WO2026012229A1 - Communication method and communication apparatus - Google Patents

Abstract

Provided in the present application are a communication method and a communication apparatus. The method comprises: receiving first identification information of a first entity, the first identification information comprising first indication information corresponding to each of at least one feature of the first entity, and the at least one feature of the first entity comprising a first feature of the first entity; on the basis of the first identification information and second identification information of a second entity, determining that the first feature of the first entity is different from a first feature of the second entity, the second identification information comprising second indication information corresponding to each of at least one feature of the second entity, and the at least one feature of the second entity comprising the first feature of the second entity; and sending first information, the first information being used for determining the first feature of the second entity, wherein the second entity is determined by the first feature and the first feature of the second entity or the second entity is determined by the first feature of the second entity. The present application helps to save air interface overhead.

Description

Communication methods and communication devices

This application claims priority to Chinese Patent Application No. 202410918816.6, filed on July 9, 2024, entitled "Communication Method and Communication Device", the entire contents of which are incorporated herein by reference.

Technical Field

This application relates to the field of communications, and more specifically, to a communication method and a communication device.

Background Technology

Currently, artificial intelligence (AI) has been introduced into wireless communication networks and has been widely applied in many application scenarios of air interface technology, such as AI-based channel state information (CSI) prediction, AI-based beam management, and AI-based CSI feedback, playing an increasingly important role.

In some applications of AI models using air interface technology, the training and inference of the AI model may be deployed on different network elements. To ensure the performance of the AI model, the network element training the AI model may need to periodically update the AI model and send the updated AI model to the network element performing AI model inference, resulting in significant air interface overhead.

Summary of the Invention

This application provides a communication method and a communication device, which is advantageous in that when two entities are identified to have at least one different feature, the first device only sends information about the at least one different feature, thereby saving air interface overhead.

Firstly, a communication method is provided, which can be executed by a first device, which can be a device on a second entity side, or a chip or circuit of the device on the second entity side. The device on the second entity side can be replaced by a device on a terminal device side or a device on a network device side. The terminal device side can include at least one of a terminal device or an AI entity on the terminal device side. The AI entity on the terminal device side can be the terminal device itself or an AI entity serving the terminal device, such as a server, such as an over-the-top (OTT) server or a cloud server. The network device side can include at least one of a network device or an AI entity on the network device side. The AI entity on the network device side can be the network device itself or an AI entity serving the network device, such as a radio access network (RAN) intelligent controller (RIC), operation administration and maintenance (OAM), or a server, such as an OTT server or a cloud server.

The method includes: receiving first identification information of a first entity, the first identification information including first indication information corresponding to each of at least one feature of the first entity, the at least one feature of the first entity including a first feature of the first entity; determining, based on the first identification information and second identification information of a second entity, that the first feature of the first entity is different from the first feature of the second entity; the second identification information including second indication information corresponding to each of at least one feature of the second entity, the at least one feature of the second entity including the first feature of the second entity; and sending first information for determining the first feature of the second entity, wherein the second entity is determined by the first feature of the second entity and the first feature, or the second entity is determined by the first feature of the second entity.

Based on the above technical solution, the first device can determine whether at least one feature of the first entity is the same as at least one feature of the second entity through the first identification information and the second identification information. This is beneficial because when it is determined that the first feature of the first entity and the second feature of the second entity are different, the first device can send the first information for determining the first feature of the second entity to the second device, instead of sending the information for determining all features of the second entity to the second device, or in other words, instead of sending the information for determining the second entity to the second device, thereby saving air interface overhead.

The determination that the first feature of the first entity is different from the first feature of the second entity based on the first identification information and the second identification information includes: if the first indication information corresponding to the first feature of the first entity included in the first identification information is different from the second indication information corresponding to the first feature of the second entity included in the second identification information, then it is determined that the first feature of the first entity is different from the first feature of the second entity.

In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: sending second indication information corresponding to the first feature of the second entity.

Based on the above technical solution, the second device can determine the second identification information of the second entity. For example, by replacing the first feature of the first entity with the second indication information corresponding to the first feature of the second entity in the first identification information of the first entity, the second device can determine the second identification information of the second entity.

Optionally, if the first feature is the highest-ranking feature among at least one feature of the second entity, then the first information is used to determine the second entity.

Optionally, if the first information is used to identify the second entity, the first device may also send second identification information.

Optionally, if the first feature of the second entity is not the highest-ranking feature among at least one feature of the second entity, and at least one feature of the second entity also includes features of a lower rank than the first feature of the second entity, then the first information is used to determine the first feature of the second entity, and is also used to determine features of a lower rank than the first feature of the second entity among at least one feature of the second entity.

Optionally, the first device further transmits second indication information corresponding to the first feature of the second entity, and also transmits second indication information corresponding to features of the second entity that are of a lower level than the first feature.

In conjunction with the first aspect, in some implementations of the first aspect, the feature corresponding to the first indication information is a first-class feature, and the feature corresponding to the second indication information is a first-class feature; the first information is used to determine the feature of the second entity that is lower in level than the first-class feature of the second entity among the first features of the second entity and the features of the second entity.

Optionally, the first device may also send second indication information corresponding to the first feature of the second entity, and further send indication information corresponding to features of the second entity that are of a lower level than the first feature.

In conjunction with the first aspect, in some implementations of the first aspect, the feature corresponding to the first indication information is a first type of feature, and the feature corresponding to the second indication information is a first type of feature; before receiving the first identification information of the first entity, the method further includes: receiving the third identification information of the first entity, the third identification information including third indication information corresponding to each of the at least one second type of feature of the first entity, wherein the level of the second type of feature is higher than the level of the first type of feature; the third identification information is the same as the fourth identification information of the second entity, the fourth identification information including fourth indication information corresponding to each of the at least one second type of feature of the second entity; and sending a request message for requesting the first identification information of the first entity.

Based on the above technical solution, by classifying different features of an entity, multiple identification information of the entity can be defined, and each of the multiple identification information corresponds to a different level of feature of the entity. Then, the first device can first compare the third identification information of the first entity with the fourth identification information of the second entity. If the third and fourth identification information determine that the highest-level feature (i.e., the second type of feature) of the first and second entities is the same, then it requests the first identification information, thereby helping to avoid the second device reporting multiple identification information of the first entity at once.

In conjunction with the first aspect, in some implementations of the first aspect, the first indication information corresponding to each feature of at least one feature of the first entity is arranged in a first order, and the second indication information corresponding to each feature of at least one feature of the second entity is arranged in a first order; or, the first identification information further includes information related to the name of each feature of at least one feature of the first entity, and the second identification information further includes information related to the name of each feature of at least one feature of the second entity.

Based on the above technical solution, the indication information corresponding to each feature in at least one feature is arranged in a first order, which facilitates the first device and the second device in determining the feature corresponding to each indication information in the identification information. Alternatively, the identification information also includes information related to the name of the feature, which facilitates the first device and the second device in determining the feature corresponding to each indication information in the identification information based on the information related to the name of the feature.

In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: sending fifth indication information; wherein, if the first indication information corresponding to each feature of at least one feature of the first entity is arranged in a first order, the fifth indication information is used to indicate that the first identification information includes the first indication information corresponding to each feature of at least one feature of the first entity, and the first indication information corresponding to each feature of at least one feature of the first entity is arranged in a first order; or, if the first identification information also includes information related to the name of each feature of at least one feature of the first entity, the fifth indication information is used to indicate that the first identification information includes the first indication information corresponding to each feature of at least one feature of the first entity, and information related to the name of each feature of at least one feature of the first entity.

In conjunction with the first aspect, in some implementations of the first aspect, the first entity and the second entity are models, then the at least one feature includes one or more of the following: the structure of the entity, the parameters related to the entity, the data related to the entity, the dataset related to the entity, and the scenarios in which the entity is applicable.

For example, the level of an entity’s structure and/or the scenario to which the entity applies is higher than one or more of the following: entity-related parameters, entity-related data, or entity-related datasets; and/or, the level of entity-related parameters is higher than one or more of the following: entity-related data or entity-related datasets.

In conjunction with the first aspect, in some implementations of the first aspect, where the first entity and the second entity are data or a dataset, the at least one feature includes one or more of the following: source (e.g., from the transmitter or receiver, or from simulation or testing), function (e.g., for training, for testing, or unable to be sent to the peer), data content (e.g., bit information (including bit information with a certain distribution), symbol information (including symbol data corresponding to different constellation types)), data size (e.g., demodulated hard/soft bits, or decoded bits), data type (e.g., the data is an estimate or a true value), or related channel information (e.g., received signal, equalized signal after channel estimation, average channel delay, or signal noise ratio (SNR)).

For example, the level of the source and/or functionality is higher than one or more of the following: data content, data size, data type, or associated channel information, and/or the level of the data type and/or data content is higher than one or more of the following: data size, or associated channel information.

In conjunction with the first aspect, in some implementations of the first aspect, the first entity and the second entity are a scene, then the at least one feature includes one or more of the following: scene-related location, scene-related time, scene-related scatterer richness, or scene-related line of sight (LOS)/non-line of sight (NLOS) ratio, etc.

For example, the level of scene-related location is higher than one or more of the following: scene-related time, scene-related scatterer richness, or scene-related LOS/NLOS ratio; and/or, the level of scene-related time is higher than one or more of the following: scene-related scatterer richness, or scene-related LOS/NLOS ratio.

In conjunction with the first aspect, in some implementations of the first aspect, the method further includes: sending a sixth indication message, the sixth indication message being used to indicate the level corresponding to each of the at least one feature.

Secondly, a communication method is provided, which can be executed by a second device. The second device can be a device on the first entity side, or a chip or circuit of the device on the first entity side. The device on the first entity side can be replaced by a device on the terminal device side or a device on the network device side. The terminal device side can include at least one of a terminal device or an AI entity on the terminal device side. The AI entity on the terminal device side can be the terminal device itself or an AI entity serving the terminal device, such as a server, like an OTT server or a cloud server. The network device side can include at least one of a network device or an AI entity on the network device side. The AI entity on the network device side can be the network device itself or an AI entity serving the network device, such as a RIC, OAM, or a server, like an OTT server or a cloud server.

The method includes: sending first identification information of a first entity, the first identification information including first indication information corresponding to each of at least one feature of the first entity, the at least one feature of the first entity including a first feature of the first entity; receiving first information for determining a first feature of a second entity, the first feature of the first entity being different from the first feature of the second entity; the second entity being determined by the first feature of the second entity and the first feature, or the second entity being determined by the first feature of the second entity.

The beneficial effects in the second aspect can be referred to the description in the first aspect above.

In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: receiving second indication information corresponding to the first feature of the second entity.

Optionally, if the first feature is the highest-ranking feature among at least one feature of the second entity, then the first information is used to determine the second entity.

Optionally, if the first information is used to identify the second entity, the second device may also receive second identification information.

Optionally, if the first feature of the second entity is not the highest-ranking feature among at least one feature of the second entity, and at least one feature of the second entity also includes features of a lower rank than the first feature of the second entity, then the first information is used to determine the first feature of the second entity, and is also used to determine features of a lower rank than the first feature of the second entity among at least one feature of the second entity.

