Communication method and related apparatus

Abstract

A communication method and a related apparatus. In the method, a first communication apparatus acquires first information, the first information being used for instructing the first communication apparatus to cache service data, the service data being associated with first processing, and the first processing being processing of a first AI model; the first communication apparatus acquires second information, the second information instructing the service data to be used for second processing; and, according to the instruction of the second information, the first communication apparatus performs second processing on the service data. In the present application, the first communication apparatus may cache service data associated with the first processing, and use the service data for other processing (for example, the second processing), thereby improving the disaster recovery capability and the network stability of a process in which the first communication apparatus processes the service data.

Description

A communication method and related apparatus

[0001] This application claims priority to Chinese Patent Application No. 202410814215.0, filed on June 21, 2024, entitled "A Communication Method and Related Device", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of communication technology, and in particular to a communication method and related apparatus. Background Technology

[0003] The introduction of artificial intelligence (AI) capabilities into wireless networks enables significant enhancements to certain communication functions and network characteristics, thereby improving the performance of the network in providing services to users. These enhancements extend beyond network optimization, resource management, and user experience improvement, encompassing key areas such as network security, energy management, and fault recovery.

[0004] In wireless networks, the performance of AI functions may be affected by a variety of factors (such as computing resources, network environment, or data complexity), resulting in some fluctuations in the performance of AI functions, which in turn affects the stability and efficiency of the network.

[0005] Therefore, ensuring the stability and efficiency of wireless networks incorporating AI capabilities is a pressing issue that needs to be addressed. Summary of the Invention

[0006] This application provides a communication method and related apparatus for improving the stability of wireless networks.

[0007] Firstly, this application provides a communication method. The method is executed by a first communication device, which may be a communication device (such as a terminal device or network device), or it may be a component of the communication device (e.g., a circuit or chip responsible for communication functions, such as a modem chip, also known as a baseband chip, or a system-on-a-chip (SoC) chip containing a modem core, or a system-in-package (SIP) chip), or it may be a logic module or software capable of implementing all or part of the functions of the communication device. In this method, the first communication device acquires first information, which instructs the first communication device to cache service data associated with a first process, wherein the first process is the processing of a first AI model. The first communication device acquires second information, which instructs the first communication device to use the service data for a second process. The first communication device performs the second processing on the service data according to the instruction of the second information.

[0008] In this application, the first communication device can cache the service data associated with the first processing and use the service data for the second processing, thereby improving the disaster recovery capability of the first communication device in processing service data and the stability of the network.

[0009] Optionally, when the performance of the first AI model fluctuates, the communication device can process the business data through the second processing, thereby avoiding the impact of the performance fluctuation of the first AI model and improving the stability and efficiency of the network.

[0010] Based on the first aspect, in one optional implementation, the business data includes at least one of the following:

[0011] The input data of the first AI model. For example, assuming that the AI ​​function enabled by the first AI model is channel state information (CSI) compressed feedback, the input data of the first AI model can be historical CSI data, location information of the terminal device, mobility information of the terminal device, location information of the base station, or antenna configuration information of the base station, etc.

[0012] The output data of the first AI model. For example, assuming that the AI ​​function enabled by the first AI model is the CSI compressed feedback function, the output data of the first AI model can be compressed CSI feedback, CSI codebook index, or predicted CSI change trend, etc.

[0013] Associated data used in conjunction with the output data of the first AI model. For example, assuming the AI ​​function enabled by the first AI model is channel estimation, and the output data of the first AI model (i.e., the channel estimation result of the first AI model) also needs to be used for subsequent channel decoding, then the data to be decoded is the aforementioned associated data. Therefore, the first communication device needs to cache the data to be decoded according to the instructions of the first information. After the first communication device performs the second processing, it performs channel decoding based on the data to be decoded and the output data of the second processing (i.e., the channel estimation result of the second processing).

[0014] Based on the first aspect, in one optional implementation, the second processing includes processing of a non-AI model and / or processing of a second AI model. Therefore, the first communication device can process business data using a non-AI model, and the first communication device stops executing the functions enabled by the first AI model (e.g., communication functions), that is, the first communication device reverts to functions enabled by the non-AI model (also called traditional functions or conventional functions); or, the first communication device can process business data using a second AI model, and the first communication device stops executing the functions enabled by the first AI model, that is, the first communication device switches the functions enabled by the first AI model to functions enabled by the second AI model. Here, processing of the non-AI model can refer to processing that does not rely on artificial intelligence technology; for example, the non-AI model can be a pre-configured logical node, logical module, program code, or software. Functions enabled by the non-AI model can be replaced with other terms, such as traditional functions or conventional functions. The non-AI model can be replaced with other terms, such as non-AI module, traditional module, or conventional module, etc.

[0015] Based on the first aspect, in one optional implementation, the first communication device receives first information from the second communication device, that is, the first information obtained by the first communication device is sent by the second communication device, enabling the first communication device to cache the service data associated with the first processing based on the instructions of other communication devices. Alternatively, the first communication device determines the first information based on the first performance of the first AI model, that is, the first information obtained by the first communication device is generated by the first communication device, in order to save overhead.

[0016] Based on the first aspect, in one optional implementation, the first communication device receives second information from the second communication device, that is, the second information obtained by the first communication device is sent by the second communication device, enabling the first communication device to cache the service data associated with the first processing based on the instructions of other communication devices. Alternatively, the first communication device determines the second information based on the first performance of the first AI model, that is, the second information obtained by the first communication device is generated by the first communication device, in order to save overhead.

[0017] Based on the first aspect, in one optional implementation, the second communication device determines a first resource for the first communication device. This first resource is used to carry data associated with the second processing (including one or more of service data, signals, information, and signaling). Then, the second communication device sends third information indicating the first resource to the first communication device; that is, the first communication device receives the third information from the second communication device. When the first communication device performs the second processing, it can use the first resource indicated by the third information to carry the data associated with the second processing (including one or more of service data, signals, information, and signaling), enabling the first communication device to transmit the data on the designated resource, thereby improving the success rate of data transmission.

[0018] Based on the first aspect, in one optional implementation, the first communication device determines a first resource based on one or more factors such as network configuration, channel conditions, and device capabilities. The first resource is used to carry data associated with the second processing (including one or more of service data, signals, information, and signaling). Then, when the first communication device executes the second processing, it can use the first resource to carry the data associated with the second processing (including one or more of service data, signals, information, and signaling). Optionally, the first communication device sends third information to the second communication device, the third information indicating the first resource so that the second communication device can transmit data associated with the second processing (including one or more of service data, signals, information, and signaling) through the first resource.

[0019] Based on the first aspect, in one optional implementation, the second communication device configures auxiliary parameters for the second processing on the first communication device. Then, the second communication device sends fourth information indicating these auxiliary parameters to the first communication device; that is, the first communication device receives the fourth information from the second communication device. When the first communication device performs the second processing, it can use the auxiliary parameters indicated by the fourth information to assist in enabling the second processing, thereby improving the efficiency of the second processing. Optionally, the fourth information includes configuration information for at least one set of auxiliary parameters for the second processing.

[0020] Based on the first aspect, in an optional implementation, the functions of the second processing are executed by the cooperation of the first and second communication devices. The first communication device determines auxiliary parameters for the second processing. Then, the first communication device sends fourth information to the second communication device, the fourth information indicating the auxiliary parameters for the second processing, so that the second communication device can use these auxiliary parameters to assist in enabling the functions of the second processing, thereby improving the efficiency of the second communication device in executing the second processing. Optionally, the fourth information includes configuration information for at least one set of auxiliary parameters for the second processing.

[0021] For example, these auxiliary parameters include, but are not limited to, synchronization signal / physical broadcast channel block (SSB or S-SS / PSBCH block), reference signal (RS), or control channel information. The reference signal can be a channel state information reference signal (CSI-RS), a demodulation reference signal (DMRS), or a sounding reference signal (SRS). Optionally, the control channel information can be physical downlink control channel (PDCCH) information or physical uplink control channel (PUCCH) information, where PDCCH information indicates available PDCCHs, and PUCCH information indicates available PUCCHs.

[0022] Assuming the first communication device is a terminal device and the second communication device is a network device, and the first and second processing are used to enable resource allocation functions, the auxiliary parameters can be PDCCH information and PUCCH information. Specifically, the network device (second communication device) sends resource allocation instructions to the terminal device (first communication device) via the PDCCH indicated by the PDCCH information to optimize the use of time and frequency resources and improve network efficiency; the terminal device (first communication device) feeds back the uplink transmission status to the network device (second communication device) via the PUCCH indicated by the PUCCH information, so that the base station can adjust its resource allocation strategy according to the uplink transmission status to ensure reliable data transmission.

[0023] Based on the first aspect, in one optional implementation, the service data cached by the first communication device includes data from one or more time units. The second information can be a rollback indication in a rollback operation; that is, the second information further indicates that one of the one or more time units is the rollback time, and / or, the second information further indicates that the starting position of the data in the service data used for the second processing is the rollback loading position. Then, the first communication device loads the service data according to the time indicated by the second information and / or the starting position of the data, thereby performing the second processing. Optionally, the time unit can be an hour, minute, second, frame, subframe, time slot, or symbol, etc.

[0024] Based on the first aspect, in one optional implementation, the second information is carried in a medium access control element (MAC CE). Optionally, the second information can be carried in other messages / signaling / information, such as downlink control information (DCI) or RRC messages.

[0025] Based on the first aspect, in one optional implementation, the second information includes at least one of the following:

[0026] The identifier of the function enabled by the first processing and / or the identifier of the model used to implement that function. The identifier of the function enabled by the first processing indicates that the function requires a processing method other than the first processing (i.e., the processing of the first AI model) (e.g., the second processing in this application); for example, assuming the current first communication device enables the CSI compressed feedback function through the first processing (i.e., the processing of the first AI model). If the second information includes the identifier of the CSI compressed feedback function, it indicates that another processing method (e.g., the second processing in this application) is required to implement the CSI compressed feedback function.

