect one or more of the following information from the network side (per-flow, per-bearer, per-LCH, per-LCG, etc.) as input for model training:
  • the data volume of the network-side DL buffer Furthermore, the data volume information can be obtained for the caches of different data layers.
  • Network-side QoS requirements related information (some of this information can be obtained from the UE itself, and this part of the information does not need to be obtained from the network side), such as UL service patterns and other information;
  • UL measurement information including RSRP, RSRQ, SINR and other information.
  • the output of the model training includes one or more of the following:
  • the UE For model training on the UE side (including the server connected to the UE), the UE needs to collect at least one of the following information from the network side (per-flow, per-bearer, per-LCH, per-LCG, etc.) as a reward for model training:
  • QoS results such as the QoS performance obtained by the UE based on the above configuration of "early sending MAC HARQ retransmission scheduling, early sending MAC HARQ-ACK information, early sending RLC ARQ SR, PDCP duplication retransmission", such as latency, power consumption, resource consumption, etc., mainly refer to the QoS results on the network side.
  • the reward for model training can be an indication based on at least one of the above-mentioned criteria.
  • model training can be iterated simultaneously with model derivation, i.e., model training can be performed online.
  • the UE For a trained model, during the inference phase, the UE needs to collect model input information. In addition to the information obtainable by the UE itself, the UE also needs to obtain one or more of the following information from the network:
  • the data volume of the network-side DL buffer Furthermore, the data volume information can be obtained for the caches of different data layers.
  • Network-side QoS requirements related information (some of this information can be obtained from the UE itself, and this part of the information does not need to be obtained from the network side), such as UL service patterns and other information;
  • UL measurement information including RSRP, RSRQ, SINR and other information.
  • the relevant information here may be specific to the UE or may include information about other UEs.
  • the relevant information here can be direct information, or information that is further determined/derived from the information mentioned above.
  • the UE may report the results of the model derivation to the network, including at least one of the following:
  • this reporting allows the network to decide whether to adjust the model configuration or make active retransmission decisions based on the results of passive retransmission.
  • the network can be configured with relevant measurement reporting mechanisms to monitor model-permitted conditions, such as requiring the UE to report one or more of the following information:
  • the QoS result exceeds or falls below a certain threshold, such as the QoS performance/performance obtained by the UE based on the above configuration of "sending MAC HARQ retransmission scheduling in advance, sending MAC HARQ feedback in advance, sending RLC ARQ SR in advance, and PDCP duplication retransmission", such as latency, power consumption, resource consumption, etc., this mainly refers to the QoS result on the UE side.
  • the network sends an instruction message to the UE.
  • the UE reports the above information to facilitate real-time network monitoring of model usage and timely adjustment of model parameters.
  • the network pre-configures the relevant triggering conditions for model configuration changes, including one or more of the following:
  • the QoS result exceeds or falls below a certain threshold, such as the QoS performance/performance obtained by the UE based on the above configuration of "sending MAC HARQ retransmission scheduling in advance, sending MAC HARQ feedback in advance, sending RLC ARQ SR in advance, and PDCP duplication retransmission", such as latency, power consumption, resource consumption, etc., this mainly refers to the QoS result on the UE side.
  • the UE When a certain triggering condition is met, the UE reports model change information to the network.
  • the reporting includes the triggering condition for the model change.
  • model management is on the UE side, which makes it easier to configure changes to model parameters in advance when the network connection is poor, and the UE can adjust the model parameters autonomously when relevant situations occur.
  • the models described in Scheme 1 and Scheme 3 are either the first AI model or the second AI model, and the inference process, training process, and model management process for the first AI model or the second AI model are described.
  • the models described in Scheme 2 and Scheme 4 are either the third AI model or the fourth AI model, and the inference process, training process, and model management process for the third AI model or the fourth AI model are described.
  • the sequence number of each process does not imply the order of execution.
  • the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
  • the terms “downlink” and “uplink” are used to indicate the transmission direction of signals or data.