Optionally, the second device also receives second indication information corresponding to the first feature of the second entity, and also receives second indication information corresponding to features of the second entity that are of a lower level than the first feature.

In conjunction with the second aspect, in some implementations of the second aspect, the feature corresponding to the first indication information is a first type of feature, and the first information is used to determine the feature of the second entity that is of a lower level than the first type of feature of the second entity.

Optionally, the second device also receives second indication information corresponding to the first feature of the second entity, and also receives indication information corresponding to features of the second entity that are of a lower level than the first feature.

In conjunction with the second aspect, in some implementations of the second aspect, the first indication information corresponding to each feature of at least one feature of the first entity is arranged in a first order; or, the first identification information further includes the name information of each feature of at least one feature of the first entity.

In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: receiving fifth indication information; wherein, if the first indication information corresponding to each feature of at least one feature of the first entity is arranged in a first order, the fifth indication information is used to indicate that the first identification information includes the first indication information corresponding to each feature of at least one feature of the first entity, and the first indication information corresponding to each feature of at least one feature of the first entity is arranged in a first order; or, if the first identification information also includes information related to the name of each feature of at least one feature of the first entity, the fifth indication information is used to indicate that the first identification information includes the first indication information corresponding to each feature of at least one feature of the first entity, and information related to the name of each feature of at least one feature of the first entity.

In conjunction with the second aspect, in some implementations of the second aspect, the first entity and the second entity are models, and the at least one feature includes one or more of the following: the structure of the entity, the parameters related to the entity, the data related to the entity, the dataset related to the entity, and the scenarios in which the entity is applicable.

For example, the level of an entity’s structure and/or the scenario to which the entity applies is higher than one or more of the following: entity-related parameters, entity-related data, or entity-related datasets; and/or, the level of entity-related parameters is higher than one or more of the following: entity-related data or entity-related datasets.

In conjunction with the second aspect, in some implementations of the second aspect, where the first entity and the second entity are data or a dataset, the at least one feature includes one or more of the following: source (e.g., from the transmitter or receiver, or from simulation or testing), function (e.g., for training, for testing, or unable to be sent to the peer), data content (e.g., bit information (including bit information with a certain distribution), symbol information (including symbol data corresponding to different constellation types)), data size (e.g., demodulated hard/soft bits, or decoded bits), data type (e.g., the data is an estimate or a true value), or related channel information (e.g., received signal, equalized signal after channel estimation, average channel delay, or SNR).

For example, the level of the source and/or functionality is higher than one or more of the following: data content, data size, data type, or associated channel information, and/or the level of the data type and/or data content is higher than one or more of the following: data size, or associated channel information.

In conjunction with the second aspect, in some implementations of the second aspect, the first entity and the second entity are a scene, and the at least one feature includes one or more of the following: scene-related location, scene-related time, scene-related scatterer richness, or scene-related LOS/NLOS ratio, etc.

For example, the level of scene-related location is higher than one or more of the following: scene-related time, scene-related scatterer richness, or scene-related LOS/NLOS ratio; and/or, the level of scene-related time is higher than one or more of the following: scene-related scatterer richness, or scene-related LOS/NLOS ratio.

In conjunction with the second aspect, in some implementations of the second aspect, the method further includes: receiving sixth indication information, the sixth indication information being used to indicate the level corresponding to each of the at least one feature.

Thirdly, a communication device is provided, which may be the first device, or a device or module for performing the functions of the first device.

One possible implementation is that the communication device may include modules or units corresponding to the methods/operations/steps/actions described in the first aspect, which may be hardware circuits, software, or a combination of hardware circuits and software.

Fourthly, a communication device is provided, which may be a second device, or a device or module for performing the functions of the second device.

One possible implementation is that the communication device may include modules or units corresponding to the methods/operations/steps/actions described in the second aspect, which may be hardware circuits, software, or a combination of hardware circuits and software.

Fifthly, a communication apparatus is provided, comprising: at least one processor for executing a computer program or instructions to perform the methods of the first aspect and any of the possible implementations thereof, or to perform the methods of the second aspect and any of the possible implementations thereof. Optionally, the apparatus further comprises a memory for storing the computer program or instructions. Optionally, the apparatus further comprises a communication interface through which the processor reads the computer program or instructions.

In one implementation, the device is a communication device (such as a terminal device or a network device).

In another implementation, the device is a chip, chip system, or circuit for communication equipment (such as terminal equipment or network equipment).

In a sixth aspect, a processor is provided for executing the method provided in the first aspect, or for executing the method provided in the second aspect.

Unless otherwise specified, or if it does not contradict its actual function or internal logic in the relevant description, the transmission and acquisition/reception operations involved in the processor can be understood as processor output and reception, input and other operations, or as transmission and reception operations performed by radio frequency circuits and antennas. This application does not limit them in this regard.

Optionally, the device further includes: a memory for storing a program; correspondingly, at least one processor for executing the computer program or instructions in the memory.

Optionally, the device also includes a communication interface. The communication interface is coupled to the processor and can be used to input information to the processor or output information from the processor.

A seventh aspect provides a computer-readable storage medium storing program code for execution by a device, the program code including methods for performing the first aspect and any of the possible implementations of the first aspect, or the program code including methods for performing the second aspect and any of the possible implementations of the second aspect.

Eighthly, a computer program product comprising instructions is provided, which, when run on a computer, causes the computer to perform the methods of the first aspect and any of the possible implementations thereof, or causes the computer to perform the methods of the second aspect and any of the possible implementations thereof.

Ninth aspect, a chip is provided, the chip including a processor and a communication interface, the processor reading instructions from a memory through the communication interface to execute the method provided by the first aspect and any of the above-described implementations of the first aspect, or to execute the method provided by the second aspect and any of the above-described implementations of the second aspect.

Optionally, as one implementation, the chip further includes a memory storing computer programs or instructions. The processor is used to execute the computer programs or instructions in the memory. When the computer programs or instructions are executed, the processor is used to execute the method provided by the first aspect and any of the above-described implementations of the first aspect, or the processor is used to execute the method provided by the second aspect and any of the above-described implementations of the second aspect.

In a tenth aspect, a communication system is provided, comprising a first device and/or a second device, wherein the first device is configured to implement the method provided by the first aspect and any possible implementation thereof, and the second device is configured to implement the method provided by the second aspect and any possible implementation thereof.

It should be understood that the beneficial effects of aspects two through ten and any of their implementations can be referenced in aspect one and any of its implementations.

Attached Figure Description

Figure 1 is a schematic diagram of a possible application framework in a communication system.

Figure 2 is a schematic diagram of a possible application framework in a communication system.

Figure 3 is a schematic diagram of a communication system applicable to the communication method of this application embodiment.

Figure 4 is a schematic diagram of a communication system applicable to the communication method of this application embodiment.

Figure 5 is a schematic diagram of the neuron structure.

Figure 6 is a schematic diagram of a communication method 600 provided in an embodiment of this application.

Figure 7 is a schematic diagram of a communication method 700 provided in an embodiment of this application.

Figure 8 is a schematic diagram of a communication method 800 provided in an embodiment of this application.

Figure 9 is a schematic diagram of a communication method 900 provided in an embodiment of this application.

Figure 10 is a schematic diagram of a communication device 2000 provided in an embodiment of this application.

Figure 11 is a schematic diagram of another communication device 3000 provided in an embodiment of this application.

Detailed Implementation

The technical solutions in this application will now be described with reference to the accompanying drawings.

The technical solutions provided in this application can be applied to various communication systems, such as: 5th generation (5G) or new radio (NR) systems, long term evolution (LTE) systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, wireless local area network (WLAN) systems, satellite communication systems, future communication systems, or integrated systems of multiple systems. The technical solutions provided in this application can also be applied to device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine (M2M) communication, machine-type communication (MTC), and Internet of Things (IoT) communication systems or other communication systems.

In a communication system, a device can send signals to or receive signals from another device. These signals can include information, signaling, or data. The term "device" can also be replaced by an entity, network entity, communication device, mobile device, network element, communication module, node, communication node, communication apparatus, etc. This disclosure uses "device" as an example. For instance, a communication system can include at least one terminal device and at least one network device. The network device can send downlink signals to the terminal device, and/or the terminal device can send uplink signals to the network device.

In the embodiments of this application, the terminal device may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent, or user device.

Terminal devices can be devices that provide voice/data, such as handheld devices with wireless connectivity, in-vehicle devices, etc. Currently, some examples of terminals include: mobile phones, tablets, laptops, PDAs, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, and so on. The embodiments of this application do not limit the scope to wireless terminals in smart cities, smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, 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, wearable devices, terminal devices in 5G networks, or terminal devices in future evolved public land mobile networks (PLMNs).

By way of example and not limitation, in this embodiment, the terminal device can also be a wearable device. Wearable devices, also known as wearable smart devices, are a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, and shoes. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not merely hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are feature-rich, large in size, and can achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those that focus on a specific type of application function and require the use of other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

In this embodiment, the device for implementing the functions of the terminal device can be the terminal device itself, or it can be any device capable of supporting the terminal device in implementing those functions, such as a chip system. This device can be installed in or used in conjunction with the terminal device. In this embodiment, the chip system can be composed of chips or may include chips and other discrete components. This embodiment only uses the terminal device as an example to illustrate the device for implementing the functions of the terminal device, and does not constitute a limitation on the solution of this embodiment.

The network device in this application embodiment may include a device for communicating with a terminal device. For example, the network device may include an access network device or a wireless access network device, such as a base station. The wireless access network device in this application embodiment may refer to a radio access network (RAN) node (or device) that connects the terminal device to the wireless network. A base station can broadly encompass various names listed below, or be replaced by names such as: NodeB, evolved NodeB (eNB), next-generation NodeB (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), master station, auxiliary station, motor slide retainer (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), radio unit (RU), positioning node, etc. A base station can be a macro base station, micro base station, relay node, donor node, or a combination thereof. A base station can also refer to a communication module, modem, or chip installed within the aforementioned equipment or apparatus. A base station can also be a mobile switching center, equipment performing base station functions in D2D, V2X, and M2M communications, or equipment performing base station functions in future communication systems. A base station can support networks using the same or different access technologies. Optionally, a RAN node can also be a server, wearable device, vehicle, or in-vehicle equipment. For example, the access network equipment in vehicle-to-everything (V2X) technology can be a roadside unit (RSU). The embodiments of this application do not limit the specific technologies or equipment forms used in the network equipment.

Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move depending on the location of the mobile base station. In other examples, a helicopter or drone can be configured as a device to communicate with another base station.

In some deployments, the network devices mentioned in the embodiments of this application may be devices including CU, DU, or CU and DU, or devices with control plane CU nodes (central unit-control plane (CU-CP)) and user plane CU nodes (central unit-user plane (CU-UP)) and DU nodes. For example, the network devices may include gNB-CU-CP, gNB-CU-UP, and gNB-DU.

In some deployments, multiple RAN nodes collaborate to assist terminals in achieving wireless access, with different RAN nodes each implementing some of the base station's functions. For example, RAN nodes can be CUs, DUs, CU-CPs, CU-UPs, or RUs. CUs and DUs can be configured separately or included in the same network element, such as a BBU. RUs can be included in radio frequency equipment or radio frequency units, such as RRUs, AAUs, or RRHs.