[0027] The identifier of the fallback level to which the second processing belongs. The first communication device deploys multiple fallback levels, each corresponding to at least one non-AI model and / or a second AI model. The identifier of the fallback level to which the second processing belongs indicates that one of the non-AI models or the second AI model corresponding to that fallback level is used to execute the second processing. Optionally, the first communication device can acquire multiple resources, including a first resource. Each resource corresponds to a fallback level, and this resource is used to carry data associated with the non-AI model and the second AI model corresponding to that fallback level. When the second information carries the identifier of the fallback level, the first communication device carries the data associated with the second processing (including one or more of service data, signals, information, and signaling) through the resource corresponding to that fallback level.

[0028] The identifier for the second processing. This identifier can be an identifier for a non-AI model or a second AI model, indicating that the non-AI model or the second AI model is used to execute the second processing. Optionally, the first communication device can acquire multiple resources, including the first resource. Each resource corresponds to a non-AI model or a second AI model, and this resource is used to carry data associated with the non-AI model or the second AI model. When the second information carries the identifier of a non-AI model or a second AI model, the first communication device uses the resource corresponding to the non-AI model or the second AI model to carry the data associated with the second processing (including one or more of business data, signals, information, and signaling).

[0029] The identifier for the auxiliary parameters used in the second processing (hereinafter referred to as the auxiliary parameter identifier) ​​indicates the function enabled by the auxiliary parameter corresponding to the auxiliary parameter identifier. Optionally, the first communication device may pre-acquire multiple sets of auxiliary parameters. For example, the first communication device receives fourth information, which includes configuration information for multiple sets of auxiliary parameters, each set of auxiliary parameters corresponding to an auxiliary parameter identifier. When the second information carries an auxiliary parameter identifier, the first communication device determines the auxiliary parameter corresponding to the auxiliary parameter identifier from the multiple sets of auxiliary parameters and uses the auxiliary parameter to assist the function enabled by the second processing.

[0030] Based on the first aspect, in one optional implementation, the second communication device sends fifth information to the first communication device. The fifth information indicates a first data format for caching service data, and this first data format is the same as the second data format used to perform the second processing. Upon receiving the fifth information, the first communication device caches the service data in the first data format indicated by the fifth information. Specifically, since the data format (i.e., the second data format) of the service data supported by the second processing (e.g., a non-AI model or a second AI model) may differ from the data format of the service data supported by the first processing, the first communication device caches the service data in the second data format used to perform the second processing, thereby improving the efficiency of the first communication device in performing the second processing. In other words, the first communication device caches the service data in the data format supported by the aforementioned candidate model. Exemplarily, the methods by which the first communication device converts the service data include, but are not limited to: converting the service data in a quantized manner or normalizing the service data according to a preset range, and then caching the converted service data. Optionally, the fifth information and the first information can be carried in the same message, message, signaling or signal, or the fifth information and the first information can be carried in different messages, messages, signaling or signals.

[0031] Secondly, this application provides a communication method. This method is executed by a second communication device, which can be a communication device (such as a terminal device or network device), or the first communication device can be a component of the communication device (e.g., a circuit or chip responsible for communication functions, such as a modem chip, also known as a baseband chip, or a system-on-a-chip (SoC) chip containing a modem core, or a system-in-package (SIP) chip), or the first communication device can also be a logic module or software capable of implementing all or part of the functions of the communication device. In this method, the second communication device sends first information, which indicates cached service data, the service data being associated with a first process, the first process being the processing of a first AI model; the second communication device then sends second information, which indicates that the service data is used for a second process.

[0032] Based on the second aspect, in one optional implementation, the second processing includes processing of a non-AI model and / or processing of a second AI model. Therefore, the first communication device can process business data using a non-AI model, and the first communication device stops executing the functions enabled by the first AI model (e.g., communication functions), that is, the first communication device reverts to functions enabled by the non-AI model (also called traditional functions or conventional functions); or, the first communication device can process business data using a second AI model, and the first communication device stops executing the functions enabled by the first AI model, that is, the first communication device switches the functions enabled by the first AI model to functions enabled by the second AI model. Here, processing of the non-AI model can refer to processing that does not rely on artificial intelligence technology; for example, the non-AI model can be a pre-configured logical node, logical module, program code, or software. Functions enabled by the non-AI model can be replaced with other terms, such as traditional functions or conventional functions. The non-AI model can be replaced with other terms, such as non-AI module, traditional module, or conventional module, etc.

[0033] Based on the second aspect, in one optional implementation, the second communication device determines a first resource for the first communication device. This first resource is used to carry data associated with the second processing (including one or more of service data, signals, information, and signaling). Then, the second communication device sends third information indicating the first resource to the first communication device; that is, the first communication device receives the third information from the second communication device. When the first communication device performs the second processing, it can use the first resource indicated by the third information to carry the data associated with the second processing (including one or more of service data, signals, information, and signaling), enabling the first communication device to transmit the data on the designated resource and thus improving the success rate of data transmission.

[0034] Based on the second aspect, in one optional implementation, the second communication device configures auxiliary parameters for the second processing on the first communication device. Then, the second communication device sends fourth information indicating these auxiliary parameters to the first communication device; that is, the first communication device receives the fourth information from the second communication device. When the first communication device performs the second processing, it can use the auxiliary parameters indicated by the fourth information to assist in enabling the second processing, thereby improving the efficiency of the second processing. Optionally, the fourth information includes configuration information for at least one set of auxiliary parameters for the second processing.

[0035] Based on the second aspect, in an optional implementation, the service data cached by the first communication device includes data from one or more time units. The second information can be a rollback indication in a rollback operation; that is, the second information further indicates that one of the one or more time units is the rollback time, and / or, the second information further indicates that the starting position of the data in the service data used for the second processing is the rollback loading position. Then, the first communication device loads the service data according to the time indicated by the second information and / or the starting position of the data, thereby performing the second processing. Optionally, the time unit can be an hour, minute, second, frame, subframe, time slot, or symbol, etc.

[0036] Based on the second aspect, in one optional implementation, the second information is carried in the MAC CE. Optionally, the second information can be carried in other messages / signaling / information, such as DCI or RRC messages.

[0037] Based on the second aspect, in one optional implementation, the second information includes at least one of the following:

[0038] The identifier of the function enabled by the first processing and / or the identifier of the model used to implement that function. The identifier of the function enabled by the first processing indicates that the function requires a processing method other than the first processing (i.e., the processing of the first AI model) (e.g., the second processing in this application); for example, assuming the current first communication device enables the CSI compressed feedback function through the first processing (i.e., the processing of the first AI model). If the second information includes the identifier of the CSI compressed feedback function, it indicates that another processing method (e.g., the second processing in this application) is required to implement the CSI compressed feedback function.

[0039] The identifier of the fallback level to which the second processing belongs. The first communication device deploys multiple fallback levels, each corresponding to at least one non-AI model and / or a second AI model. The identifier of the fallback level to which the second processing belongs indicates that one of the non-AI models or the second AI model corresponding to that fallback level is used to execute the second processing. Optionally, the first communication device can acquire multiple resources, including a first resource. Each resource corresponds to a fallback level, and this resource is used to carry data associated with the non-AI model and the second AI model corresponding to that fallback level. When the second information carries the identifier of the fallback level, the first communication device carries the data associated with the second processing (including one or more of service data, signals, information, and signaling) through the resource corresponding to that fallback level.

[0040] The identifier for the second processing. This identifier can be an identifier for a non-AI model or a second AI model, indicating that the non-AI model or the second AI model is used to execute the second processing. Optionally, the first communication device can acquire multiple resources, including the first resource. Each resource corresponds to a non-AI model or a second AI model, and this resource is used to carry data associated with the non-AI model or the second AI model. When the second information carries the identifier of a non-AI model or a second AI model, the first communication device uses the resource corresponding to the non-AI model or the second AI model to carry the data associated with the second processing (including one or more of business data, signals, information, and signaling).

[0041] The identifier for the auxiliary parameters used in the second processing (hereinafter referred to as the auxiliary parameter identifier) ​​indicates the function enabled by the auxiliary parameter corresponding to the auxiliary parameter identifier. Optionally, the first communication device may pre-acquire multiple sets of auxiliary parameters. For example, the first communication device receives fourth information, which includes configuration information for multiple sets of auxiliary parameters, each set of auxiliary parameters corresponding to an auxiliary parameter identifier. When the second information carries an auxiliary parameter identifier, the first communication device determines the auxiliary parameter corresponding to the auxiliary parameter identifier from the multiple sets of auxiliary parameters and uses the auxiliary parameter to assist the function enabled by the second processing.

[0042] Based on the second aspect, in one optional implementation, the second communication device sends fifth information to the first communication device. The fifth information indicates a first data format for caching service data, and this first data format is the same as the second data format used to perform the second processing. Upon receiving the fifth information, the first communication device caches the service data in the first data format indicated by the fifth information. Specifically, since the data format (i.e., the second data format) of the service data supported by the second processing (e.g., a non-AI model or a second AI model) may differ from the data format of the service data supported by the first processing, the first communication device caches the service data in the second data format used to perform the second processing, thereby improving the efficiency of the first communication device in performing the second processing. In other words, the first communication device caches the service data in the data format supported by the aforementioned candidate model. Exemplarily, the methods by which the first communication device converts the service data include, but are not limited to: converting the service data in a quantized manner or normalizing the service data according to a preset range, and then caching the converted service data. Optionally, the fifth information and the first information can be carried in the same message, message, signaling or signal, or the fifth information and the first information can be carried in different messages, messages, signaling or signals.

[0043] Thirdly, this application provides a communication device, which is a first communication device, comprising an acquisition unit and a processing unit. The acquisition unit is configured to acquire first information, the first information indicating cached business data, the business data being associated with first processing, the first processing being the processing of a first AI model; the acquisition unit is further configured to acquire second information, the second information indicating that the business data is used for second processing; the processing unit is configured to perform the second processing on the business data.

[0044] In the third aspect of this application, the constituent modules of the communication device can also be used to execute the steps performed in various possible implementations of the first aspect and achieve the corresponding technical effects. For details, please refer to the first aspect, which will not be repeated here.