  • Downlink indicates that the transmission direction of signals or data is a first direction from the site to the user equipment in the cell
  • uplink indicates that the transmission direction of signals or data is a second direction from the user equipment in the cell to the site.
  • downlink signal indicates that the transmission direction of the signal is the first direction.
  • the term "and/or” is merely a description of the association relationship between related objects, indicating that three relationships can exist.
  • a and/or B can represent: A existing alone, A and B existing simultaneously, and B existing alone.
  • the character "/" in this document generally indicates that the preceding and following related objects have an "or" relationship.
  • this application provides corresponding wireless communication devices.
  • FIG 12 is a schematic diagram of the structure of a wireless communication device provided in an embodiment of this application.
  • the wireless communication device 1200 (hereinafter referred to as device 1200) includes:
  • the first determining unit 1201 is configured to determine the retransmission decision corresponding to the first information based on the first AI model
  • the first communication unit 1202 is configured to send retransmission data to the second device when a retransmission decision is made.
  • the retransmission decision includes whether to retransmit and/or the number of retransmissions.
  • the first information includes one or more of the following:
  • Second information sent by the second device includes one or more of the following: feedback information on the data sent by the first device, time-related information on the transmission of the feedback information, first indication information on the data sent by the first device, and time-related information on the transmission of the first indication information; the first indication information is used to indicate whether to retransmit the data.
  • Third information is information determined based on one or more of the second information, QoS performance, QoS requirement information, channel measurement information and buffer information.
  • QoS requirement-related information includes terminal-side QoS requirement-related information and/or network-side QoS requirement-related information.
  • the channel measurement information includes uplink channel measurement information and/or downlink channel measurement information.
  • QoS performance includes terminal-side QoS performance and/or network-side QoS performance.
  • the first communication unit 1202 is further configured to send a retransmission decision to the second device.
  • the first communication unit 1202 is configured to send a retransmission decision to the second device via one or more of the following signaling:
  • the first communication unit 1202 is configured to periodically send retransmission decisions to the second device.
  • the first communication unit 1202 is configured to send a retransmission decision to the second device when a first condition is met.
  • the first condition includes one or more of the following:
  • the retransmission decision is different from the previous retransmission decision
  • the first determining unit 1201 is further configured to update the model parameters of the first AI model to new model parameters based on the QoS performance generated by the retransmission decision; wherein the QoS performance generated by the retransmission decision includes terminal-side QoS performance and/or network-side QoS performance.
  • the first determining unit 1201 is configured to: determine a first difference of the first AI model based on the QoS performance generated by the retransmission decision; and update the model parameters of the first AI model to new model parameters based on the first difference.
  • the first device is a terminal device and the second device is a network device.
  • the embodiments where the first device is a terminal device and the second device is a network device, specifically those involving active retransmission, will be referred to as Embodiment Five.
  • the apparatus 1200 applied to the terminal device, its first communication unit 1202, is further configured to receive one or more of the following information sent by the network device:
  • cache-related information includes uplink data cache information on the terminal side.
  • the first information includes information specific to the terminal device, or the first information includes information specific to the terminal device and information from other terminal devices different from the terminal device.
  • the first communication unit 1202 of the device 1200 applied to the terminal device is further configured to receive second indication information sent by the network device, the second indication information being used to indicate that the model parameters of the first AI model be updated to new model parameters.
  • the new model parameters are related to the QoS performance resulting from the retransmission decision; the QoS performance resulting from the retransmission decision includes the terminal-side QoS performance resulting from the retransmission decision and/or the network-side QoS performance resulting from the retransmission decision.
  • the apparatus 1200 applied to the terminal device wherein its first communication unit 1202 is further configured to receive third indication information sent by the network device, the third indication information being used to indicate that the first AI model be replaced with the second AI model.
  • the apparatus 1200 applied to the terminal device, its first communication unit 1202, is further configured to report information satisfying one or more of the following second conditions to the network device:
  • the uplink data cache information on the terminal side exceeds or falls below the corresponding threshold
  • the apparatus 1200 applied to the terminal device, its first communication unit 1202 is further configured to replace the first AI model with a second AI model when one or more of the following second conditions are met:
  • the uplink data cache information on the terminal side exceeds or falls below the corresponding threshold
  • the terminal-side QoS requirements are either higher or lower than the corresponding threshold.