RAN nodes can support one or more types of fronthaul interfaces, each corresponding to a DU and RU with different functions. If the fronthaul interface between the DU and RU is a common public radio interface (CPRI), the DU is configured to implement one or more baseband functions, and the RU is configured to implement one or more radio frequency functions. If the fronthaul interface between the DU and RU is another type of interface, relative to CPRI, some downlink and/or uplink baseband functions, such as, for downlink, precoding, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT)/cyclic prefix addition (CP), are moved from the DU to the RU; and for uplink, one or more of digital beamforming (BF), or fast Fourier transform (FFT)/cyclic prefix removal (CP), are moved from the DU to the RU. In one possible implementation, the interface can be an enhanced common public radio interface (eCPRI). Under the eCPRI architecture, the segmentation between DU and RU differs, corresponding to different categories (Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, F.

Taking eCPRI Cat A as an example, for downlink transmission, the DU is configured to implement one or more functions before and after the layer mapping (i.e., coding, rate matching, scrambling, modulation, layer mapping), while other functions after the layer mapping (e.g., RE mapping, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT)/adding a cyclic prefix (CP)) are moved to the RU for implementation. For uplink transmission, the de-RE mapping is used as the dividing line. The DU is configured to implement one or more functions preceding de-mapping (i.e., decoding, rate matching de-matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, and one or more functions in de-RE mapping). Other functions following de-mapping (e.g., one or more functions in digital BF or fast Fourier transform (FFT)/de-CP) are moved to the RU. It is understood that descriptions of the functions of the DU and RU corresponding to various types of eCPRI can be found in the eCPRI protocol and will not be elaborated upon here.

In one possible design, the processing unit in the BBU used to implement baseband functions is called the baseband high (BBH) unit, and the processing unit in the RRU/AAU/RRH used to implement baseband functions is called the baseband low (BBL) unit.

In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an open RAN (ORAN) system, CU can also be called O-CU (open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.

In this embodiment, the apparatus for implementing the functions of a network device can be a network device itself; it can also be an apparatus capable of supporting the network device in implementing those functions, such as a chip system, hardware circuit, software module, or a hardware circuit plus a software module. This apparatus can be installed in the network device or used in conjunction with the network device. In this embodiment, the example of a network device being used to implement the functions of a network device is provided only and does not constitute a limitation on the solutions described in this embodiment.

Network devices and/or terminal devices can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; they can also be deployed on water; and they can also be deployed in the air on airplanes, balloons, and satellites. This application does not limit the scenario in which the network devices and terminal devices are located. Furthermore, terminal devices and network devices can be hardware devices, or software functions running on dedicated hardware or general-purpose hardware, such as virtualization functions instantiated on a platform (e.g., a cloud platform), or entities that include dedicated or general-purpose hardware devices and software functions. This application does not limit the specific form of the terminal devices and network devices.

In wireless communication networks, such as mobile communication networks, the services supported by the networks are becoming increasingly diverse, thus requiring increasingly diverse demands. For example, networks need to support ultra-high speeds, ultra-low latency, and/or massive connectivity. This characteristic makes network planning, network configuration, and/or resource scheduling increasingly complex. Furthermore, as network functions become more powerful, such as supporting higher spectrum levels, supporting higher-order multiple-input multiple-output (MIMO) technologies, supporting beamforming, and/or supporting beam management, network energy efficiency has become a hot research topic. These new demands, new scenarios, and new characteristics bring unprecedented challenges to network planning, operation, and efficient operation. To meet these challenges, artificial intelligence technology can be introduced into wireless communication networks to achieve network intelligence.

To support artificial intelligence (AI) technology in wireless networks, AI nodes may also be introduced into the network.

Optionally, the AI node can be deployed in one or more of the following locations within the communication system: access network equipment, terminal equipment, or core network equipment, etc. Alternatively, the AI node can be deployed independently, for example, in a location other than any of the aforementioned devices, such as in the host or cloud server of an over-the-top (OTT) system. The AI node can communicate with other devices in the communication system, which can be, for example, one or more of the following: wireless access network equipment, terminal equipment, or core network elements, etc.

It is understood that this application does not limit the number of AI nodes. For example, when there are multiple AI nodes, they can be divided based on function, such as different AI nodes being responsible for different functions.

It can also be understood that AI nodes can be independent devices, or they can be integrated into the same device to achieve different functions. Alternatively, they can be network elements in hardware devices, software functions running on dedicated hardware, or virtualization functions instantiated on a platform (e.g., a cloud platform). This application does not limit the specific form of the aforementioned AI nodes.

AI nodes can be AI network elements or AI modules.

Figure 1 illustrates a possible application framework in a communication system. As shown in Figure 1, network elements in the communication system are connected via interfaces (e.g., next-generation (NG) interfaces, Xn interfaces) or air interfaces. These network element nodes, such as core network equipment, access network nodes or equipment (RAN nodes or equipment), terminals, or one or more devices in operation administration and maintenance (OAM), are equipped with one or more AI modules (only one is shown in Figure 1 for clarity). The access network node can be a single RAN node or can include multiple RAN nodes, for example, including CU and DU. The CU and/or DU can also be equipped with one or more AI modules. Optionally, the CU can also be split into CU-CP and CU-UP. One or more AI models are configured in CU-CP and/or CU-UP.

The AI module is used to implement corresponding AI functions. AI modules deployed in different network elements can be the same or different. Depending on the parameter configuration, the AI module can implement different functions. The AI module model can be configured based on one or more of the following parameters: structural parameters (e.g., at least one of the following: number of neural network layers, neural network width, inter-layer connections, neuron weights, neuron activation function, or bias in the activation function), input parameters (e.g., type and/or dimension of input parameters), or output parameters (e.g., type and/or dimension of output parameters). The bias in the activation function can also be referred to as the neural network bias.

An AI module can have one or more models. A model can infer an output, which includes one or more parameters. The learning, training, or inference processes of different models can be deployed on different nodes or devices, or they can be deployed on the same node or device.

Figure 2 illustrates a possible application framework in a communication system. As shown in Figure 2, the communication system includes a RAN intelligent controller (RIC). For example, the RIC can be the AI module shown in Figure 1, used to implement AI-related functions. The RIC includes near-real-time RICs (near-RT RICs) and non-real-time RICs (non-RT RICs). Non-real-time RICs primarily process non-real-time information, such as data that is not sensitive to latency, with latency in the order of seconds. Real-time RICs primarily process near-real-time information, such as data that is relatively sensitive to latency, with latency in the order of tens of milliseconds.

The near real-time RIC is used for model training and inference. For example, it is used to train an AI model and then use that AI model for inference. The near real-time RIC can obtain network-side and/or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and/or RU) and/or terminals. This information can be used as training data or inference data. Optionally, the near real-time RIC can deliver the inference results to the RAN nodes and/or terminals. Optionally, inference results can be exchanged between CU and DU, and/or between DU and RU. For example, the near real-time RIC delivers the inference results to the DU, and the DU then sends the inference results to the RU.

The non-real-time RIC is also used for model training and inference. For example, it can be used to train an AI model and then use that model for inference. The non-real-time RIC can obtain network-side and/or terminal-side information from RAN nodes (e.g., CU, CU-CP, CU-UP, DU, and/or RU) and/or terminals. This information can be used as training data or inference data, and the inference results can be delivered to RAN nodes and/or terminals. Optionally, inference results can be exchanged between CU and DU, and/or between DU and RU. For example, the non-real-time RIC delivers the inference results to the DU, and the DU then sends the inference results to the RU.

The near real-time RIC and non-real-time RIC can also be set up as separate network elements. Optionally, the near real-time RIC and non-real-time RIC can also be part of other devices. For example, the near real-time RIC can be set in the RAN node (e.g., in CU, DU), while the non-real-time RIC can be set in the OAM, cloud server, core network device, or other network device.

Figure 3 is a schematic diagram of a communication system applicable to the communication method of this application embodiment. As shown in Figure 3, the communication system 100 may include at least one network device, such as network device 110 shown in Figure 3; the communication system 100 may also include at least one terminal device, such as terminal device 120 and terminal device 130 shown in Figure 3. Network device 110 and terminal devices (such as terminal device 120 and terminal device 130) can communicate via a wireless link. The communication devices in this communication system, for example, network device 110 and terminal device 120, can communicate via multi-antenna technology.

Figure 4 is a schematic diagram of a communication system applicable to the communication method of this application embodiment. Compared with the communication system 100 shown in Figure 3, the communication system 200 shown in Figure 4 further includes an AI network element 140. The AI network element 140 is used to perform AI-related operations, such as building a training dataset or training an AI model.

In one possible implementation, network device 110 can send data related to the training of the AI model to AI network element 140, which then constructs a training dataset and trains the AI model. For example, the data related to the training of the AI model may include data reported by the terminal device. AI network element 140 can send the results of operations related to the AI model to network device 110, which then forwards them to the terminal device. For example, the results of operations related to the AI model may include at least one of the following: a trained AI model, model evaluation results, or test results. Exemplarily, a portion of the trained AI model may be deployed on network device 110, and another portion on the terminal device. Alternatively, the trained AI model may be deployed on network device 110. Or, the trained AI model may be deployed on the terminal device.

It should be understood that Figure 4 is only used as an example of the AI network element 140 being directly connected to the network device 110. In other scenarios, the AI network element 140 can also be connected to a terminal device. Alternatively, the AI network element 140 can be connected to both the network device 110 and a terminal device simultaneously. Alternatively, the AI network element 140 can also be connected to the network device 110 through a third-party network element. This application embodiment does not limit the connection relationship between the AI network element and other network elements.

AI element 140 can also be set as a module in network devices and/or terminal devices, for example, in network device 110 or terminal device shown in Figure 3.

It should be noted that Figures 3 and 4 are simplified schematic diagrams for ease of understanding. For example, the communication system may also include other devices, such as wireless relay devices and/or wireless backhaul devices, which are not shown in Figures 3 and 4. In practical applications, the communication system may include multiple network devices or multiple terminal devices. The embodiments of this application do not limit the number of network devices and terminal devices included in the communication system.

To facilitate understanding of the solutions in the embodiments of this application, the terms that may be involved in the embodiments of this application are explained below.

(1) Artificial Intelligence: This refers to enabling machines to learn, accumulate experience, and solve problems that humans can solve through experience, such as natural language understanding, image recognition, and chess. Artificial intelligence can be understood as the intelligence exhibited by machines created by humans. Generally, artificial intelligence refers to the technology of presenting human intelligence through computer programs. The goals of artificial intelligence include understanding intelligence by constructing computer programs that demonstrate symbolic reasoning or reasoning.

(2) Machine Learning (ML): This is an implementation method of artificial intelligence. Machine learning is a method that endows machines with the ability to perform functions that cannot be accomplished through direct programming. In practical terms, machine learning is a method that uses data to train a model and then uses the model to make predictions. There are many machine learning methods, such as neural networks (NN), decision trees, and support vector machines. Machine learning theory mainly involves designing and analyzing algorithms that enable computers to learn automatically. Machine learning algorithms are a class of algorithms that automatically analyze data to obtain patterns and use these patterns to predict unknown data.