[0045] Fourthly, this application provides a communication device, which is a second communication device, comprising a first transmitting unit and a second transmitting unit. The first transmitting unit is used to transmit first information, the first information indicating cached service data, the service data being associated with a first processing, the first processing being the processing of a first AI model; the second transmitting unit is used to transmit second information, the second information indicating that the service data is used for a second processing.

[0046] In the fourth aspect of this application, the constituent modules of the communication device can also be used to perform the steps executed in various possible implementations of the second aspect and achieve the corresponding technical effects. For details, please refer to the second aspect, which will not be repeated here.

[0047] A fifth aspect of this application provides a communication device including at least one processor coupled to a memory; the memory is used to store a program or instructions; the at least one processor is used to execute the program or instructions to enable the communication device to implement the method described in any possible implementation of any of the first to third aspects. Optionally, the communication device may include the memory.

[0048] The sixth aspect of this application provides a communication device including at least one logic circuit and an input / output interface; the logic circuit is used to perform the method described in any of the possible implementations of the first to third aspects described above.

[0049] The seventh aspect of this application provides a communication system, which includes the first communication device and the second communication device described above.

[0050] An eighth aspect of this application provides a computer-readable storage medium for storing one or more computer-executable instructions, which, when executed by a processor, perform the method as described in any possible implementation of any of the first to second aspects described above.

[0051] The ninth aspect of this application provides a computer program product (or computer program) that, when executed by a processor, performs the method described in any possible implementation of any of the first to second aspects described above.

[0052] The tenth aspect of this application provides a chip system including at least one processor for supporting a communication device in implementing the method described in any possible implementation of any of the first to second aspects.

[0053] In one possible design, the chip system may further include a memory for storing program instructions and data necessary for the communication device. The chip system may be composed of chips or may include chips and other discrete devices. Optionally, the chip system may also include interface circuitry that provides program instructions and / or data to the at least one processor.

[0054] The technical effects of any of the design methods in aspects two through ten can be found in the technical effects of the different design methods in aspect one above, and will not be repeated here. Attached Figure Description

[0055] Figures 1a to 1e are schematic diagrams of the AI ​​processing involved in this application;

[0056] Figures 2a to 2c are schematic diagrams of the communication system provided in this application;

[0057] Figure 3 is a schematic diagram of a possible implementation of the communication method in this application;

[0058] Figure 4 is a schematic diagram of another possible implementation of the communication method in this application;

[0059] Figure 5 is a schematic diagram of another possible implementation of the communication method in this application;

[0060] Figure 6 is a schematic diagram of a possible implementation of the second information in this application;

[0061] Figures 7 to 11 are schematic diagrams of the communication device provided in this application. Detailed Implementation

[0062] The present application will now be described with reference to the accompanying drawings. The terminology used in the embodiments section is for illustrative purposes only and is not intended to limit the scope of the application. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided in this application are equally applicable to similar technical problems.

[0063] First, some of the nouns or terms used in this application will be explained, and these nouns or terms are also part of the content of the invention.

[0064] (1) The terms “system” and “network” in this application are used interchangeably. “Multiple” refers to two or more. “And / or” describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, or B exists alone, where A and B can be singular or plural. The character “ / ” generally indicates that the related objects before and after are in an “or” relationship. “At least one of the following” or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, “at least one of A, B and C” includes A, B, C, AB, AC, BC or ABC. Unless otherwise specified, the ordinal numbers such as “first” and “second” mentioned in this application are used to distinguish multiple objects and are not used to limit the order, sequence, priority or importance of multiple objects. Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or apparatus.

[0065] (2) 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 sending directly via the air interface or sending indirectly via the air interface from other units or modules. "Receive information from YY" can be understood as the source of the information being YY, which may include receiving directly from YY via the air interface or receiving indirectly from YY via the air interface from other units or modules. "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.

[0066] 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 buses, wiring, or interfaces.

[0067] It is understandable that information may undergo necessary processing, such as encoding and modulation, between the source and destination, but the destination can understand the valid information from the source. Similar statements in this application can be interpreted in a similar way and will not be elaborated further.

[0068] (3) Configuration and Pre-configuration: In this application, both configuration and pre-configuration are used. Configuration refers to the network device or server sending configuration information or parameter values ​​to the terminal device via messages or signaling, so that the terminal device can determine the communication parameters or resources for transmission based on these values ​​or information. Pre-configuration is similar to configuration; it can be parameter information or parameter values ​​pre-negotiated between the network device / server and the terminal device, parameter information or parameter values ​​specified by standard protocols for use by the base station / network device or terminal device, or parameter information or parameter values ​​pre-stored in the base station / server or terminal device. This application does not limit this.

[0069] It should be understood that these values ​​and parameters can change or be updated.

[0070] (4) In this application, "instruction" can include direct instruction and indirect instruction, as well as explicit instruction and implicit instruction. The information indicated by a certain piece of information (such as the first and second information described below) is called the information to be instructed. In specific implementation, there are many ways to instruct the information to be instructed, such as, but not limited to, directly instructing the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly instruct the information to be instructed by instructing other information, where there is a correlation between the other information and the information to be instructed; or it can only instruct 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 predefined) arrangement of various pieces of information, thereby reducing instruction overhead to some extent. This application does not limit the specific method of instruction. It is understood that for the sender of the instruction information, the instruction information can be used to instruct the information to be instructed, and for the receiver of the instruction information, the instruction information can be used to determine the information to be instructed.

[0071] (5) Terminal device: can be a wireless terminal device capable of receiving network device scheduling and instruction information. The wireless terminal device can be a device that provides voice and / or data connectivity to the user, or a handheld device with wireless connection function, or other processing device connected to a wireless modem.

[0072] Terminal devices can communicate with one or more core networks or the Internet via a radio access network (RAN). Terminal devices can be mobile terminal devices, such as mobile phones (or "cellular" phones), computers, and data cards. For example, they can be portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile devices that exchange voice and / or data with the RAN. Examples include personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablets, and computers with wireless transceiver capabilities. Wireless terminal equipment can also be referred to as a system, subscriber unit, subscriber station, mobile station (MS), remote station, access point (AP), remote terminal, access terminal, user terminal, user agent, subscriber station (SS), customer premises equipment (CPE), terminal, user equipment (UE), mobile terminal (MT), etc.

[0073] By way of example and not limitation, the terminal device in this application can also be a wearable device. Wearable devices, also known as wearable smart devices or smart wearable 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, smart helmets, and smart jewelry for vital sign monitoring.

[0074] Terminals can also be drones, robots, devices in device-to-device (D2D) communication, vehicles to everything (V2X) communication, virtual reality (VR) terminal devices, augmented reality (AR) terminal devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in telemedicine or telehealth services, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, etc.

[0075] Furthermore, terminal devices can also be terminal devices in communication systems evolved from fifth-generation (5G) communication systems (such as 5G Advanced or sixth-generation (6G) communication systems), or terminal devices in future public land mobile networks (PLMNs). For example, 5G Advanced or 6G networks can further expand the form and function of 5G communication terminals; 6G terminals include, but are not limited to, vehicles, cellular network terminals (integrating satellite terminal functions), drones, and Internet of Things (IoT) devices.

[0076] In this application, the aforementioned terminal device can also obtain artificial intelligence (AI) services provided by the network device. Optionally, the terminal device can also have AI processing capabilities.

[0077] (6) Network equipment: This can be equipment in a wireless network. For example, network equipment can be a RAN node (or device) that connects terminal devices to the wireless network, and can also be called a base station. Currently, some examples of RAN equipment include: base station, evolved NodeB (eNodeB), gNB (gNodeB) in 5G communication systems, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), home base station (e.g., home evolved Node B, or home Node B, HNB), base band unit (BBU), or wireless fidelity (Wi-Fi) access point (AP), etc. In addition, in a network structure, network equipment can include central unit (CU) nodes, distributed unit (DU) nodes, or RAN equipment including CU nodes and DU nodes.

[0078] Optionally, RAN nodes can also be macro base stations, micro base stations, indoor stations, relay nodes, donor nodes, or radio controllers in cloud radio access network (CRAN) scenarios. RAN nodes can also be servers, wearable devices, vehicles, or in-vehicle equipment. For example, the access network equipment in vehicle-to-everything (V2X) technology can be a roadside unit (RSU).

[0079] In another possible scenario, multiple RAN nodes collaborate to assist the terminal 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, CUs (control plane, CP), CUs (user plane, UP), or radio units (RUs). CUs and DUs can be configured separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio equipment or radio units, such as remote radio units (RRUs), active antenna units (AAUs), radio heads (RHs), or remote radio heads (RRHs).

[0080] 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 access network (open RAN, O-RAN, or 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. For ease of description, this application uses CU, CU-CP, CU-UP, DU, and RU as examples. 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.

[0081] Communication between access network devices and terminal devices follows a specific protocol layer structure. This protocol layer may include a control plane protocol layer and a user plane protocol layer. The control plane protocol layer may include at least one of the following: radio resource control (RRC) layer, packet data convergence protocol (PDCP) layer, radio link control (RLC) layer, media access control (MAC) layer, or physical (PHY) layer, etc. The user plane protocol layer may include at least one of the following: service data adaptation protocol (SDAP) layer, PDCP layer, RLC layer, MAC layer, or physical layer, etc.

[0082] The correspondence between network elements and their achievable protocol layer functions in the ORAN system can be found in Table 1 below.

[0083] Table 1

[0084] Network devices can be other devices that provide wireless communication functions for terminal devices. The embodiments of this application do not limit the specific technology or form of the network device. For ease of description, this application is not limiting.

[0085] Network equipment may also include core network equipment, such as the Mobility Management Entity (MME), Home Subscriber Server (HSS), Serving Gateway (S-GW), Policy and Charging Rules Function (PCRF), and Public Data Network Gateway (PDN gateway or P-GW) in 4th generation (4G) networks; and access and mobility management function (AMF), user plane function (UPF), or session management function (SMF) in 5G networks. Furthermore, this core network equipment may also include other core network equipment in 5G networks and next-generation networks of 5G networks.