  • the number of retransmissions in the retransmission decision exceeds or falls below the corresponding threshold
  • the terminal-side QoS performance exceeds or falls below the corresponding threshold
  • the QoS performance on the terminal side exceeds or falls below the corresponding threshold.
  • the apparatus 1200 applied to the terminal device wherein its first communication unit 1202 is further configured to receive fourth indication information sent by the network device, the fourth indication information being used to indicate the second condition.
  • model replacement-related information includes a second condition that triggers the replacement of the first AI model with the second AI model.
  • the first device is a network device and the second device is a terminal device.
  • the embodiments where the first device is a network device and the second device is a terminal device, specifically those involving active retransmission, will be referred to as Embodiment Six.
  • the first device is a network device and the second device is a terminal device.
  • the apparatus 1200 applied to the network device, its first communication unit 1202, is further configured to receive one or more of the following information sent by the terminal device:
  • cache-related information includes network-side downlink data cache information.
  • the apparatus 1200 applied to a network device, wherein its first communication unit 1202 is further configured to replace the first AI model with the second AI model when a second condition and/or a third condition is met.
  • the second condition includes one or more of the following:
  • the apparatus 1200 applied to the network device wherein its first communication unit 1202 is further configured to send fifth indication information to the terminal device, the fifth indication information being used to indicate the second condition.
  • the third condition includes one or more of the following:
  • the downlink data cache information on the network side exceeds or falls below the corresponding threshold
  • FIG 13 is a schematic diagram of the structure of a wireless communication device provided in an embodiment of this application, applied to a second device.
  • the wireless communication device 1300 (hereinafter referred to as device 1300) includes:
  • the second determining unit 1301 is configured to determine feedback information and/or first indication information corresponding to the fourth information based on the third AI model; wherein the feedback information is feedback information for the data transmitted by the first device; and the first indication information is used to indicate whether to retransmit the data.
  • the second communication unit 1302 is configured to send feedback information and/or first indication information to the first device.
  • the second communication unit 1302 is configured to send feedback information and/or first indication information to the first device according to transmission time-related information; wherein, the transmission time-related information includes the first transmission time of the feedback information and/or the second transmission time of the first indication information.
  • the first transmission time and/or the second transmission time both occur before the verification result of the transmitted data is obtained.
  • the first transmission time and/or the second transmission time are pre-configured.
  • the first transmission time and/or the second transmission time are determined by a third AI model using fourth information.
  • the first transmission time and/or the second transmission time satisfy one or more of the following:
  • the timer is used to trigger the sending of feedback information
  • the feedback information includes ACK information, which is information regarding data loss or receiving incorrect data.
  • the fourth information includes one or more of the following:
  • Fifth information is information determined based on one or more of the following: QoS requirement-related information, channel measurement information, buffer-related information, retransmission decision sent by the first device, QoS performance, and downlink data reception status.
  • QoS requirement-related information includes terminal-side QoS requirement-related information and/or network-side QoS requirement-related information.
  • the channel measurement information includes uplink channel measurement information and/or downlink channel measurement information.
  • QoS performance includes terminal-side QoS performance and/or network-side QoS performance.
  • the second communication unit 1302 is further configured to send second information to the first device; wherein the second information includes one or more of the following:
  • the second communication unit 1302 is configured to send second information to the first device via one or more of the following signaling:
  • the second communication unit 1302 is configured to periodically send second information to the first device.
  • the second communication unit 1302 is configured to send second information to the first device when a fourth condition is met.
  • the fourth condition includes one or more of the following:
  • the second determining unit 1301 is further configured to update the model parameters of the third AI model to new model parameters based on the QoS performance generated by the second information; wherein the QoS performance generated by the second information includes terminal-side QoS performance and/or network-side QoS performance.
  • the second determining unit 1301 is configured to: determine a second difference of the third AI model based on the QoS performance generated by the second information; and update the model parameters of the third AI model to new model parameters based on the second difference.