(3) Neural Networks: Neural networks are a specific manifestation of machine learning methods. A neural network is a mathematical model that mimics the behavioral characteristics of animal neural networks to process information. As shown in Figure 5, a neural network can be composed of three types of computational layers: input layer, hidden layer, and output layer. Each layer has one or more logical judgment units, called neurons. Common neural network structures include feedforward neural networks (FNN), convolutional neural networks (CNN), and recurrent neural networks (RNN), all of which are based on neurons. Each neuron can perform a weighted summation operation on its input values and output the result of the weighted summation operation through a nonlinear function. The weights of the weighted summation operation of neurons in a neural network and the nonlinear function can be called the parameters of the neural network. The connection relationships between neurons in a neural network can be called the structure of the neural network. The parameters of all neurons in a neural network constitute the parameters of the neural network.

(4) Deep neural network: A neural network with multiple hidden layers.

(5) Deep learning: Machine learning using deep neural networks.

(6) AI Model: An AI model is an algorithm or computer program that can implement AI functions. An AI model represents the mapping relationship between the model's input and output; in other words, an AI model is a function model that maps an input of a certain dimension to an output of a certain dimension. The parameters of the function model can be obtained through machine learning training. For example, f(x) = ^ax² + b is a quadratic function model, which can be considered an AI model. a and b are the parameters of this AI model, and a and b can be obtained through machine learning training. Exemplarily, the AI models mentioned in the following embodiments of this application are not limited to neural networks, linear regression models, decision tree models, support vector machines (SVM), Bayesian networks, Q-learning models, or other machine learning (ML) models.

The implementation of an AI model can be a hardware circuit, software, or a combination of both; there are no restrictions. Non-restrictive examples of software include: program code, program, subroutine, instruction, instruction set, code, code segment, software module, application program, or software application, etc.

(7) Training dataset:

In the field of machine learning, ground truth usually refers to data that is considered accurate or real.

Training datasets are used to train AI models. A training dataset can include the input to the AI model, or it can include both the input and the target output of the AI model. Specifically, a training dataset includes one or more training data points, which can include training samples input to the AI model or the target output of the AI model. The target output can also be referred to as a label, sample label, or labeled sample. The label is the ground truth value.

In the field of communications, training datasets can include simulation data collected through simulation platforms, experimental data collected from experimental scenarios, or measured data collected in actual communication networks. Because the geographical environment and channel conditions where the data is generated vary—for example, indoor/outdoor conditions, movement speed, frequency bands, or antenna configurations—the collected data can be categorized during acquisition. For instance, data with the same channel propagation environment and antenna configuration can be grouped together.

Model training essentially involves learning certain features from training data. In training AI models (such as neural network models), the goal is to make the model's output as close as possible to the desired predicted value. This is achieved by comparing the network's current predictions with the target value and updating the weight vector of each layer based on the difference. (Of course, there's usually an initialization process before the first update, where parameters are pre-configured for each layer.) For example, if the network's prediction is too high, the weight vector is adjusted to predict a lower value. This adjustment continues until the AI model can predict the target value or a value very close to it. Therefore, it's necessary to predefine "how to compare the difference between the predicted and target values," which is the loss function or objective function. These are important equations used to measure the difference between the predicted and target values. Taking the loss function as an example, a higher output value (loss) indicates a greater difference. Therefore, training the AI model becomes a process of minimizing this loss, making the loss function value less than a threshold, or making the loss function value meet the target requirements. For example, if the AI model is a neural network, adjusting the model parameters of the neural network includes adjusting at least one of the following parameters: the number of layers of the neural network, the width, the weights of the neurons, or the parameters in the activation function of the neurons.

(8) Model training: The process of training the model parameters by selecting an appropriate loss function and using optimization algorithms to make the value of the loss function less than the threshold, or to make the value of the loss function meet the target requirements.

(9) Model Monitoring: This is used to observe the running AI model and ensure its performance and reliability. Before model monitoring, the performance of the AI model is judged by observing its performance on a pre-provided dataset (i.e., data collected from a non-real-world environment). After training on a static dataset (i.e., training data), the AI model is put into inference tasks in constantly changing real-world scenarios. This difference between the static dataset during training and the dynamically changing data in actual use can cause the performance of the AI model to degrade over time, necessitating model monitoring of the running AI model.

In some applications of AI models using air interface technology, the training and inference of the AI model may be deployed on different network elements. To ensure the performance of the AI model, the network element training the AI model may need to periodically update the AI model and send the updated AI model to the network element performing AI model inference, resulting in significant air interface overhead.

This application provides a communication method in which a first device can identify whether an entity on the first device side and an entity on the second device side have the same at least one feature by using the entity's identification information. This is advantageous because if at least one feature of the two entities is identified to be different, the first device will only send information about the at least one different feature, thereby saving air interface overhead.

Before introducing the scheme of this application, the following points should be noted.

(1) In this application, “instruction” may include direct instruction, indirect instruction, explicit instruction, and implicit instruction. When describing a certain instruction information for the purpose of instructing A, it can be understood that the instruction information carries A, directly instructs A, or indirectly instructs A.

In this application, the information indicated by the instruction information is called the information to be instructed. In specific implementations, there are many ways to indicate the information to be instructed, such as, but not limited to, directly indicating the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly indicate the information to be instructed by indicating other information, where there is a relationship between the other information and the information to be instructed. It can also indicate only a part of the information to be instructed, while the other parts are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol-defined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. Furthermore, the information to be instructed can be sent as a whole or divided into multiple sub-information pieces, and the sending period and/or timing of these sub-information pieces can be the same or different.

(2) In this application, the expression "/" is used to indicate that the objects before and after are in an "or" relationship; for example, A/B can mean: A or B. The expression "and/or" is used to indicate that the objects before and after are in a relationship of either "and" or "or"; for example, A and/or B can mean the following: A exists alone, B exists alone, A and B exist simultaneously, where A and B can be single or multiple. "At least one of the following" or similar expressions are used to indicate any combination of the listed items; for example, at least one of A, B and/or C can mean the following: A exists alone, B exists alone, C exists alone, A and B exist simultaneously, B and C exist simultaneously, A and C exist simultaneously, A, B and C exist simultaneously, where A, B, and C can be single or multiple.

(3) In this application, "send" and "receive" indicate the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information being XX, which may include direct transmission via the air interface or indirect transmission by other units or modules via the air interface. "Receive information from YY" can be understood as the source of the information being YY, which may include direct reception from YY via the air interface or indirect reception from YY by other units or modules via the air interface. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface. In other words, sending and receiving can occur between devices, such as between network devices and terminal devices, or within a device, such as between components, modules, chips, software modules, or hardware modules within the device via a bus, wiring, or interface.

(4) In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terms and/or descriptions of different embodiments are consistent and can be referenced by each other. The technical features of different embodiments can be combined to form new embodiments according to their inherent logical relationship.

(5) In this application, the terms "first," "second," "#1," "#2," etc., are used for ease of description and to distinguish objects, and are not intended to limit the scope of the embodiments of this application. They are not used to describe the order or sequence of features. It should be understood that such described objects can be interchanged where appropriate so as to describe solutions other than those in the embodiments of this application.

(6) In this application, “predefined” may mean a standard protocol predefined, or it may mean that the devices have agreed or negotiated in advance.

(7) In this application, the words “exemplary”, “for example”, etc., are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as an “example” in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word “example” is intended to present the concept in a specific manner. In the embodiments of this application, “of,” “corresponding, relevant,” and “corresponding” can sometimes be used interchangeably, and it should be noted that their intended meanings are consistent when their differences are not emphasized. In addition, “corresponding to” in this application can also be replaced with “as,” “determined according to xx,” or “used to determine.” For example, in the following embodiment, “the first storage resource information corresponds to the number of storage resources available for performing tasks”, “corresponding to” can be replaced with “used to determine.” As another example, in the following embodiment, “the storage resources available for performing tasks correspond to the storage resources available for running AI models/functions to perform tasks”, “corresponding to” can be replaced with “as.”

The communication method provided by the embodiments of this application will be described in detail below with reference to the accompanying drawings. The embodiments provided by this application can be applied to the communication systems shown in Figure 3 or Figure 4 above, and are not limited thereto.

It should be noted that the first or second device in the following embodiments can be a terminal device, or a component of a terminal device, such as a chip or circuit. Alternatively, the first or second device in the following embodiments can be a network device, or a component of a network device, such as a chip or circuit. Or, the first or second device in the following embodiments can be an access network node, such as a RIC, CU, DU, or RU, etc., or the second communication device can be a component of an access network node, such as a chip or circuit. Alternatively, the first or second device in the following embodiments can be an OTT server or a cloud server, etc.

It should also be noted that the first device and the second device in the following embodiments are different. For example, the first device is a network device and the second device is a terminal device.

It should also be noted that the entities in the following examples can be replaced with objects, targets, targets, etc.

Figure 6 shows a schematic flowchart of the communication method provided in an embodiment of this application. As shown in Figure 6, method 600 may include the following steps.

S610, the first device sends the fifth instruction information.

Correspondingly, the second device receives the fifth instruction information.

The fifth instruction information is used to indicate the format of the first identification information of the first entity. The first identification information of the first entity can also be called the identity (ID) of the first entity.

In one possible implementation, if the first identification information includes first indication information corresponding to each feature of at least one feature of the first entity, and the first indication information corresponding to each feature of at least one feature of the first entity is arranged in a first order, then the fifth indication information is used to indicate that the first identification information includes first indication information corresponding to each feature of at least one feature of the first entity, and the first indication information corresponding to each feature of at least one feature of the first entity is arranged in a first order.

The following describes the possible forms of the fifth instruction message.

Possible form 1: The fifth indication information may include an index of a first order. For example, the second device may have pre-configured a first correspondence, which is a correspondence between at least one order and an index of at least one order, where at least one order includes a first order. Upon receiving the fifth indication information, the second device can determine the first order based on the index of the first order and the correspondence. Each of the at least one order is an arrangement order of at least one feature of the first entity. For example, the first correspondence pre-configured by the second device is shown in Table 1 below.

Table 1

It can be understood that the arrangement order of the features shown in Table 1 is equivalent to the arrangement order of the first indication information corresponding to the features. For example, if the first order is the order with index 1, then the second device can determine that the format of the first identification information of the first entity is [the first indication information corresponding to feature #1, the first indication information corresponding to feature #2, and the first indication information corresponding to feature #3].

Optionally, the fifth indication information may also include information related to the name of each of the at least one feature. For example, if at least one feature of the first entity is a subset of all the features of the first entity, the second device can determine which features of the first entity are among all the features of the first entity based on the fifth indication information. For instance, if all the features of the first entity include features #1 to #3, and the fifth indication information includes information related to the names of features #1 and #3, then the second device can determine that the first identification information of the first entity includes the first indication information corresponding to feature #1 and the first indication information corresponding to feature #3.

The information related to the feature name may include the index of the feature name and/or the feature name itself.

Possible form 2: The fifth instruction information may include an index of the format of the first identification information. For example, the second device may have a pre-configured second correspondence, which is a correspondence between the format of at least one identification information and the index of the format of at least one identification information. If the at least one identification information includes the first identification information, then when the second device receives the fifth instruction information, it can determine the format of the first identification information based on the index of the format of the first identification information and the second correspondence. For example, the second correspondence pre-configured by the second device is shown in Table 2 below.