[0086] In this application, the aforementioned network equipment may also have network nodes with AI capabilities, which can provide AI services to terminals or other network equipment. For example, it can be an AI node, computing power node, RAN node with AI capabilities, or core network element with AI capabilities on the network side (access network or core network).

[0087] In this application, the means for implementing the functions of a network device can be a network device itself, or it can be a means that enables the network device to implement those functions, such as a chip system, which can be installed in the network device. In the technical solutions provided in this application, the example of a network device being used to implement the functions of a network device is used to describe the technical solutions provided in this application.

[0088] (7) AI can enable machines to possess human-like intelligence, for example, allowing machines to use computer hardware and software to simulate certain intelligent human behaviors. To achieve artificial intelligence, machine learning methods can be used. In machine learning, machines learn (or train) a model using training data. This model represents the mapping between input and output. The learned model can be used for reasoning (or prediction), that is, it can be used to predict the output corresponding to a given input. This output can also be called the reasoning result (or prediction result).

[0089] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Unsupervised learning can also be called learning without supervision.

[0090] Supervised learning, based on collected sample values ​​and labels, uses machine learning algorithms to learn the mapping relationship between sample values ​​and labels, and then expresses this learned mapping relationship using an AI model. The process of training the machine learning model is the process of learning this mapping relationship. During training, sample values ​​are input into the model to obtain the model's predicted values, and the model parameters are optimized by calculating the error between the model's predicted values ​​and the sample labels (ideal values). After the mapping relationship is learned, it can be used to predict new sample labels. The mapping relationship learned in supervised learning can include linear or non-linear mappings. Based on the type of label, the learning task can be divided into classification tasks and regression tasks.

[0091] Unsupervised learning relies on collected sample values ​​to discover inherent patterns within the samples themselves. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping relationship from sample to sample; this is called self-supervised learning. During training, model parameters are optimized by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used for signal compression and decompression recovery applications; common algorithms include autoencoders and generative adversarial networks.

[0092] Reinforcement learning, unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have explicit "correct" action labels. The algorithm needs to interact with the environment to obtain reward signals from the environment, and then adjust its decision actions to obtain a larger reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmission power of each user based on the total system throughput feedback from the wireless network, aiming to achieve a higher system throughput. The goal of reinforcement learning is also to learn the mapping relationship between the environment state and a better (e.g., optimal) decision action. However, because the label of the "correct action" cannot be obtained in advance, the network cannot be optimized by calculating the error between the action and the "correct action." Reinforcement learning training is achieved through iterative interaction with the environment.

[0093] Neural networks (NNs) are a specific model in machine learning techniques. According to the general approximation theorem, neural networks can theoretically approximate any continuous function, thus enabling them to learn arbitrary mappings. Traditional communication systems rely on extensive expert knowledge to design communication modules, while deep learning communication systems based on neural networks can automatically discover hidden pattern structures from large datasets, establish mapping relationships between data, and achieve performance superior to traditional modeling methods.

[0094] The idea behind neural networks comes from the neuronal structure of the brain. For example, each neuron performs a weighted summation of its input values ​​and outputs the result through an activation function.

[0095] Figure 1a shows a schematic diagram of a neuron structure. Assume the neuron's input is x = [x0, x1, ..., x...]. n The weights corresponding to each input are w = [w0, w1, ..., w] n ], where n is a positive integer, w i and x i It can be any possible type, such as a decimal, an integer (e.g., 0, a positive integer, or a negative integer), or a complex number. i As x i The weights are used to assign weights to x. i Weighting is applied. The bias for the weighted sum of the input values ​​is, for example, b. Activation functions can take many forms. Suppose the activation function of a neuron is: y = f(z) = max(0, z), then the output of that neuron is: For example, if the activation function of a neuron is y = f(z) = z, then the output of that neuron is: Here, b can be any possible type, such as a decimal, an integer (e.g., 0, a positive integer, or a negative integer), or a complex number. The activation functions of different neurons in a neural network can be the same or different.

[0096] Furthermore, neural networks generally consist of multiple layers, each of which may include one or more neurons. Increasing the depth and / or width of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems. The depth of a neural network can refer to the number of layers it includes, and the number of neurons in each layer can be called the width of that layer. In one implementation, a neural network includes an input layer and an output layer. The input layer processes the received input information through neurons and passes the processing result to the output layer, which then obtains the output of the neural network. In another implementation, a neural network includes an input layer, hidden layers, and an output layer. The input layer processes the received input information through neurons and passes the processing result to the hidden layer. The hidden layer calculates the received processing result and passes the calculation result to the output layer or the next adjacent hidden layer, ultimately obtaining the output of the neural network. A neural network may include one hidden layer or multiple sequentially connected hidden layers, without limitation.

[0097] Neural networks, for example, are deep neural networks (DNNs). Depending on how the network is constructed, DNNs can include feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

[0098] Figure 1b is a schematic diagram of an FNN network. A characteristic of FNN networks is that neurons in adjacent layers are completely connected pairwise. This characteristic makes FNNs typically require a large amount of storage space, leading to high computational complexity.

[0099] CNNs are neural networks specifically designed to process data with a grid-like structure. For example, time-series data (e.g., discrete sampling along a time axis) and image data (e.g., two-dimensional discrete sampling) can both be considered grid-like data. CNNs do not use all the input information at once for computation; instead, they use a fixed-size window to extract a portion of the information for convolution operations, which significantly reduces the computational cost of model parameters. Furthermore, depending on the type of information extracted by the window (e.g., people and objects in an image represent different types of information), each window can use different convolution kernels, allowing CNNs to better extract features from the input data.

[0100] Recurrent Neural Networks (RNNs) are a type of distributed neural network (DNN) that utilizes feedback time-series information. The input to an RNN includes the current input value and its own output value from the previous time step. RNNs are well-suited for acquiring temporally correlated sequence features, and are particularly applicable to applications such as speech recognition and channel coding / decoding.

[0101] In the model training process described above, a loss function can be defined. The loss function describes the difference between the model's output value and the ideal target value. The loss function can be expressed in various forms, and there are no restrictions on its specific form. The model training process can be viewed as follows: by adjusting some or all of the model's parameters, the value of the loss function is made to be less than a threshold or to meet the target requirement.

[0102] A model can also be called an AI model, a rule, or other names. An AI model can be considered a specific method for implementing AI functions. An AI model represents the mapping relationship or function between the model's input and output. AI functions can include one or more of the following: data collection, model training (or model learning), model information dissemination, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model validation, or inference result publication, etc. AI functions can also be called AI (related) operations or AI-related functions.

[0103] The implementation process of a fully connected neural network will be described below with reference to the accompanying drawings. A fully connected neural network is also called a multilayer perceptron (MLP).

[0104] As shown in Figure 1c, an MLP consists of an input layer (left side), an output layer (right side), and multiple hidden layers (middle). Each layer of an MLP contains several nodes, called neurons. Neurons in adjacent layers are connected pairwise.

[0105] Optionally, considering neurons in two adjacent layers, the output h of the next layer's neurons is the weighted sum of all neurons x in the previous layer connected to it and passed through an activation function, which can be expressed as: h = f(wx + b).

[0106] Where w is the weight matrix, b is the bias vector, and f is the activation function.

[0107] Alternatively, the output of the neural network can be recursively expressed as: y = f n (w n f n-1 (…)+b n ).

[0108] Where n is the index of the neural network layer, n is greater than or equal to 1 and less than or equal to N, where N is the total number of layers in the neural network.

[0109] In other words, a neural network can be understood as a mapping from an input data set to an output data set. Neural networks are typically initialized randomly; the process of obtaining this mapping from random values ​​w and b using existing data is called training the neural network.

[0110] Optionally, the training method involves using a loss function to evaluate the output of the neural network.

[0111] As shown in Figure 1d, the error can be backpropagated, and the neural network parameters (including w and b) can be iteratively optimized using gradient descent until the loss function reaches its minimum, which is the "better point (e.g., the optimal point)" in Figure 1d. It can be understood that the neural network parameters corresponding to the "better point (e.g., the optimal point)" in Figure 1d can be used as the neural network parameters in the trained AI model information.

[0112] Alternatively, the gradient descent process can be represented as:

[0113] Where θ represents the parameters to be optimized (including w and b), L is the loss function, and η is the learning rate, controlling the step size of gradient descent. This represents the differentiation operation. This indicates taking the derivative of θ with respect to L.

[0114] Alternatively, the backpropagation process can utilize the chain rule for partial derivatives.

[0115] As shown in Figure 1e, the gradient of the parameters in the previous layer can be recursively calculated from the gradient of the parameters in the next layer, and can be expressed as:

[0116] Among them, w ij Let s be the weight of the connection between node j and node i. i The weighted sum of the inputs at node i.

[0117] Next, possible, non-limiting scenarios related to this application will be described. In this application, unless otherwise specified, the same or similar parts between various embodiments can be referred to each other. In the various embodiments of this application, and in the various methods / designs / implementations within each embodiment, unless otherwise specified or logically conflicting, the terminology and / or descriptions between different embodiments and between the various methods / designs / implementations within each embodiment are consistent and can be mutually referenced. The technical features in different embodiments and the various methods / designs / implementations within each embodiment can be combined to form new embodiments, methods, or implementations based on their inherent logical relationships. The following descriptions of the embodiments of this application do not constitute a limitation on the scope of protection of this application.

[0118] The introduction of artificial intelligence (AI) capabilities into wireless networks allows for significant enhancements to certain functions and network characteristics through AI-enabled methods, thereby improving the performance of the network in providing services to users. These enhancements extend beyond network optimization, resource management, and user experience improvement, encompassing key areas such as network security, energy management, and fault recovery. The following provides illustrative examples of AI-enabled scenarios that may be involved in wireless networks:

[0119] Network optimization and resource management: The application of AI technology in wireless networks can improve the efficiency of spectrum resource utilization and the balance of network load through intelligent analysis and prediction. For example, AI algorithms can monitor network traffic and interference in real time, dynamically adjust spectrum allocation, and ensure better service quality in high-load areas and time periods. Simultaneously, AI can optimize network configuration parameters, including antenna adjustment, power control, and channel selection, reducing interference and signal coverage blind spots, and improving overall network performance.