  • the feedback information includes MAC HARQ-ACK information and/or RLC ARQ SR; and/or, the first indication information includes MAC HARQ retransmission scheduling and/or PDCP duplication retransmission configuration.
  • the downlink data reception status includes one or more of the following reception statuses:
  • the first device is a terminal device and the second device is a network device.
  • the embodiments where the first device is a terminal device and the second device is a network device, specifically the passive retransmission embodiments, will be referred to as Embodiment Seven.
  • the second device is a network device and the first device is a terminal device.
  • the second communication unit 1302 of the apparatus 1300 applied to the network device is further configured to receive one or more of the following information sent by the terminal device:
  • the second determining unit 1301 of the apparatus 1300 applied to the network device is further configured to replace the third AI model with the fourth AI model if the second condition and/or the fifth condition are met.
  • the second condition includes one or more of the following:
  • the uplink data cache information on the terminal side exceeds or falls below the corresponding threshold
  • the second communication unit 1302 of the apparatus 1300 applied to the network device is further configured to send fourth indication information to the terminal device, the fourth indication information being used to indicate relevant information for reporting the second condition.
  • the fifth condition includes one or more of the following:
  • Uplink channel measurement information exceeds or falls below the corresponding threshold
  • the first device is a network device and the second device is a terminal device.
  • the embodiments where the first device is a network device and the second device is a terminal device, specifically the passive retransmission embodiments, will be referred to as Embodiment Eight.
  • the second device is a terminal device and the first device is a network device.
  • the second communication unit 1302 of the apparatus 1300 applied to the terminal device is further configured to receive one or more of the following information sent by the network device:
  • the fourth information includes information specific to the terminal device, or the fourth information includes information specific to the terminal device and information from other terminal devices different from the terminal device.
  • the second communication unit 1302 of the device 1300 applied to the terminal device is further configured to receive fifth indication information sent by the network device, the fifth indication information being used to indicate that the model parameters of the third AI model be updated to new model parameters.
  • the new model parameters are related to the QoS performance generated based on the second information.
  • the QoS performance generated based on the second information includes the terminal-side QoS performance generated based on the second information and/or the network-side QoS performance generated based on the second information.
  • the second communication unit 1302 of the apparatus 1300 applied to the terminal device is further configured to send one or more of the following information to the network device:
  • the second communication unit 1302 of the device 1300 applied to the terminal device is further configured to receive a sixth instruction information sent by the network device, the sixth instruction information being used to instruct the third AI model to be replaced with a fourth AI model.
  • the second communication unit 1302 of the apparatus 1300 applied to the terminal device is further configured to report information satisfying one or more of the following sixth conditions to the network device:
  • the second determining unit 1301 of the apparatus 1300 applied to the terminal device is further configured to replace the third AI model with the fourth AI model if one or more of the following sixth conditions are met:
  • the second communication unit 1302 of the device 1300 applied to the terminal device is further configured to receive seventh indication information sent by the network device, the seventh indication information being used to indicate a sixth condition.
  • the second communication unit 1302 of the device 1300 applied to the terminal device is further configured to report model replacement information for the third AI model to the network device.
  • model replacement information includes a sixth condition that triggers the replacement of the third AI model with the fourth AI model.
  • Figure 14 is a schematic structural diagram of a communication device 1400 provided in an embodiment of this application.
  • This communication device can be a terminal device or a network device.
  • the communication device 1400 shown in Figure 14 includes a processor 1410, which can call and run computer programs from memory to implement the methods in the embodiments of this application.
  • the communication device 1400 may further include a memory 1420.
  • the processor 1410 may retrieve and run computer programs from the memory 1420 to implement the methods described in the embodiments of this application.
  • the memory 1420 can be a separate device independent of the processor 1410, or it can be integrated into the processor 1410.
  • the communication device 1400 may further include a transceiver 1430, and the processor 1410 may control the transceiver 1430 to communicate with other devices. Specifically, it may send information or data to other devices or receive information or data sent by other devices.
  • the transceiver 1430 may include a transmitter and a receiver.