Table 2

Possible form 3: The fifth instruction information may include the first identification information. Accordingly, the second device may determine the format of the first identification information.

In one possible implementation, if the first identification information includes first indication information corresponding to each feature of at least one feature of the first entity, and information related to the name of each feature of at least one feature, then the fifth indication information is used to indicate that the first identification information includes the first indication information corresponding to each feature of at least one feature of the first entity, and information related to the name of each feature of at least one feature of the first entity.

The following describes the possible forms of the fifth instruction message.

Possible form 4, the fifth instruction information may include information about the name of each feature in at least one feature of the first entity.

Possible form 5: The fifth indication information may include an index of the format of the first identification information. For example, if the second device is pre-configured with a second correspondence as shown in Table 2, and the index included in the fifth indication information is 3, then the second device can determine that the first identification information includes information related to the names of features #1 and #2, as well as the first indication information corresponding to features #1 and #2 respectively.

Possible form 6: The fifth instruction information may include the first identification information. Accordingly, the second device may determine the format of the first identification information.

The first entity and at least one feature of the first entity are described below.

For example, the first entity can be a model, such as an AI model, and at least one feature of the first entity can include one or more of the following features: the structure of the entity (i.e., the model structure), the parameters associated with the entity (i.e., the model parameters), the data associated with the entity (e.g., the data used to train the model and/or the data used to monitor the model), the dataset associated with the entity (e.g., the dataset used to train the model and/or the dataset used to monitor the model), or the scenario in which the entity is applicable (i.e., the scenario in which the model is applicable, such as indoors or outdoors).

For example, the first entity can be data, and at least one characteristic of the first entity can include one or more of the following: source (e.g., from a transmitter or receiver), function (e.g., for training, for testing, or unable to be sent to the peer), data content (e.g., bit information (including bit information with a certain distribution), symbol information (including symbol data corresponding to different constellation types)), data size (e.g., demodulated hard/soft bits, or decoded bits), data type (e.g., the data is an estimate or a true value), or related channel information (e.g., received signal, equalized signal after channel estimation, average channel delay, or signal noise ratio (SNR)).

For example, the first entity can be a dataset, and at least one characteristic of the first entity can include one or more of the following: source (e.g., from a transmitter or receiver, or from simulation or testing), function (e.g., for training, for testing, or unable to be sent to the peer), data content (e.g., bit information (including bit information with a certain distribution), symbol information (including symbol data corresponding to different constellation types)), data size (e.g., demodulated hard/soft bits, or decoded bits), data type (e.g., the data is an estimated value or a true value), or related channel information (e.g., received signal, equalized signal after channel estimation, average channel delay, or average SNR). The dataset is a training/test dataset stored according to a certain size, and the dataset can include multiple datasets.

For example, the first entity can be a scene, and at least one feature of the first entity can include one or more of the following: scene-related location, scene-related time, scene-related scatterer richness, or scene-related line of sight (LOS)/non-line of sight (NLOS) ratio, etc.

It should be noted that S610 is an optional step. For example, if the format of the first identification information is a predefined format, then method 600 may not include S610.

S620, the second device sends the first identification information.

Accordingly, the first device receives the first identification information.

In one possible implementation, the second device may periodically send the first identification information. For example, the second device periodically sends the first identification information while using the first entity or performing a task related to the first entity.

In one possible implementation, the second device may send first identification information to the first device in response to a request from the first device. For example, before sending the first identification information, the second device may receive a request message #1 from the first device, which requests the first identification information, and then the second device sends the first identification information to the first device in response to the request message #1.

Optionally, if the second device sends a request message #1 to the first device, the request message #1 may include the fifth instruction information described above.

S630, the first device determines, based on the first identification information and the second identification information of the second entity, whether at least one feature of the first entity is the same as at least one feature of the second entity.

The second entity is the entity obtained by updating the first entity, or the second entity is the same as the first entity.

It is understood that if the first entity is a model, then the second entity is a model, such as an AI model. At least one feature of the second entity may include one or more of the following features: the structure of the entity (i.e., the model structure), the parameters associated with the entity (i.e., the model parameters), the data associated with the entity (e.g., the data used to train the model and/or the data used to monitor the model), the dataset associated with the entity (e.g., the dataset used to train the model and/or the dataset used to monitor the model), or the scenario in which the entity is applicable (i.e., the scenario in which the model is applicable, such as indoor or outdoor).

For example, if the first entity is data, then the second entity is data, and at least one characteristic of the second entity may include one or more of the following: source (e.g. from the sender or receiver, or from simulation or testing), function (e.g. for training, for testing, or unable to be sent to the peer), data content (e.g., bit information (including bit information with a certain distribution), symbol information (including symbol data corresponding to different constellation types)), data size (e.g., demodulated hard/soft bits, or decoded bits), data type (e.g., the data is an estimate or the actual value), or related channel information (e.g., received signal, equalized signal after channel estimation, average channel delay, or SNR).

For example, if the first entity is a dataset, then the second entity is a dataset. At least one characteristic of the second entity may include one or more of the following: source (e.g., from a transmitter or receiver, or from simulation or testing), function (e.g., for training, for testing, or unable to be sent to the peer), data content (e.g., bit information (including bit information with a certain distribution), symbol information (including symbol data corresponding to different constellation types)), data size (e.g., demodulated hard/soft bits, or decoded bits), data type (e.g., the data is an estimate or a true value), or related channel information (e.g., received signal, equalized signal after channel estimation, average channel delay, or average SNR). The dataset may include multiple datasets.

For example, if the first entity is a scene, then the second entity is a scene, and at least one feature of the second entity may include one or more of the following: scene-related location, scene-related time, scene-related scatterer richness, or scene-related line of sight (LOS)/non-line of sight (NLOS) ratio, etc.

The second identification information of the second entity includes second indication information corresponding to each feature of at least one feature of the second entity.

For example, taking the first feature of at least one feature as an example, the first device determines whether at least one feature of the first entity is the same as at least one feature of the second entity in the following way.

If the first indication information corresponding to the first feature of the first entity included in the first identification information is the same as the second indication information corresponding to the first feature of the second entity included in the second identification information, then the first feature of the first entity is the same as the first feature of the second entity. If the first indication information corresponding to the first feature of the first entity included in the first identification information is different from the second indication information corresponding to the first feature of the second entity included in the second identification information, then the first feature of the first entity is different from the first feature of the second entity.

Based on the method by which the first device determines whether at least one feature of the first entity is the same as at least one feature of the second entity, it is clear that the first identification information, including the first indication information corresponding to the first feature, only needs to have the function of distinguishing whether the first feature of the first entity and the first feature of the second entity are the same. The first identification information, including the first indication information corresponding to the first feature, may or may not be able to determine the first feature; this application does not limit this. In other words, the first identification information, including the first indication information corresponding to the first feature, may be an index or value that can be used to determine the first feature, or it may not be an index or value that can be used to determine the first feature.

For example, if the first entity and the second entity are models, and the first feature is the model structure, and the model structure of the first entity is a CNN and the model structure of the second entity is a DNN, then the difference between the model structures of the first entity and the second entity can be distinguished if the first indication information corresponding to the model structure of the first entity is different from the second indication information corresponding to the model structure of the second entity. This application does not limit whether the model structure of the first entity can be determined to be a CNN based on the first indication information, or whether the model structure of the second entity can be determined to be a DNN based on the second indication information.

It should be understood that the second identification information has the same format as the first identification information. For example, if the first identification information, which includes first indication information corresponding to each feature of at least one feature of the first entity, is arranged in a first order, then the second identification information, which includes second indication information corresponding to each feature of at least one feature of the first entity, is arranged in the first order.

Optionally, if the first device determines, based on the first identification information and the second identification information, that the first feature of the first entity is different from the first feature of the second entity, then method 600 further includes S640.

S640, the first device sends the first information.

Correspondingly, the second device receives the first information.

The first information is used to determine the first feature of the second entity. This application does not limit the first information; any information that can be used to determine the first feature of the second entity can be considered the first information. For example, the first information may include an index of the first feature of the second entity, or the first information may include the first feature of the second entity.

In one possible implementation, if the first feature of the second entity is the highest-ranking feature among at least one feature of the second entity, then the first information is used to determine the second entity.

In one possible implementation, if the first feature of the second entity is not the highest-ranking feature among at least one feature of the second entity, and at least one feature of the second entity also includes features of a lower rank than the first feature of the second entity, then the first information is used to determine the first feature of the second entity, and is also used to determine features of a lower rank than the first feature of the second entity among at least one feature of the second entity.

In one possible implementation, the first information is further used to determine at least one feature of the second entity that is at the same level as the first feature of the second entity.

In this context, higher-ranking features can be replaced with features of higher priority, greater importance, or features that carry more weight in identifying entities. Similarly, lower-ranking features can be replaced with features of lower priority, less importance, or features that carry less weight in identifying entities.

For example, if two entities have different first features, then at least one of their features with a lower rank than the first feature is different. If two entities have the same first feature, then at least one of their features with a lower rank than the first feature may be the same or different.

It should be noted that the levels of different features can be adjusted periodically. For example, in the first period, the level of feature #1 is higher than that of feature #2, but in the second period, the level of feature #2 may be higher than that of feature #1.

The following describes the hierarchical classification of at least one feature of different entities.

For example, if the entity is a model, then the level of the model's structure and/or the scenarios in which the model is applicable is higher than the level of one or more of the following characteristics: model parameters, model-related data, or model-related datasets. The level of model-related parameters is higher than the level of model-related data and/or model-related datasets.

For example, the different features of the model are classified as follows: upper-level features: model structure, applicable scenarios; middle-level features: model parameters, movement speed; lower-level features: model layer type. Among them, movement speed is related to the applicable scenarios of the model, and model layer type is related to model structure.

For example, if the second entity can be data, then the level of its source and/or function is higher than one or more of the following: data content, data size, data type, or associated channel information, and/or the level of its data type and/or data content is higher than one or more of the following: data size, or associated channel information. For instance, the different features of a scene can be categorized as follows: upper-level feature: geographic location; middle-level feature: time; lower-level feature: scatterer richness, LOS/NLOS ratio, etc.

It should be understood that after receiving the first information, the second device can determine the second entity based on the first information.

For example, if the first information is used to determine a first feature of the second entity, then the second device can determine the second entity based on the first feature of the second entity and the first entity. For example, the second device can replace the first feature of the first entity with the first feature of the second entity to determine the second entity.

For example, if the first information is used to determine the second entity, then the second device can directly determine the second entity based on the first information.

For example, if the first information is used to determine a first feature of the second entity, and is also used to determine at least one feature of the second entity that is of a lower level than the first feature of the second entity, then the second device can determine the second entity based on the first information and the first entity. For example, the second device can replace the first feature of the first entity with the first feature of the second entity, and replace at least one feature of the first entity that is of a lower level than the first feature with the corresponding feature of the second entity, thereby determining the second entity.