[0120] Adaptive Traffic Management and Quality of Service Assurance: Through deep analysis of network traffic using AI, wireless networks can achieve more efficient traffic management and resource allocation. AI can identify different types of traffic (such as video, voice, or data transmission) and intelligently allocate them according to real-time demand, ensuring the quality of service for critical businesses. Furthermore, AI can predict user behavior patterns, proactively adjust network resource allocation, avoid network congestion, and improve user experience.

[0121] User Experience and Personalized Services: AI technology enables wireless networks to better understand and meet user needs. By learning from user behavior and preferences, AI can provide personalized service recommendations and customized data plans. For example, based on a user's historical usage data, AI can recommend the most suitable data plan or proactively adjust service strategies when a user enters a high-traffic area to ensure the user always enjoys the best network experience.

[0122] Intelligent Energy Management: In terms of energy management, the introduction of AI can significantly reduce the energy consumption of wireless networks. By predicting network load, AI can intelligently adjust the operating mode of base stations, reducing power consumption during low-load periods and operating at full capacity during high-load periods, thereby achieving the goal of energy conservation and emission reduction. This not only reduces operating costs but also aligns with the concept of green and environmentally friendly development.

[0123] Enhanced cybersecurity: AI is equally indispensable in cybersecurity. Through real-time monitoring and analysis of network behavior, AI can quickly identify abnormal traffic and potential security threats, providing early warnings and taking defensive measures. For example, AI can automatically adjust firewall rules, isolate infected devices, and ensure the security and stability of the entire network.

[0124] Intelligent fault diagnosis and rapid recovery; AI can also significantly improve network fault diagnosis and recovery capabilities. Through real-time monitoring and data analysis, AI can quickly locate network fault points and automatically diagnose and repair them. For example, when a base station fault is detected, AI can automatically adjust the parameters of surrounding base stations to ensure uninterrupted service. Furthermore, AI can continuously optimize fault handling processes and improve fault recovery speed by learning from historical fault data.

[0125] Therefore, introducing AI capabilities into wireless networks can comprehensively enhance their implementation capabilities and service performance. From network optimization and resource management to user experience and network security, all aspects can benefit from the application of AI technology. This not only enhances the performance of existing networks but also provides strong technical support for the future development of wireless networks.

[0126] In wireless networks, the performance of AI functions may be affected by a variety of factors (such as computing resources, network environment, or data complexity), resulting in some fluctuations in the performance of AI functions, which in turn affects the stability and efficiency of the network.

[0127] To address the aforementioned problems, this application provides a communication method and related apparatus for improving the stability of wireless networks. The communication method and related apparatus provided in this application can be applied to Long Term Evolution (LTE) systems, New Radio (NR) systems, or future evolution communication systems. The communication system includes at least one network device and / or at least one terminal device.

[0128] For example, please refer to Figure 2a, which is a possible, non-limiting architectural diagram of the communication system in this application. The communication system illustrated in Figure 2a includes a network device and six terminal devices, namely terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6. In the example shown in Figure 2a, terminal device 1 is a smart teacup, terminal device 2 is a smart air conditioner, terminal device 3 is a smart gas pump, terminal device 4 is a vehicle, terminal device 5 is a mobile phone, and terminal device 6 is a printer for illustrative purposes.

[0129] As shown in Figure 2a, the entity sending the AI ​​configuration information can be a network device. The entity receiving the AI ​​configuration information can be terminal devices 1-6. In this case, the network device and terminal devices 1-6 form a communication system. In this communication system, terminal devices 1-6 can send data to the network device, and the network device needs to receive the data sent by terminal devices 1-6. At the same time, the network device can send configuration information to terminal devices 1-6.

[0130] In the communication system illustrated in Figure 2a, terminal devices 4 to 6 can also form a communication system. Terminal device 5 acts as a network device, i.e., the entity sending AI configuration information; terminal devices 4 and 6 act as terminal devices, i.e., the entities receiving AI configuration information. For example, in a vehicle-to-everything (V2X) system, terminal device 5 sends AI configuration information to terminal devices 4 and 6 respectively, and receives data sent by terminal devices 4 and 6; correspondingly, terminal devices 4 and 6 receive the AI ​​configuration information sent by terminal device 5 and send data back to terminal device 5.

[0131] Taking the communication system shown in Figure 2a as an example, in addition to performing communication-related services, different devices (including network devices and network devices, network devices and terminal devices, and / or terminal devices and terminal devices) may also perform AI-related services.

[0132] For example, please refer to Figure 2b, which is a schematic diagram of another possible, non-limiting architecture of the communication system in this application. As shown in Figure 2b, taking a network device as a base station as an example, the base station can perform communication-related services and AI-related services with one or more terminal devices, and different terminal devices can also perform communication-related services and AI-related services.

[0133] For example, please refer to Figure 2c, which is a schematic diagram of another possible, non-limiting architecture of the communication system in this application. As shown in Figure 2c, taking a terminal device including a television and a mobile phone as an example, communication-related services and AI-related services can also be performed between the television and the mobile phone.

[0134] The technical solutions provided in this application can be applied to wireless communication systems (such as the communication systems shown in Figures 2a, 2b, or 2c). For example, AI network elements can be introduced into the communication system provided in this application to implement some or all AI-related operations. AI network elements can also be called AI nodes, AI devices, AI entities, AI modules, AI models, or AI units, etc. The AI ​​network element can be built into a network element within the communication system. For example, the AI ​​network element can be an AI module built into: access network equipment, core network equipment, cloud servers, or operation, administration, and maintenance (OAM) systems to implement AI-related functions. The OAM can act as the network manager for the core network equipment and / or the network manager for the access network equipment. Alternatively, the AI ​​network element can also be an independently configured network element within the communication system. Optionally, the terminal or its built-in chip can also include an AI entity to implement AI-related functions.

[0135] Optionally, the communication system in which the technical solutions provided in this application are applied may involve AI functions including, but not limited to: channel status information (CSI) feedback enhancement, beam management enhancement, positioning accuracy enhancement, network energy saving, load balancing, or mobility optimization. These will be described in detail below.

[0136] 1. Enhanced CSI feedback.

[0137] Channel quality information (CSI) is the channel attribute of a communication link, reported by the terminal device to the network device. By reporting this information, the terminal device can select an appropriate modulation and coding scheme (MCS) to adapt to changing wireless channels. For example, the terminal device might perform channel estimation based on the received channel state information-reference signal (CSI-RS) and then feed back the CSI-RS to the network device. This information serves as input to the network device's model, enabling AI model training. Applying AI to CSI feedback enhancement can reduce overhead, improve accuracy, and enhance predictive capabilities.

[0138] CSI-RS feedback enhancement can include at least one sub-function, such as: CSI compression, CSI prediction, and CSI-RS configuration signaling reduction. CSI compression can be further divided into CSI compression in at least one domain: spatial, time, and frequency.

[0139] 2. Enhanced beam management.

[0140] The primary goal of beamforming (BM) is to discover the strongest transmit / receive beam pairs. AI-based sparse beam prediction can improve accuracy. Based on AI training and inference, it can be divided into network-side AI sparse beam prediction and terminal device-side AI sparse beam prediction. Taking terminal device-side AI sparse beam prediction as an example, the pre-trained AI model on the terminal device can be provided by the network or pre-stored on the terminal device. During the training phase, the network device scans all possible beams and then reports the transmit beam pattern to the terminal device. Once the model training is complete, the network only needs to scan a small subset of beams, and then the terminal device feeds back the inference results to the network. AI-based beam management can achieve beam prediction in, for example, the temporal and / or spatial domains, reducing overhead and latency and improving beam selection accuracy.

[0141] Beam management enhancements may include at least one sub-function, such as: beam scan matrix prediction and optimal beam prediction.

[0142] 3. Enhanced positioning.

[0143] In line-of-sight (LOS) or non-line-of-sight (NLOS) scenarios, AI-based positioning can improve positioning accuracy with a smaller number of TRP antennas. Positioning enhancement can include at least one sub-function, such as: positioning enhancement based on access network devices, positioning enhancement based on positioning management function network elements, and positioning enhancement based on terminal devices.

[0144] 4. Network energy saving.

[0145] Network energy conservation can be achieved through cell activation / deactivation, load reduction, coverage improvement, or other RAN setting adjustments. AI technology can be used to optimize energy-saving decisions by leveraging data collected within the RAN network. AI algorithms can predict energy efficiency and load status for the next cycle, which can be used to assist in cell activation / deactivation decisions to save energy. Based on the predicted load, the system can dynamically configure energy-saving strategies to maintain a balance between system performance and energy efficiency, and reduce energy consumption.

[0146] 5. Load balancing.

[0147] Load balancing can distribute the load evenly between cells and across different areas within a cell, or transfer some traffic from congested cells, or offload users across a single cell, carrier, or access standard, thereby improving network performance. Using AI models to enhance load balancing performance—such as inputting various measurements and feedback from terminal devices and network nodes, as well as historical data—can provide a higher quality user experience and increase system capacity.

[0148] 6. Mobility management.

[0149] Mobility management is a solution that ensures service continuity for mobile devices by minimizing dropped calls, radio link failures (RLFs), unnecessary handovers, and ping-pong effects. AI can enhance mobility management by, for example, reducing the probability of unexpected events, predicting device location / mobility / performance, and routing traffic.

[0150] It should be understood that the definitions of the above technical terms are merely illustrative. For example, as technology continues to develop, the scope of the above definitions may also change, and the embodiments of this application are not intended to limit the scope.

[0151] For example, an AI function may include multiple AI sub-functions.

[0152] Optionally, AI features are also called AI use cases or AI application scenarios.

[0153] Next, the communication method provided in this application will be described. Please refer to Figure 3, which is a schematic diagram of a possible implementation of the communication method in this application. It should be understood that Figure 3 uses a first communication device and other communication devices (such as a second communication device) as examples to illustrate the method, but this application does not limit the execution subject of the interaction. For example, the first communication device in Figure 3 can be a communication device (such as a terminal device or a network device), or the communication device can also be a chip, baseband chip, modem chip, system-on-chip (SoC) chip containing a modem core, system-in-package (SIP) chip, communication module, chip system, processor, logic module, or software in the communication device; the second communication device in Figure 3 can be a communication device (such as a terminal device or a network device), or the communication device can also be a chip, baseband chip, modem chip, SoC chip containing a modem core, SIP chip, communication module, chip system, processor, logic module, or software in the communication device.