  • the transceiver 1430 may further include an antenna, and the number of antennas may be one or more.
  • the communication device 1400 may specifically be a network device in the embodiments of this application, and the communication device 1400 may implement the corresponding processes implemented by the network device in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.
  • the communication device 1400 may specifically be a terminal device in the embodiments of this application, and the communication device 1400 may implement the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.
  • Figure 15 is a schematic structural diagram of a chip according to an embodiment of this application.
  • the chip 1500 shown in Figure 15 includes a processor 1510, which can call and run computer programs from memory to implement the methods in the embodiments of this application.
  • chip 1500 may further include memory 1520.
  • Processor 1510 may retrieve and run computer programs from memory 1520 to implement the methods in the embodiments of this application.
  • the memory 1520 can be a separate device independent of the processor 1510, or it can be integrated into the processor 1510.
  • the chip 1500 may also include an input interface 1530.
  • the processor 1510 can control the input interface 1530 to communicate with other devices or chips; specifically, it can acquire information or data sent by other devices or chips.
  • the chip 1500 may also include an output interface 1540.
  • the processor 1510 can control the output interface 1540 to communicate with other devices or chips, specifically, to output information or data to other devices or chips.
  • the chip can be applied to the network device in the embodiments of this application, and the chip can implement the corresponding processes implemented by the network device in the various methods of the embodiments of this application.
  • the chip can implement the corresponding processes implemented by the network device in the various methods of the embodiments of this application.
  • the chip can implement the corresponding processes implemented by the network device in the various methods of the embodiments of this application.
  • the chip can be applied to the terminal device in the embodiments of this application, and the chip can implement the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application.
  • the chip can implement the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application.
  • the chip can implement the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application.
  • chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.
  • This application also provides a computer storage medium storing one or more programs, which can be executed by one or more processors to implement the methods in this application.
  • Figure 16 is a schematic block diagram of a communication system 1600 provided in an embodiment of this application. As shown in Figure 16, the communication system 1600 includes a terminal device 1610 and a network device 1620.
  • the terminal device 1610 can be used to implement the corresponding functions implemented by the terminal device in the above method
  • the network device 1620 can be used to implement the corresponding functions implemented by the network device in the above method. For the sake of brevity, they will not be described in detail here.
  • the processor in the embodiments of this application may be an integrated circuit chip with signal processing capabilities.
  • the steps of the above method embodiments can be completed by integrated logic circuits in the processor's hardware or by instructions in software form.
  • the processor described above may be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application.
  • the general-purpose processor may be a microprocessor or any conventional processor.
  • the steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor.
  • the software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art.
  • the storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.
  • Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.
  • Volatile memory can be random access memory (RAM), which serves as an external cache.
  • RAM random access memory
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • SDRAM synchronous dynamic random access memory
  • DDR SDRAM double data rate synchronous dynamic random access memory
  • ESDRAM enhanced synchronous dynamic random access memory
  • ESDRAM Synchronous Link Dynamic Random Access Memory
  • DR RAM Direct Rambus RAM
  • the memory in the embodiments of this application may also be static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DR RAM), etc. That is to say, the memory in the embodiments of this application is intended to include, but is not limited to, these and any other suitable types of memory.
  • SRAM static random access memory
  • DRAM dynamic random access memory
  • SDRAM synchronous dynamic random access memory
  • DDR SDRAM double data rate synchronous dynamic random access memory
  • ESDRAM enhanced synchronous dynamic random access memory
  • SLDRAM synchronous link dynamic random access memory
  • DR RAM direct memory bus RAM
  • This application also provides a computer-readable storage medium for storing computer programs.
  • the computer-readable storage medium can be applied to the network device in the embodiments of this application, and the computer program causes the computer to execute the corresponding processes implemented by the network device in the various methods of the embodiments of this application.
  • the computer program causes the computer to execute the corresponding processes implemented by the network device in the various methods of the embodiments of this application.
  • the computer program causes the computer to execute the corresponding processes implemented by the network device in the various methods of the embodiments of this application.