Optionally, in S640, the first device further sends second indication information corresponding to the first feature of the second entity, or sends second identification information of the second entity. Accordingly, if the second device receives the second indication information corresponding to the first feature of the second entity, the second device replaces the first feature of the first entity in the first identification information with the second indication information corresponding to the first feature of the second entity, thereby determining the second identification information of the second entity.

Optionally, if the first information is used to identify the second entity, then in S640, the first device also sends the second identification information of the second entity.

Optionally, if the first information is used to determine the first feature of the second entity, and is also used to determine at least one feature of the second entity that is lower in level than the first feature, then in S640, the first device further sends second indication information corresponding to the first feature of the second entity, and also sends second indication information corresponding to the features of the second entity that are lower in level than the first feature.

It should be noted that if the first device updates the first entity to the second entity and then determines the second identification information based on the first identification information of the first entity in a predefined manner, the first device may not send the second indication information corresponding to the first feature of the second entity, or may not send the second identification information of the second entity.

For example, if the first indication information corresponding to the first feature of the first entity is valued as A, and the first device updates the first entity to a second entity, and the difference between the second entity and the first entity is that the first feature of the second entity is different from the first entity's first feature, then the first device sets the value of the second indication information corresponding to the first feature of the second entity to A+1 according to a predefined method. The second indication information corresponding to the other features of the second entity besides the first feature is the same as the first indication information corresponding to the corresponding features of the first entity. For example, the second indication information corresponding to feature #1 of the second entity is the same as the first indication information corresponding to feature #1 of the first entity.

In this embodiment of the application, the first device can determine whether at least one feature of the first entity is the same as at least one feature of the second entity through the first identification information and the second identification information. This is advantageous because when it is determined that the first feature of the first entity and the second feature of the second entity are different, the first device can send the first information for determining the first feature of the second entity to the second device, instead of sending the information for determining all features of the second entity to the second device, or in other words, instead of sending the information for determining the second entity to the second device, thereby saving air interface overhead.

Figure 7 shows a schematic flowchart of the communication method provided in an embodiment of this application. As shown in Figure 7, method 700 may include the following steps.

S710, the first device sends the sixth instruction message.

Correspondingly, the second device receives the sixth instruction information.

The sixth indication information is used to indicate the level corresponding to each feature in at least one feature of the first entity.

For a description of the levels of features, please refer to S640 of Method 600 above. For the sake of brevity, it will not be repeated here.

For a description of the first entity, please refer to S610 of Method 600 above. For the sake of brevity, it will not be repeated here.

S720, the first device sends instruction information #C.

Correspondingly, the second device receives instruction information #C.

The instruction message #C is used to determine the format of the third identification information of the first entity. The third identification information of the first entity can also be referred to as the second-class ID, parent ID, or first-level ID of the first entity, etc.

The third identification information of the first entity includes third indication information corresponding to each of the at least one second-class features of the first entity. The at least one second-class feature of the first entity includes the highest-ranking feature among the at least one features of the first entity.

Optionally, the third identification information includes third indication information corresponding to each of the at least one second-class features of the first entity, arranged in a second order. Alternatively, the third identification information may also include name information for each of the at least one second-class features of the first entity.

In one possible implementation, if the third identification information includes third indication information corresponding to each of the at least one second type of features of the first entity, and the third indication information corresponding to each of the at least one second type of features of the first entity is arranged in a second order, then the indication information #C is used to determine that the third identification information includes third indication information corresponding to each of the at least one second type of features of the first entity, and the third indication information corresponding to each of the at least one second type of features of the first entity is arranged in a second order.

In one possible implementation, if the third identification information includes third indication information corresponding to each of the at least one second-class features of the first entity, and information related to the name of each of the at least one second-class features, then the indication information #C is used to determine that the third identification information includes third indication information corresponding to each of the at least one second-class features of the first entity, and information related to the name of each of the at least one second-class features.

For a more detailed description of instruction information #C, please refer to the description of the fifth instruction information in Method 600 above. For the sake of brevity, this application will not repeat it.

Optionally, the indication information #C is also used to indicate the format of the first identification information of the first entity, which includes first indication information corresponding to each of at least one first-class feature of the first entity. The level of the first-class feature of the first entity is lower than the level of the second-class feature of the first entity. The first identification information of the first entity may also be referred to as the first-class ID, intermediate ID, lower-level ID, or second-level ID of the first entity, etc.

In one possible implementation, if the first identification information includes first indication information corresponding to each of the first type features of at least one first type of feature of the first entity, and the first indication information corresponding to each of the first type features of at least one first type of feature of the first entity is arranged in a first order, then the indication information #C is used to determine that the first identification information includes first indication information corresponding to each of the first type features of at least one first type of feature of the first entity, and the first indication information corresponding to each of the first type features of at least one first type of feature of the first entity is arranged in a first order.

In one possible implementation, if the first identification information includes first indication information corresponding to each of the at least one first type of features of the first entity, and information related to the name of each of the at least one first type of features, then the indication information #C is used to determine that the first identification information includes first indication information corresponding to each of the at least one first type of features of the first entity, and information related to the name of each of the at least one first type of features.

The format of the instruction information #C used to indicate the first identification information of the first entity can be referred to the format of the fifth instruction information used to indicate the first identification information described in S610.

S730, the second device sends the third identification information of the first entity.

Correspondingly, the first device receives the third identification information of the first entity.

In one possible implementation, the second device may periodically send the third identification information. For example, the second device periodically sends the third identification information while using the first entity, or while performing a task related to the first entity.

In one possible implementation, the second device may send third identification information to the first device in response to a request from the first device. For example, before sending the third identification information, the second device may receive a request message #2 from the first device, which requests the third identification information. The second device then responds to the request message #2 by sending the third identification information to the first device.

Optionally, if the second device sends a request message #2 to the first device, the request message #2 may include the sixth instruction information and/or instruction information #C described above.

S740, the first device determines, based on the third identification information and the fourth identification information of the second entity, whether at least one second-type feature of the first entity is the same as at least one second-type feature of the second entity.

For a description of the second entity, please refer to S640 of Method 600 above. For the sake of brevity, it will not be repeated here.

The fourth identification information of the second entity includes fourth indication information corresponding to each of the at least one second type of feature of the second entity.

Optionally, the fourth identification information includes fourth indication information corresponding to each of the at least one second-class features of the second entity, arranged in a second order. Alternatively, the fourth identification information may also include name information for each of the at least one second-class features of the second entity.

The method by which the first device determines whether at least one second-type feature of the first entity is the same as at least one second-type feature of the second entity can be referred to the description in S640 of method 600 above, and will not be repeated here for the sake of brevity.

Optionally, if the first device determines, based on the third and fourth identification information, that at least one second-type feature of the first entity is the same as at least one second-type feature of the second entity, then method 700 continues to execute S750b1 to S750b4. If at least one second-type feature of the first entity includes a second feature of the first entity, at least one second-type feature of the second entity includes a second feature of the first entity, and the first device determines, based on the third and fourth identification information, that the second feature of the first entity is different from the second feature of the first entity, then method 700 continues to execute S750a.

S750a, the first device sends the second information.

Correspondingly, the first device receives the second information.

The second piece of information is used to identify the second entity.

Optionally, in S750a, the first device also sends fourth identification information of the second entity and second identification information of the second entity, the second identification information being described in S750b3 below.

If at least one feature of the second entity also includes a feature of a lower level than the first type of feature, such as a third type of feature, then in S750a, the first device further sends fifth identification information of the second entity, the fifth identification information including fifth indication information corresponding to each of the at least one third type of feature of the second entity.

S750b1, the first device sends a request message.

Accordingly, the second device receives the request message.

The request message is used to request the first identification information of the first entity.

For example, the request message may include fifth indication information, which indicates the format of the first identification information. Alternatively, the request message may include indication information #2, which indicates the level of at least one first-class feature of the first entity. Alternatively, the request message may include information related to the name of any one or more of the first-class features of the first entity.

S750b2, the second device sends the first identification information of the first entity.

Accordingly, the first device receives the first identification information of the first entity.

In response to the request message, the second device sends the first identification information of the first entity to the first device.

S750b3, the first device determines, based on the first identification information and the second identification information of the second entity, whether at least one first-type feature of the first entity is the same as at least one first-type feature of the second entity.

The second identification information of the second entity includes second indication information corresponding to each of the first type features in at least one of the first type features of the second entity.

Optionally, the second identification information includes second indication information corresponding to each of the at least one first-class features of the first entity, arranged in a first order. Alternatively, the second identification information may also include name information for each of the at least one first-class features of the first entity.

The method by which the first device determines whether at least one first-class feature of the first entity is the same as at least one first-class feature of the second entity can be referred to the description in S640 of method 600 above, and will not be repeated here for the sake of brevity.

Optionally, if at least one first-class feature of the first entity includes the first feature of the first entity, at least one first-class feature of the second entity includes the first feature of the first entity, and the first device determines, based on the first identification information and the second identification information, that the first feature of the first entity is different from the first feature of the first entity, then method 700 continues to execute S750b4.

S750b4, the first device sends the first information.

Correspondingly, the second device receives the first information.

The first information is used to determine the first characteristic of the second entity.

Optionally, if at least one feature of the second entity also includes features of a lower level than the first type of features of the second entity, the first information is further used to determine features of the second entity that have a lower level than the first type of features of the first entity.

It should be understood that after receiving the first information, the second device can determine the second entity based on the first information. For example, the second device can replace the first feature of the first entity with the first feature of the second entity to determine the second entity.

Optionally, in S750b4, the first device also sends second identification information of the second entity.

If at least one feature of the second entity also includes a feature of a lower level than the first type of feature, such as a third type of feature, then in S750b4, the first device further sends fifth identification information of the second entity, the fifth identification information including fifth indication information corresponding to each of the at least one third type of feature of the second entity.

In this embodiment, by classifying different features of an entity, multiple identification information of the entity can be defined, and each of the multiple identification information corresponds to a different level of feature of the entity. Then, the first device can first compare the third identification information of the first entity with the fourth identification information of the second entity. If, based on the third and fourth identification information, it is determined that the highest-level feature (i.e., the second type of feature) of the first and second entities is not completely identical, the first device can determine that the two entities are different and send second information to the second device to identify the second entity, thereby avoiding the first device reporting the first identification information.

The communication method provided in the embodiments of this application will be described below with reference to Figures 8 and 9, taking the first entity and the second entity as models.

Figure 8 illustrates a schematic flow of the communication method provided in an embodiment of this application. As shown in Figure 8, method 800 may include the following steps.

Download S810, Model #1 (Example of the first entity).

For example, in S810, the first device sends information about model #1 to the second device. This information includes information for identifying model #1, and also includes identification information #1 for model #1 (an example of first identification information). The identification information #1 for model #1 includes indication information #1 (an example of first indication information) corresponding to each of at least one feature of model #1. The identification information #1 can also be referred to as the ID of model #1.

Optionally, the information of model #1 may also include indication information #D (a fifth example of indication information), which is used to indicate the format of identification information #1.

For example, if the instruction information #D includes an index of the format of the identification information #1, then the second device can determine the format of the identification information #1 based on the instruction information #D and the pre-configured correspondence #1. The correspondence #1 is a correspondence between the format of at least one identification piece of information and the index of the format of at least one identification piece of information, where at least one identification piece of information includes identification information #1. For example, the pre-configured correspondence #1 of the second device is shown in Table 3 below.