[0154] As shown in Figure 3, the communication method of this application includes, but is not limited to, steps 101 to 103.

[0155] 101. The first communication device acquires the first information.

[0156] In this application, a first communication device obtains first information, which instructs the first communication device to cache service data associated with a first process. This first process is the processing of a first AI model, which is an AI model used to enable one or more functions. In other words, during the execution of the AI ​​functions enabled by the first AI model, the first communication device caches the service data based on the first information.

[0157] Optionally, the business data in this application includes at least one of the following:

[0158] The input data for the first AI model. For example, assuming the AI ​​function enabled by the first AI model is CSI compressed feedback, the input data for the first AI model can be historical CSI data, location information of the terminal device, movement information of the terminal device, location information of the base station, or antenna configuration information of the base station, etc.

[0159] The output data of the first AI model. For example, assuming that the AI ​​function enabled by the first AI model is the CSI compressed feedback function, the output data of the first AI model can be compressed CSI feedback, CSI codebook index, or predicted CSI change trend, etc.

[0160] Associated data used in conjunction with the output data of the first AI model. For example, assuming the AI ​​function enabled by the first AI model is channel estimation, and the output data of the first AI model (i.e., the channel estimation result of the first AI model) also needs to be used for subsequent channel decoding, then the data to be decoded is the aforementioned associated data. Therefore, the first communication device needs to cache the data to be decoded according to the instructions of the first information. After the first communication device performs the second processing, it performs channel decoding based on the data to be decoded and the output data of the second processing (i.e., the channel estimation result of the second processing).

[0161] For example, the AI ​​functions enabled by the first AI model may be CSI compression feedback, CSI prediction, CSI feedback enhancement, beam management enhancement, positioning enhancement, network power saving, mobility enhancement, or load balancing, etc. Alternatively, the AI ​​functions enabled by the first AI model may be other functions, which are not limited here.

[0162] In this application, the AI ​​model used to enable AI functions (such as a first AI model or a second AI model) includes, but is not limited to, neural networks, neural network models, AI neural network models, machine learning models, mathematical models, or AI processing models.

[0163] In this application, AI function may be replaced with other terms, such as AI-enabled features, AI capabilities, or AI-enabled functions.

[0164] In one possible implementation, the first communication device receives first information from the second communication device, meaning the first information obtained by the first communication device in step 101 was sent by the second communication device, enabling the first communication device to cache the service data associated with the first processing based on instructions from other communication devices. Alternatively, the first communication device determines the first information based on the first performance of the first AI model, meaning the first information obtained by the first communication device is generated by the first communication device, thus saving overhead.

[0165] Specifically, in this application, the performance of the AI ​​model (e.g., the first communication device and / or the second communication device monitoring the first AI model) can be periodically monitored by a communication device. Therefore, the "first performance" in this application and the "second performance" described below refer to the performance of the first AI model monitored by the first communication device and / or the second communication device in different periods. For example, assuming that the AI ​​function enabled by the first AI model is the CSI compressed feedback function, the first communication device and / or the second communication device can determine the performance of the first AI model based on indicators such as system throughput, channel reconstruction similarity, current environmental parameters, and dataset distribution. If the performance of the first AI model monitored by the first communication device and / or the second communication device is lower than a first threshold, it indicates that the output result of the first AI model is unreliable, and the first communication device determines the first information; or, the second communication device generates the first information and sends the first information to the first communication device. After obtaining the first information in step 101, the first communication device can cache service data based on the first information.

[0166] It should be understood that after obtaining the first information in step 101, the first communication device can cache the number of services based on the first information. The cached service data is then used by the first communication device for second processing. The second processing includes processing of a second AI model and / or processing of a non-AI model. Optionally, the first communication device deploys at least one non-AI model and / or at least one second AI model. Each non-AI model and each second AI model corresponds to a first threshold, i.e., each non-AI model and each second AI model corresponds to a first performance range. If the performance of the first AI model detected by the first communication device and / or the second communication device falls within a certain first performance range, then the first communication device and / or the second communication device uses the non-AI model or the second AI model corresponding to that first performance range as a candidate model. This candidate model is used by the first communication device to perform the second processing; that is, the second processing is the processing of the candidate model, which is one of the non-AI model and the second AI model.

[0167] In this application, the functions enabled by the non-AI model and the second AI model are the same as those enabled by the first AI model. The difference lies in the varying complexity of each non-AI model, each second AI model, and each first AI model, as well as the different performance characteristics of the functions enabled by each non-AI model, each second AI model, and each first AI model, resulting in different applicable network environments. The processing of the non-AI model can refer to processing that does not rely on artificial intelligence technology; for example, a non-AI model can be a pre-configured logical node, logical module, program code, or software. The functions enabled by the non-AI model can be replaced with other terms, such as traditional functions or conventional functions. The non-AI model can also be replaced with other terms, such as non-AI module, traditional module, or conventional module.

[0168] In one possible implementation, the second communication device sends a fifth message to the first communication device. This fifth message indicates a first data format for caching service data, and this first data format is the same as the second data format used to perform the second processing. Upon receiving the fifth message, the first communication device caches the service data in the first data format indicated by the fifth message. Specifically, since the data format (i.e., the second data format) of the service data supported by the second processing (e.g., a non-AI model or a second AI model) may differ from the data format of the service data supported by the first processing, the first communication device caches the service data in the second data format used to perform the second processing, thereby improving the efficiency of the first communication device in performing the second processing. In other words, the first communication device caches the service data in the data format supported by the aforementioned candidate model. Exemplarily, the methods by which the first communication device transforms the service data include, but are not limited to: transforming the service data in a quantized manner or normalizing the service data according to a preset range, and then caching the transformed service data. Optionally, the fifth information and the first information can be carried in the same message, message, signaling or signal, or the fifth information and the first information can be carried in different messages, messages, signaling or signals.

[0169] Optionally, the first communication device can cache service data by pre-configuring a fixed-size storage space and using a circular buffer to cache the service data in that storage space. This method is very effective in achieving efficient data management and real-time processing, especially in data scenarios that require frequent updates and access, such as performance monitoring of CSI compression feedback functions. Alternatively, the first communication device can also use other caching methods, such as least recently used (LRU) caching and least frequently used (LFU) caching, which are not limited here.

[0170] 102. The first communication device acquires the second information.

[0171] If the first communication device obtains the second information, and the second information indicates that the service data is used for the second processing, then the first communication device executes step 103.

[0172] In one possible implementation, the first communication device receives second information from the second communication device, meaning the second information acquired by the first communication device was sent by the second communication device, enabling the first communication device to cache the service data associated with the first processing based on instructions from other communication devices. Alternatively, the first communication device determines the second information based on the first performance of the first AI model, meaning the second information acquired by the first communication device was generated by the first communication device, thus saving overhead.

[0173] As can be seen from the above, in this application, the performance of the first AI model can be periodically monitored by the first communication device and / or the second communication device. If the performance of the first AI model monitored by the first communication device and / or the second communication device is lower than a second threshold, it indicates that the output of the first AI model is continuously unreliable. In this case, the first communication device determines the second information, or the second communication device generates the second information and sends it to the first communication device. After obtaining the second information, the first communication device performs the second processing on the business data.

[0174] Optionally, as described above, if the number of non-AI models and second models deployed in the first communication device is greater than or equal to 2, then after the first performance of the first AI model falls below a first threshold, the first communication device determines a candidate model from the non-AI models and second models in the manner described in step 101. Next, if the performance of the first AI model detected by the first communication device and / or the second communication device is lower than a second threshold, the first communication device processes the cached business data using the candidate model; that is, the second processing is the processing of the business data by the candidate model.

[0175] On the other hand, if the performance of the first AI model recovers (e.g., the performance of the first AI model is higher than the third threshold) or the output of the first AI model is reliable, the first communication device releases the cached business data and performs the first processing without performing the second processing.

[0176] Please refer to Figure 4, which is a schematic diagram of another possible implementation of the communication method in this application. In the scenario shown in Figure 4, the first communication device triggers the first caching of service data, but if the performance of the first AI model subsequently recovers or the output result of the first AI model is reliable, the first communication device releases the cached service data. Next, the first communication device triggers the second caching of service data, and then the first communication device obtains second information. Based on the second information, the first communication device loads the service data, thereby performing a second processing on the service data.

[0177] In one possible implementation, the service data cached by the first communication device includes data from one or more time units. The second information can be a rollback indication in a rollback operation; that is, the second information further indicates that one of the one or more time units is the rollback time, and / or, the second information further indicates that the starting position of the data in the service data used for the second processing is the rollback loading position. Then, the first communication device loads the service data according to the time indicated by the second information and / or the starting position of the data, thereby performing the second processing. Optionally, the time unit can be an hour, minute, second, frame, subframe, time slot, or symbol, etc.

[0178] 103. The first communication device performs a second processing on the service data.

[0179] The first communication device performs a second processing on the business data according to the instructions of the second information. As can be seen from the above, in this application, the second processing includes processing using a non-AI model and / or processing using a second AI model. Therefore, the first communication device can process the business data using a non-AI model, and the first communication device stops executing the functions enabled by the first AI model, that is, the first communication device reverts to the functions enabled by the non-AI model (also called traditional functions or conventional functions); or, the first communication device can process the business data using a second AI model, and the first communication device stops executing the functions enabled by the first AI model, that is, the first communication device switches the functions enabled by the first AI model to the functions enabled by the second AI model.

[0180] In this application, the first communication device can cache the service data associated with the first processing and use the service data for the second processing, thereby improving the disaster recovery capability of the first communication device in processing service data and the stability of the network.

[0181] Optionally, when the performance of the first AI model fluctuates, the communication device can process the business data through the second processing, thereby avoiding the impact of the performance fluctuation of the first AI model and improving the stability and efficiency of the network.