  • the computer-readable storage medium can be applied to the terminal device in the embodiments of this application, and the computer program causes the computer to execute the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application.
  • the computer program causes the computer to execute the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application.
  • the computer program causes the computer to execute the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application.
  • This application also provides a computer program product, including computer program instructions.
  • the computer program product can be applied to the network device in the embodiments of this application, and the computer program instructions cause the computer to execute the corresponding processes implemented by the network device in the various methods of the embodiments of this application.
  • the computer program instructions cause the computer to execute the corresponding processes implemented by the network device in the various methods of the embodiments of this application.
  • the network device in the embodiments of this application.
  • the computer program product can be applied to the terminal device in the embodiments of this application, and the computer program instructions cause the computer to execute the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application.
  • the computer program instructions cause the computer to execute the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application.
  • the terminal device in the embodiments of this application
  • the computer program instructions cause the computer to execute the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application.
  • This application also provides a computer program.
  • the computer program can be applied to the network device in the embodiments of this application.
  • the computer program When the computer program is run on the computer, it causes the computer to execute the corresponding processes implemented by the network device in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.
  • the computer program can be applied to the terminal device in the embodiments of this application.
  • the computer program When the computer program is run on the computer, it causes the computer to execute the corresponding processes implemented by the terminal device in the various methods of the embodiments of this application. For the sake of brevity, it will not be described in detail here.
  • the disclosed systems, apparatuses, and methods can be implemented in other ways.
  • the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods.
  • multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.
  • the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
  • the units described as separate components may or may not be physically separate.
  • the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
  • the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
  • the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.
  • This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.
  • the aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

Un mode de réalisation de la présente demande concerne un procédé de communication sans fil. Le procédé comprend les étapes suivantes : sur la base d'un premier modèle d'IA, un premier dispositif détermine une décision de retransmission correspondant à des premières informations ; et lorsque la décision de retransmission se traduit par la réalisation d'une retransmission, le premier dispositif envoie des données retransmises à un second dispositif.
PCT/CN2024/105501 2024-07-15 2024-07-15 Procédés et appareils de communication sans fil, et dispositif, puce et support de stockage Pending WO2026016000A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/CN2024/105501 WO2026016000A1 (fr) 2024-07-15 2024-07-15 Procédés et appareils de communication sans fil, et dispositif, puce et support de stockage

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2024/105501 WO2026016000A1 (fr) 2024-07-15 2024-07-15 Procédés et appareils de communication sans fil, et dispositif, puce et support de stockage

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WO2026016000A1 true WO2026016000A1 (fr) 2026-01-22

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220337346A1 (en) * 2020-06-09 2022-10-20 Samsung Electronics Co., Ltd. Method and apparatus for retransmitting packets in dual connectivity network
WO2023159426A1 (fr) * 2022-02-24 2023-08-31 Huawei Technologies Co., Ltd. Procédés et appareil d'échange adaptatif de paramètres d'intelligence artificielle/apprentissage automatique (ai/ml)
US20240039659A1 (en) * 2021-02-11 2024-02-01 Qualcomm Incorporated Machine learning assisted predictive retransmission feedback
WO2024097693A1 (fr) * 2022-11-04 2024-05-10 Google Llc Gestion de rapport d'informations d'état de canal basé sur l'apprentissage automatique au niveau d'un réseau

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20220337346A1 (en) * 2020-06-09 2022-10-20 Samsung Electronics Co., Ltd. Method and apparatus for retransmitting packets in dual connectivity network
US20240039659A1 (en) * 2021-02-11 2024-02-01 Qualcomm Incorporated Machine learning assisted predictive retransmission feedback
WO2023159426A1 (fr) * 2022-02-24 2023-08-31 Huawei Technologies Co., Ltd. Procédés et appareil d'échange adaptatif de paramètres d'intelligence artificielle/apprentissage automatique (ai/ml)
WO2024097693A1 (fr) * 2022-11-04 2024-05-10 Google Llc Gestion de rapport d'informations d'état de canal basé sur l'apprentissage automatique au niveau d'un réseau

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