Table 3

In the model ID corresponding to index 1, A represents the indication information corresponding to the model structure, B represents the indication information corresponding to the model parameters, C represents the indication information corresponding to the training data/dataset, and D represents the indication information corresponding to the model application scenario. In the model ID corresponding to index 2, A1 represents the name information of the model structure, A2 represents the indication information corresponding to the model structure, B1 represents the name information of the model parameters, B2 represents the indication information corresponding to the model parameters, C1 represents the name information of the training data/dataset, C2 represents the indication information corresponding to the training data/dataset, D1 represents the name information of the model application scenario, and D2 represents the indication information corresponding to the model application scenario.

Assume that the format of the identification information #1 of model #1 is the same as the format corresponding to index 1 shown in Table 3, and the identification information #1 of model #1 is [0.0.0.0].

S820, Model #1 is updated to Model #2 (example of the second entity).

For example, if the first device determines that the performance of model #1 is lower than the performance threshold, model #1 can be updated to obtain model #2. After determining model #2, the first device will also determine the identification information #2 of model #2 (an example of second identification information). The identification information #2 of model #2 includes indication information #2 (an example of second indication information) corresponding to each feature in at least one feature of model #2.

It should be understood that the format of identification information #2 is the same as that of identification information #1.

Suppose that during the process of updating model #1 to obtain model #2, the first device only changed the model parameters. In other words, the difference between model #1 and model #2 lies in the different model parameters. Then, the difference between the identification information #2 and identification information #1 of model #2 determined by the first device is that the indication information #1 corresponding to the model parameters included in identification information #1 is different from the indication information #2 corresponding to the model parameters included in identification information #2. For example, the identification information #2 of model #2 determined by the first device is [0.1.0.0].

S830, the first device sends request message #3.

Accordingly, the second device receives request message #3.

Request message #3 is used to request identification information #1 for model #1.

S840, the second device sends identification information #1 for model #1.

Correspondingly, the first device receives the identification information #1 of model #1.

S850, the first device determines that feature #1 of model #1 is different from feature #1 of model #2 based on the identification information #1 of model #1 and the identification information #2 of model #2.

The identification information #1 of model #1 is [0.0.0.0], and the identification information #2 of model #2 is [0.1.0.0]. Since the identification information #1 includes indication information #1 corresponding to the model parameters (i.e. feature #1) and the identification information #2 includes indication information #2 corresponding to the model parameters, the second device can determine that the model parameters of model #1 and model #2 are different.

S860, the first device sends information #1 (example of the first information).

Correspondingly, the second device receives information #1.

Information #1 is used to determine the model parameters of model #2.

Optionally, in S860, the first device may also send identification information #2 of model #2, or send indication information #2 corresponding to the model parameters of model #2.

In this embodiment of the application, the first device can determine the specific differences between the two models by comparing the identification information of the two models before and after the update. Thus, it can send only the part of the updated model that has changed compared to the model before the update, such as only sending the model parameters, thereby avoiding the overhead of sending the entire model.

Figure 9 illustrates a schematic flow of the communication method provided in an embodiment of this application. As shown in Figure 9, method 900 may include the following steps.

Download S901, Model #1 (Example of the first entity).

For example, in S910, the first device sends information about model #1 to the second device. This information includes information for identifying model #1, and also includes identification information #3 (an example of third identification information) and identification information #5 (an example of first identification information) for model #1. Identification information #3 includes indication information #3 (an example of third indication information) corresponding to each of at least one superior feature (an example of second-class features) of model #1. Identification information #5 includes indication information #5 (an example of first indication information) corresponding to each of at least one intermediate feature (an example of first-class features) of model #1. Identification information #3 can also be referred to as the superior ID of model #1, and identification information #5 can also be referred to as the intermediate ID of model #1.

Optionally, if the features of model #1 also include lower-level features, the information of model #1 may further include identification information #7 of model #1. Identification information #7 includes indication information #7 corresponding to each of the at least one lower-level features of model #1. Identification information #7 may also be referred to as the lower-level ID of model #1.

S902, Model #1 is updated to Model #2 (example of the second entity).

S902 can refer to S820 in method 800 above.

S903, the first device sends request message #4.

Accordingly, the second device receives request message #4.

Request message #4 is used to request identification information #3 for model #1.

S904, the second device sends identification information #3 for model #1.

Correspondingly, the first device receives the identification information #3 from model #1.

S905, the first device determines whether the superior features of model #1 and model #2 are the same based on the identification information #3 of model #1 and the identification information #4 of model #2.

The identification information #4 of model #2 includes indication information #4 (an example of the fourth indication information) corresponding to each of the at least one superior feature of model #2. The identification information #4 can also be referred to as the superior ID of model #2.

Assuming that the upper-level features include the model structure and the scenarios in which the model is applicable, and the identification information #3 of model #1 is [0.0] and the identification information #4 of model #2 is [1.0], then the first device can determine that the model structure of model #1 is different from that of model #2.

Assuming that the superior features include the model structure and the scenarios in which the model is applicable, and that the identification information #3 of model #1 is [0.0] and the identification information #4 of model #2 is [0.0], then the first device can determine that the superior features of model #1 are the same as the superior features of model #2.

Optionally, if in S905 the first device determines that the superior feature of model #1 is the same as the superior feature of model #2, then method 900 continues to execute S907 to S910. If in S905 the first device determines that the model structure of model #1 is different from the model structure of model #2, then method 900 continues to execute S906.

S906, the first device sends information #2.

Correspondingly, the second device receives information #2.

Information #2 is used to determine model #2.

Optionally, in S906, the first device also sends identification information #4 of model #2.

Optionally, in S906, the first device further transmits identification information #6 of model #2, which includes indication information #6 (an example of second indication information) corresponding to each intermediate feature among at least one intermediate feature of model #2. Identification information #6 may also be referred to as the intermediate ID of model #2.

Optionally, if the features of model #2 also include lower-level features, then in S906, the first device further sends identification information #8 of model #2, which includes indication information #2 corresponding to each of the at least one lower-level features of model #2. Identification information #2 can also be referred to as the lower-level ID of model #2.

S907, the first device sends request message #5.

Accordingly, the second device receives request message #5.

Request message #5 is used to request identification information #5 for model #1.

S908, the second device sends identification information #5 for model #1.

Correspondingly, the first device receives the identification information #5 from model #1.

S909, the first device determines whether the intermediate features of model #1 and model #2 are the same based on the identification information #5 of model #1 and the identification information #6 of model #2.

Assuming that intermediate features include model parameters and movement speed, and the identification information #5 of model #1 is [0.0] and the identification information #6 of model #2 is [1.0], then the first device can determine that the model parameters of model #1 are different from those of model #2.

Assuming that the intermediate features include model parameters and movement speed, and the identification information #5 of model #1 is [0.0] and the identification information #5 of model #2 is [0.0], then the first device can determine that the intermediate features of model #1 are the same as the intermediate features of model #2.

Optionally, if in S909 the first device determines that the intermediate features of model #1 are the same as the intermediate features of model #2, then the first device continues to request the identification information #7 of model #1 and determines whether the lower-level features of model #1 are the same as the lower-level features of model #2. If in S909 the first device determines that the model parameters of model #1 are different from the model parameters of model #2, then method 900 continues to execute S910.

S910, the first device sends information #3.

Correspondingly, the second device receives information #3.

Information #3 is used to determine the model parameters of model #2.

Optionally, information #3 is also used to determine other intermediate features of model #2, for example, information #3 is also used to determine the movement speed of model #2.

Optionally, information #3 is also used to determine the lower-level features of model #2.

Optionally, in S910, the first device also sends identification information #6 of model #2.

Optionally, if the features of model #2 also include lower-level features, then in S910, the first device also sends the identification information #8 of model #2.

In this embodiment of the application, the first device can determine whether the upper-level features of the two models are the same by comparing the upper-level IDs of the two models before and after the update. If it is determined that the upper-level features of the two models are different, the updated model can be sent, thereby avoiding requesting the second device to report the intermediate ID and/or lower-level ID.

It should be understood that 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.

It should also be understood that, in the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terms and/or descriptions between different embodiments are consistent and can be referenced by each other, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

It is understood that, in the above-described method embodiments, the methods and operations implemented by the apparatus (such as the first apparatus or the second apparatus) can also be implemented by components of the apparatus (such as chips or circuits).

The communication method provided in the embodiments of this application has been described in detail above with reference to Figures 6 to 9. The above communication method is mainly described from the perspective of the interaction between the first device and the second device. It is understood that, in order to achieve the above functions, the first device and the second device include hardware structures and/or software modules corresponding to the execution of each function.

It is understood that, in order to achieve the functions in the above embodiments, the first device and the second device include hardware structures and/or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and method steps of the various examples described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application scenario and design constraints of the technical solution.

Figures 10 and 11 are schematic block diagrams of communication devices provided in embodiments of this application. These communication devices can be used to implement the functions of the first or second device in the above method embodiments, and thus can also achieve the beneficial effects of the above method embodiments.

Figure 10 is a schematic block diagram of a communication device 2000 provided in an embodiment of this application. As shown in Figure 10, the communication device 2000 includes a transceiver unit (or communication unit) 2020. Optionally, the communication device 2000 also includes a processing unit 2010. The communication device 2000 is used to implement the functions of the first or second device in the method embodiments shown in Figures 6, 7, 8, or 9 above.

When the communication device 2000 is used to implement the function of the first device in the method embodiment shown in FIG6, FIG7, FIG8 or FIG9: the transceiver unit 2020 is used to receive first identification information of the first entity, the first identification information including first indication information corresponding to each of at least one feature of the first entity, and at least one feature of the first entity including the first feature of the first entity; the processing unit 2010 is used to determine, based on the first identification information and the second identification information of the second entity, that the first feature of the first entity is different from the first feature of the second entity; the second identification information includes second indication information corresponding to each of at least one feature of the second entity, and at least one feature of the second entity including the first feature of the second entity; the transceiver unit 2020 is also used to send first information, the first information being used to determine the first feature of the second entity, the second entity being determined by the first feature of the second entity and the first entity, or the second entity being determined by the first feature of the second entity.

For a more detailed description of the processing unit 2010 and the transceiver unit 2020, please refer to the relevant descriptions in the method embodiments shown in Figures 6, 7, 8 or 9.

When the communication device 2000 is used to implement the function of the second device in the method embodiment shown in FIG6, FIG7, FIG8 or FIG9: the transceiver unit 2020 is used to send first identification information of the first entity, the first identification information includes first indication information corresponding to each of at least one feature of the first entity, and at least one feature of the first entity includes the first feature of the first entity; the transceiver unit 2010 is also used to receive first information, the first information is used to determine the first feature of the second entity, the first feature of the second entity is different from the first feature of the first entity, the second entity is determined by the first feature of the second entity and the first entity, or the second entity is determined by the first feature of the second entity.

For a more detailed description of the processing unit 2010 and the transceiver unit 2020, please refer to the relevant descriptions in the method embodiments shown in Figures 6, 7, 8 or 9.