[0182] Please refer to Figure 5, which is a schematic diagram of another possible implementation of the communication method in this application. It should be understood that Figure 5 illustrates the method by using a first communication device and other communication devices (such as a second communication device) as examples of the execution subjects of this interaction, but this application does not limit the execution subjects of this interaction. For example, the first communication device in Figure 5 can be a communication device (such as a terminal device or a network device), or the communication device can also be a chip, baseband chip, modem chip, system-on-chip (SoC) chip containing a modem core, system-in-package (SIP) chip, communication module, chip system, processor, logic module, or software in a communication device; the second communication device in Figure 5 can be a communication device (such as a terminal device or a network device), or the communication device can also be a chip, baseband chip, modem chip, SoC chip containing a modem core, SIP chip, communication module, chip system, processor, logic module, or software in a communication device.

[0183] As shown in Figure 5, the communication method in this application is not limited to steps 201 to 205.

[0184] 201. The first communication device acquires the third information.

[0185] The first communication device needs to expend resources during the execution of the first or second processing. For example, these resources may be time-frequency resources used for the first communication device to transmit data (including one or more of service data, signals, information, and signaling), or they may be a search space used for the first communication device to monitor and control information. Therefore, the first communication device needs to acquire pre-configured resources in order to perform its various functions.

[0186] In one possible implementation, a second communication device determines a first resource for the first communication device. This first resource is used to carry data associated with the second processing (including one or more of service data, signals, information, and signaling). Then, the second communication device sends third information indicating the first resource to the first communication device; that is, the first communication device receives the third information from the second communication device. When the first communication device executes the second processing, it can use the first resource indicated by the third information to carry the data associated with the second processing (including one or more of service data, signals, information, and signaling), enabling the first communication device to transmit the data on the designated resource and thus improving the success rate of data transmission.

[0187] In one possible implementation, a first communication device determines a first resource based on one or more factors such as network configuration, channel conditions, and device capabilities. This first resource is used to carry data associated with the second processing (including one or more of service data, signals, information, and signaling). When the first communication device executes the second processing, it can use the first resource to carry the data associated with the second processing (including one or more of service data, signals, information, and signaling). Optionally, the first communication device sends third information to the second communication device, indicating the first resource so that the second communication device can transmit data associated with the second processing (including one or more of service data, signals, information, and signaling) through the first resource.

[0188] Optionally, the first resource is in an inactive state. When the first communication device performs the second process, it activates the first resource and then uses the first resource to carry data associated with the second process. Alternatively, the first communication device receives an activation command from another communication device (e.g., the second communication device), enabling the first communication device to activate the first resource and use the first resource to carry data associated with the second process.

[0189] Optionally, the first resource is already activated. When the first communication device performs the second process, it can carry the data associated with the second process without reactivating the first resource.

[0190] 202. The first communication device receives or sends the fourth information.

[0191] During the execution of the first or second processing, the first communication device needs to enable the first or second processing function through one or more auxiliary parameters. For example, these auxiliary parameters include, but are not limited to, SSB, RS, or control channel information. The reference signal can be one or more of CSI-RS, DMRS, or SRS. Optionally, the control channel information can be PDCCH information or PUCCH information, wherein the PDCCH information is used to indicate the available PDCCH, and the PUCCH information is used to indicate the available PUCCH.

[0192] Assuming the first communication device is a terminal device and the second communication device is a network device, and the first and second processing are used to enable resource allocation functions, the auxiliary parameters can be PDCCH information and PUCCH information. Specifically, the network device (second communication device) sends resource allocation instructions to the terminal device (first communication device) via the PDCCH indicated by the PDCCH information to optimize the use of time and frequency resources and improve network efficiency; the terminal device (first communication device) feeds back the uplink transmission status to the network device (second communication device) via the PUCCH indicated by the PUCCH information, so that the base station can adjust its resource allocation strategy according to the uplink transmission status to ensure reliable data transmission.

[0193] In one possible implementation, a second communication device configures auxiliary parameters for the second processing on the first communication device. Then, the second communication device sends fourth information indicating these auxiliary parameters to the first communication device; that is, the first communication device receives the fourth information from the second communication device. When the first communication device executes the second processing, it can utilize the auxiliary parameters indicated by the fourth information to assist in enabling the second processing, thereby improving the efficiency of the second processing.

[0194] In one possible implementation, the functions of the second processing are executed by the cooperation of the first and second communication devices. The first communication device determines auxiliary parameters for the second processing. Then, the first communication device sends fourth information to the second communication device, indicating the auxiliary parameters for the second processing, so that the second communication device can use these auxiliary parameters to assist in enabling the second processing, thereby improving the efficiency of the second communication device in executing the second processing.

[0195] Optionally, the fourth information includes configuration information for at least one set of auxiliary parameters used in the second processing.

[0196] In this application, the execution order between steps 201 and 202 is not limited; that is, step 201 can be executed before step 202, or step 201 can be executed after step 202.

[0197] 203. The first communication device acquires the first information.

[0198] The process of step 203 is similar to that of step 101 above. Please refer to the description of step 101 above for details. It will not be repeated here.

[0199] Steps 201 and 202 can be performed before or after step 203, and this application does not limit this.

[0200] 204. The first communication device acquires the second information.

[0201] The process of step 203 is similar to that of step 101 above. Please refer to the description of step 101 above for details. It will not be repeated here.

[0202] Please refer to Figure 6, which is a schematic diagram of a possible implementation of the second information in this application. As shown in Figure 6, the second information includes at least one of the following:

[0203] The identifier of the function enabled by the first processing and / or the identifier of the model used to implement the function indicates that the function needs to be implemented using a processing method other than the first processing (i.e., the processing of the first AI model) (e.g., the second processing in this application); for example, assuming that the current first communication device enables the CSI compressed feedback function through the first processing (i.e., the processing of the first AI model). If the second information includes the identifier of the CSI compressed feedback function and / or the identifier of the first AI model used to implement the CSI compressed feedback function, it indicates that other processing methods (e.g., the second processing in this application) are needed to implement the CSI compressed feedback function.

[0204] The identifier of the fallback level to which the second processing belongs. The first communication device deploys multiple fallback levels, each corresponding to at least one non-AI model and / or a second AI model. The identifier of the fallback level to which the second processing belongs indicates that one of the non-AI models or the second AI model corresponding to that fallback level is used to execute the second processing. Optionally, the first communication device can acquire multiple resources, including the first resource acquired in step 201 above. Each resource corresponds to a fallback level, and this resource is used to carry data associated with the non-AI model and the second AI model corresponding to that fallback level. When the second information carries the identifier of the fallback level, the first communication device carries the data associated with the second processing (including one or more of service data, signals, information, and signaling) through the resource corresponding to that fallback level.

[0205] The identifier for the second processing. This identifier can be the identifier of a non-AI model or a second AI model, indicating that the non-AI model or the second AI model is used to perform the second processing. Optionally, the first communication device can acquire multiple resources, including the first resource acquired in step 201 above. Each resource corresponds to a non-AI model or a second AI model, and this resource is used to carry data associated with the non-AI model or the second AI model. When the second information carries the identifier of a non-AI model or a second AI model, the first communication device uses the resource corresponding to the non-AI model or the second AI model to carry the data associated with the second processing (including one or more of business data, signals, information, and signaling).

[0206] The identifier for the auxiliary parameters used in the second processing (hereinafter referred to as the auxiliary parameter identifier) ​​indicates the function enabled by the auxiliary parameter corresponding to the auxiliary parameter identifier. Optionally, the first communication device may pre-acquire multiple sets of auxiliary parameters. For example, the first communication device receives fourth information, which includes configuration information for multiple sets of auxiliary parameters, each set of auxiliary parameters corresponding to an auxiliary parameter identifier. When the second information carries an auxiliary parameter identifier, the first communication device determines the auxiliary parameter corresponding to the auxiliary parameter identifier from the multiple sets of auxiliary parameters and uses the auxiliary parameter to assist the function enabled by the second processing.

[0207] In one possible implementation, the second information is carried in a Medium Access Control Element (MAC CE). Alternatively, the second information can be carried in other messages / signaling / information, such as Downlink Control Information (DCI) or RRC messages.

[0208] 205. The first communication device performs a second processing on the service data.

[0209] The process of step 205 is similar to that of step 103 mentioned above. Please refer to the description of step 103 mentioned above for details, which will not be repeated here.

[0210] Accordingly, this application also provides related apparatus for implementing the above-described scheme. Please refer to Figure 7, which is a schematic diagram of a communication device 300 provided in an embodiment of this application. This communication device 300 can realize the functions of the first communication device (or the second communication device) in the above method embodiments, and therefore can also achieve the beneficial effects of the above method embodiments. In the embodiments of this application, the communication device 300 can be the first communication device (or the second communication device), or it can be an integrated circuit or component inside the first communication device (or the second communication device), such as a chip, baseband chip, modem chip, SoC chip containing a modem core, system-in-package (SIP) chip, communication module, chip system, processor, etc.

[0211] As shown in Figure 7, the first communication device 300 includes a transceiver unit 301 and a processing unit 302. Optionally, the transceiver unit 301 may include a sending unit and a receiving unit, which are used to perform sending and receiving, respectively.

[0212] In one possible implementation, when the communication device 300 is used to execute the method performed by the first communication device in the embodiment corresponding to FIG3 or FIG5, the communication device 300 includes a transceiver unit 301 and a processing unit 302; the processing unit 302 is used to acquire first information, the first information indicating cached service data, the service data being associated with a first processing, the first processing being the processing of a first AI model; the processing unit 302 is also used to acquire second information, the second information indicating that the service data is used for a second processing; the processing unit 302 is also used to perform a second processing on the service data.

[0213] Optionally, the transceiver unit 301 is used to receive the first information and / or the second information in the embodiment corresponding to FIG3 or FIG5.

[0214] In one possible implementation, when the communication device 300 is used to execute the method performed by the second communication device in the corresponding embodiment of FIG3 or FIG5, the communication device 300 includes a transceiver unit 301 and a processing unit 302; the processing unit 302 is used to determine first information and second information; the transceiver unit 301 is used to send the first information, the first information indicating cached service data, the service data being associated with a first processing, the first processing being the processing of a first AI model; the transceiver unit 301 is also used to send the second information, the second information indicating that the service data is used for a second processing.