The apparatus 2000 of each of the above-described schemes has the function of implementing the corresponding steps performed by the first apparatus in the above-described method, or the apparatus 2000 of each of the above-described schemes has the function of implementing the corresponding steps performed by the second apparatus in the above-described method. The function can be implemented by hardware or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions; for example, the transceiver unit can be replaced by a transceiver (e.g., the transmitting unit in the transceiver unit can be replaced by a transmitter, and the receiving unit in the transceiver unit can be replaced by a receiver), and other units, such as processing units, can be replaced by processors, respectively executing the transceiver operations and related processing operations in each method embodiment.

Furthermore, the aforementioned transceiver unit can also be a transceiver circuit (e.g., it may include a receiving circuit and a transmitting circuit), and the processing unit can be a processing circuit. The processing circuit can be one or more processors, or all or part of the circuitry within one or more processors used for control or processing functions. In embodiments of this application, the device in FIG10 can be the first or second device in the foregoing embodiments, or it can be a chip or a chip system, such as a system-on-chip (SoC). The transceiver unit can be an input/output circuit or a communication interface; the processing unit is a processor, microprocessor, or integrated circuit integrated on the chip. No limitations are imposed here.

Figure 11 is a schematic block diagram of a communication device 3000 provided in an embodiment of this application. The device 3000 includes a processing circuit. The device may also include a communication circuit. The processing circuit and the communication circuit communicate with each other via an internal connection path. The processing circuit executes instructions to control the communication circuit to send and/or receive signals.

Taking a processing circuit including one or more processors and a communication circuit including a transceiver as an example, as shown in Figure 11, the communication device 3000 includes a processor 3010 and a transceiver 3020. The processor 3010 and the transceiver 3020 are coupled to each other. It is understood that the transceiver 3020 can be a transceiver or an input/output interface. Optionally, the communication device 3000 may also include a memory 3030 for storing instructions executed by the processor 3010, or storing input data required by the processor 3010 to execute instructions, or storing data generated after the processor 3010 executes instructions. Sometimes, the transceiver 3020 can also be understood as part of the processor 3010, in which case the communication device 3000 includes the processor 3010.

In one possible implementation, the apparatus 3000 is used to implement the various processes and steps corresponding to the first apparatus in the above method embodiments. In another possible implementation, the apparatus 3000 is used to implement the various processes and steps corresponding to the second apparatus in the above method embodiments.

It is understood that device 3000 can specifically be the first device or the second device in the above embodiments, or it can be a chip or a chip system. Correspondingly, the communication circuit can be the interface circuit of the chip, or an input/output circuit, which is not limited here. Specifically, device 3000 can be used to execute the various steps and/or processes corresponding to the first device or the second device in the above method embodiments.

When the communication device 3000 is used to implement the method shown in FIG6, FIG7, FIG8 or FIG9, the processor 3010 is used to implement the function of the processing unit 2010, and the transceiver 3020 is used to implement the function of the transceiver unit 2020.

When the aforementioned communication device is a chip or OTT device applied to the first device, the chip or OTT device of the first device implements the functions of the first device in the above method embodiments, for example, implementing the processing functions of the first device. The chip or OTT device of the first device receiving information from the second device can be understood as the information being first received by other modules (such as radio frequency modules or antennas) in the first device, and then sent by these modules to the chip or OTT device of the first device. The chip or OTT device of the first device sending information to the second device can be understood as the information being first sent by the chip or OTT device of the first device to other modules (such as radio frequency modules or antennas) in the first device, and then sent by these modules to the second device.

When the aforementioned communication device is a chip or OTT device applied to the second device, the chip or OTT device of the second device implements the functions of the second device in the above method embodiments, for example, implementing the processing functions of the second device. The chip or OTT device of the second device receiving information from the first device can be understood as the information being first received by other modules (such as radio frequency modules or antennas) in the second device, and then sent by these modules to the chip or OTT device of the second device. The chip or OTT device of the second device sending information to the first device can be understood as the information being first sent by the chip or OTT device of the second device to other modules (such as radio frequency modules or antennas) in the second device, and then sent by these modules to the first device.

It is understood that, in order to achieve the functions in the above embodiments, the first device and the second device include hardware structures and/or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and method steps of the various examples described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application scenario and design constraints of the technical solution.

It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), image processors, artificial intelligence processors, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

The method steps in the embodiments of this application can be implemented in hardware or in software instructions executable by a processor. The software instructions can consist of corresponding software modules, which can be stored in random access memory, flash memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. The storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC. Alternatively, the ASIC can reside in a first device or a second device. The processor and the storage medium can also exist as discrete components in the first device or the second device.

In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this application are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a network device, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video optical disc; or it can be a semiconductor medium, such as a solid-state drive. The computer-readable storage medium may be a volatile or non-volatile storage medium, or may include both types of storage media.

In the above embodiments, unless otherwise specified or there is a logical conflict, the terms and/or descriptions between different embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationships.

Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, 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. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, 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.

In addition, 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.

If 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. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. 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.

The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims (25)

A communication method, characterized in that it includes: Receive first identification information of a first entity, the first identification information including first indication information corresponding to each feature of at least one feature of the first entity, the at least one feature of the first entity including the first feature of the first entity; Based on the first identification information and the second identification information of the second entity, it is determined that the first feature of the first entity is different from the first feature of the second entity; the second identification information includes second indication information corresponding to each feature of at least one feature of the second entity, and at least one feature of the second entity includes the first feature of the second entity; Send first information, the first information being used to determine the first feature of the second entity, the second entity being determined by the first feature of the second entity and the first entity, or the second entity being determined by the first feature of the second entity. The method according to claim 1, characterized in that the method further comprises: Send the second indication information corresponding to the first feature of the second entity. The method according to claim 1 or 2, wherein the feature corresponding to the first indication information is a first type of feature; The first information is used to determine the first feature of the second entity and the features of the second entity that are of a lower level than the first type of feature of the second entity. The method according to claim 3, characterized in that the method further comprises: Send the second indication information corresponding to the first feature of the second entity, and the indication information corresponding to the features of the second entity that are of a lower level than the first type of feature of the second entity. The method according to any one of claims 1 to 4 is characterized in that the feature corresponding to the first indication information is a first type of feature, and the feature corresponding to the second indication information is a first type of feature; Before receiving the first identification information of the first entity, the method further includes: Receive third identification information of a first entity, the third identification information including third indication information corresponding to each of at least one second type of feature of the first entity, wherein the level of the second type of feature is higher than the level of the first type of feature; The third identification information is the same as the fourth identification information of the second entity, and the fourth identification information includes fourth indication information corresponding to each of the second type features in at least one of the second type features of the second entity; A request message is sent, which is used to request the first identification information of the first entity. The method according to any one of claims 1 to 5, characterized in that the first indication information corresponding to each feature of at least one feature of the first entity is arranged in a first order; or, The first identification information also includes information related to the name of each feature in at least one feature of the first entity. The method according to claim 6, characterized in that the method further comprises: Send the fifth instruction message; Wherein, if the first indication information corresponding to each feature of at least one feature of the first entity is arranged in the first order, then the fifth indication information is used to indicate that the first identification information includes the first indication information corresponding to each feature of at least one feature of the first entity, and the first indication information corresponding to each feature of at least one feature of the first entity is arranged in the first order; or, If the first identification information also includes information related to the name of each feature in at least one feature of the first entity, then the fifth indication information is used to indicate that the first identification information includes first indication information corresponding to each feature in at least one feature of the first entity, and information related to the name of each feature in at least one feature of the first entity. The method according to any one of claims 1 to 7 is characterized in that the at least one feature includes one or more of the following: the structure of the entity, entity-related parameters, entity-related data, entity-related dataset, and the scenario to which the entity is applicable. The method according to claim 8, characterized in that the level of the entity's structure and/or the scenario to which the entity is applicable is higher than one or more of the following: entity-related parameters, entity-related data, or entity-related datasets; and/or, The level of the entity-related parameter is higher than one or more of the following: entity-related data or entity-related datasets. The method according to any one of claims 1 to 9, characterized in that the method further comprises: Send a sixth indication message, which indicates the level corresponding to each of the at least one feature. A communication method, characterized in that it includes: Send first identification information of a first entity, the first identification information including first indication information corresponding to each feature of at least one feature of the first entity, the at least one feature of the first entity including the first feature of the first entity; Receive first information, the first information being used to determine a first feature of a second entity, the first feature of the second entity being different from the first feature of the first entity, the second entity being determined by the first feature of the second entity and the first entity, or the second entity being determined by the first feature of the second entity. The method according to claim 11, characterized in that the method further comprises: Receive the second indication information corresponding to the first feature of the second entity. The method according to claim 11 or 12 is characterized in that the feature corresponding to the first indication information is a first type of feature; The first information is used to determine the first feature of the second entity and the features of the second entity that are of a lower level than the first type of feature of the second entity. The method according to claim 13, characterized in that the method further comprises: Receive second indication information corresponding to the first feature of the second entity, and indication information corresponding to features of the second entity that are of a lower level than the first type of feature of the second entity. The method according to any one of claims 11 to 14, characterized in that the first indication information corresponding to each feature of at least one feature of the first entity is arranged in a first order; or, The first identification information also includes information related to the name of each feature in at least one feature of the first entity. The method according to claim 15, characterized in that the method further comprises: Receive the fifth instruction message; Wherein, if the first indication information corresponding to each feature of at least one feature of the first entity is arranged in the first order, then the fifth indication information is used to indicate that the first identification information includes the first indication information corresponding to each feature of at least one feature of the first entity, and the first indication information corresponding to each feature of at least one feature of the first entity is arranged in the first order; or, If the first identification information also includes information related to the name of each feature in at least one feature of the first entity, then the fifth indication information is used to indicate that the first identification information includes first indication information corresponding to each feature in at least one feature of the first entity, and information related to the name of each feature in at least one feature of the first entity. The method according to any one of claims 11 to 16 is characterized in that the at least one feature includes one or more of the following: the structure of the entity, entity-related parameters, entity-related data, entity-related dataset, and the scenario to which the entity is applicable. The method according to claim 17, characterized in that the level of the entity's structure and/or the scenario to which the entity is applicable is higher than one or more of the following: entity-related parameters, entity-related data, or entity-related datasets; and/or, The level of the entity-related parameter is higher than one or more of the following: entity-related data or entity-related dataset. The method according to any one of claims 11 to 18, characterized in that the method further comprises: Receive a sixth indication message, which indicates the level corresponding to each of the at least one feature. A communication device, characterized in that it includes a module or unit for performing the method as described in any one of claims 1 to 10, or includes a module or unit for performing the method as described in any one of claims 11 to 19. A communication device, characterized in that it includes a processor for executing a computer program or instructions to cause the method as described in any one of claims 1 to 19 to be performed. The communication device according to claim 21, wherein the communication device further comprises a memory. A computer-readable storage medium, characterized in that the computer-readable storage medium stores program code for execution by a device, the program code being used to perform the method as described in any one of claims 1 to 19. A computer program product, characterized in that it includes instructions that, when the computer program product is run on a computer, cause the computer to perform the method as described in any one of claims 1 to 19. A chip, characterized in that it includes a processor and a communication interface, wherein the processor reads instructions from a memory via the communication interface to execute the method as described in any one of claims 1 to 19.