[0215] It should be noted that the information interaction and execution process between the modules / units in the communication device 300 are based on the same concept as the method embodiments corresponding to Figures 3 and 5 in this application. For details, please refer to the description in the method embodiments shown above in this application, which will not be repeated here.

[0216] Please refer to Figure 8, which is another schematic structural diagram of the communication device 400 provided in this application. The communication device 400 includes a logic circuit 401 and an input / output interface 402. The communication device 400 can be a chip or an integrated circuit.

[0217] In this context, the transceiver unit 301 shown in Figure 7 can be a communication interface, which can be the input / output interface 402 in Figure 8. The input / output interface 402 can include an input interface and an output interface. Alternatively, the communication interface can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.

[0218] In one possible implementation, when the device 400 is used to execute the method performed by the first communication device in FIG3 and related embodiments, the input / output interface 402 is used to receive first information and / or second information; the logic circuit 401 is used to determine the first information and the second information and perform second processing on the service data.

[0219] In one possible implementation, when the device 400 is used to execute the method performed by the second communication device in FIG3 and related embodiments, the input / output interface 402 is used to send first information, the first information indicating cached service data, the service data being associated with a first process, the first process being the processing of a first AI model; the input / output interface 402 is used to send second information, the second information indicating service data for a second process, and the logic circuit 401 is used to determine the first information and the second information.

[0220] The logic circuit 401 and the input / output interface 402 can also perform other steps performed by the first or second communication device in any embodiment and achieve corresponding beneficial effects, which will not be elaborated here.

[0221] In one possible implementation, the processing unit 302 shown in FIG7 can be the logic circuit 401 in FIG8.

[0222] Optionally, the logic circuit 401 can be a processing device, the functions of which can be partially or entirely implemented in software.

[0223] Optionally, the processing apparatus may include a memory and a processor, wherein the memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform the corresponding processing and / or steps in any of the method embodiments.

[0224] Optionally, the processing device may consist of only a processor. A memory for storing computer programs is located outside the processing device, and the processor is connected to the memory via circuitry / wires to read and execute the computer programs stored in the memory. The memory and processor may be integrated together or physically independent of each other.

[0225] Optionally, the processing device may be one or more chips, or one or more integrated circuits. For example, the processing device may be one or more field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), system-on-chips (SoCs), central processing units (CPUs), network processors (NPs), digital signal processors (DSPs), microcontroller units (MCUs), programmable logic controllers (PLDs), or other integrated chips, or any combination of the above chips or processors.

[0226] Please refer to Figure 9, which shows the communication device 500 involved in the above embodiments provided in the embodiments of this application. Specifically, the communication device 500 can be the communication device that serves as a terminal device in the above embodiments.

[0227] The present invention is a possible logical structure diagram of the communication device 500, which may include, but is not limited to, at least one processor 501 and a communication port 502.

[0228] In this context, the transceiver unit 301 shown in Figure 7 can be a communication interface, which can be the communication port 502 in Figure 9. The communication port 502 can include an input interface and an output interface. Alternatively, the communication port 502 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.

[0229] Further optionally, the device may also include at least one of a memory 503 and a bus 504. In the embodiments of this application, the at least one processor 501 is used to control the operation of the communication device 500.

[0230] Furthermore, processor 501 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc. Those skilled in the art will clearly 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.

[0231] It should be noted that the communication device 500 shown in Figure 9 can be used to implement the steps implemented by the terminal device in the aforementioned method embodiments and achieve the corresponding technical effects of the terminal device. The specific implementation of the communication device shown in Figure 9 can be referred to the description in the aforementioned method embodiments, and will not be repeated here.

[0232] Please refer to Figure 10, which is a schematic diagram of the structure of the communication device 600 involved in the above embodiments provided in the embodiments of this application. Specifically, the communication device 600 can be a communication device as a network device in the above embodiments.

[0233] The communication device 600 includes at least one processor 611 and at least one network interface 614. Optionally, the communication device further includes at least one memory 612, at least one transceiver 613, and one or more antennas 614. The processor 611, memory 612, transceiver 613, and network interface 614 are connected, for example, via a bus. In this embodiment, the connection may include various interfaces, transmission lines, or buses, etc., and this embodiment is not limited thereto. The antenna 615 is connected to the transceiver 613. The network interface 614 enables the communication device to communicate with other communication devices through a communication link. For example, the network interface 614 may include a network interface between the communication device and core network equipment, such as an S1 interface, or a network interface between the communication device and other communication devices (e.g., other network devices or core network equipment), such as an X2 or Xn interface.

[0234] In this context, the transceiver unit 301 shown in Figure 7 can be a communication interface, which can be the network interface 614 in Figure 10. The network interface 614 can include an input interface and an output interface. Alternatively, the network interface 614 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.

[0235] The processor 611 is primarily used to process communication protocols and communication data, control the entire communication device, execute software programs, and process data from the software programs, for example, to support the actions described in the embodiments of the communication device. The communication device may include a baseband processor and a central processing unit (CPU). The baseband processor is primarily used to process communication protocols and communication data, while the CPU is primarily used to control the entire terminal device, execute software programs, and process data from the software programs. The processor 611 in Figure 10 can integrate the functions of both the baseband processor and the CPU. Those skilled in the art will understand that the baseband processor and the CPU can also be independent processors interconnected via technologies such as buses. Those skilled in the art will understand that the terminal device can include multiple baseband processors to adapt to different network standards, and multiple CPUs to enhance its processing capabilities. The various components of the terminal device can be connected via various buses. The baseband processor can also be described as a baseband processing circuit or a baseband processing chip. The CPU can also be described as a central processing circuit or a central processing chip. The function of processing communication protocols and communication data can be built into the processor or stored in memory as a software program, which is then executed by the processor to implement the baseband processing function.

[0236] The memory is primarily used to store software programs and data. The memory 612 can exist independently or be connected to the processor 611. Optionally, the memory 612 can be integrated with the processor 611, for example, integrated into a single chip. The memory 612 can store program code that executes the technical solutions of the embodiments of this application, and its execution is controlled by the processor 611. The various types of computer program code being executed can also be considered as drivers for the processor 611.

[0237] Figure 10 shows only one memory and one processor. In actual terminal devices, there may be multiple processors and multiple memories. Memory can also be called storage medium or storage device, etc. Memory can be a storage element on the same chip as the processor, i.e., an on-chip storage element, or it can be a separate storage element; this application does not limit this.

[0238] Transceiver 613 can be used to support the reception or transmission of radio frequency (RF) signals between a communication device and a terminal. Transceiver 613 can be connected to antenna 615. Transceiver 613 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 615 can receive RF signals. The receiver Rx of transceiver 613 receives the RF signals from the antennas, converts the RF signals into digital baseband signals or digital intermediate frequency (IF) signals, and provides the digital baseband signals or IF signals to processor 611 so that processor 611 can perform further processing on the digital baseband signals or IF signals, such as demodulation and decoding. Furthermore, the transmitter Tx in transceiver 613 is also used to receive modulated digital baseband signals or IF signals from processor 611, convert the modulated digital baseband signals or IF signals into RF signals, and transmit the RF signals through one or more antennas 615. Specifically, the receiver Rx can selectively perform one or more stages of downmixing and analog-to-digital conversion on the radio frequency signal to obtain a digital baseband signal or a digital intermediate frequency (IF) signal. The order of these downmixing and IF conversion processes is adjustable. The transmitter Tx can selectively perform one or more stages of upmixing and digital-to-analog conversion on the modulated digital baseband signal or digital IF signal to obtain a radio frequency signal. The order of these upmixing and IF conversion processes is also adjustable. The digital baseband signal and the digital IF signal can be collectively referred to as digital signals.

[0239] The transceiver 613 can also be called a transceiver unit, transceiver, transceiver device, etc. Optionally, the device in the transceiver unit that performs the receiving function can be regarded as the receiving unit, and the device in the transceiver unit that performs the transmitting function can be regarded as the transmitting unit. That is, the transceiver unit includes a receiving unit and a transmitting unit. The receiving unit can also be called a receiver, input port, receiving circuit, etc., and the transmitting unit can be called a transmitter, transmitter, or transmitting circuit, etc.

[0240] It should be noted that the communication device 600 shown in Figure 10 can be used to implement the steps implemented by the network device in the aforementioned method embodiments and achieve the corresponding technical effects of the network device. The specific implementation of the communication device 600 shown in Figure 10 can be referred to the description in the aforementioned method embodiments, and will not be repeated here.

[0241] Please refer to Figure 11, which is a schematic diagram of the structure of the communication device involved in the above embodiments provided in the embodiments of this application.

[0242] It is understood that the communication device 700 includes, for example, modules, units, elements, circuits, or interfaces, which are appropriately configured together to execute the technical solutions provided in this application. The communication device 700 may be the terminal device or network device described above, or a component (e.g., a chip) within these devices, used to implement the methods described in the following method embodiments. The communication device 700 includes one or more processors 701. The processor 701 may be a general-purpose processor or a dedicated processor, for example, a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (e.g., a RAN node, terminal, or chip), execute software programs, and process data from the software programs.

[0243] Optionally, in one design, processor 701 may include program 703 (sometimes also referred to as code or instructions), which may be executed on processor 701 to cause communication device 700 to perform the methods described in the embodiments below. In yet another possible design, communication device 700 includes circuitry (not shown in FIG11).

[0244] Optionally, the communication device 700 may include one or more memories 702 storing a program 704 (sometimes referred to as code or instructions), which can be run on the processor 701 to cause the communication device 700 to perform the methods described in the above method embodiments.

[0245] Optionally, the processor 701 and / or memory 702 may include AI modules 707 and 708, which are used to implement AI-related functions. The AI ​​modules can be implemented through software, hardware, or a combination of both. For example, the AI ​​module may include a radio intelligence control (RIC) module. For instance, the AI ​​module may be a near real-time RIC or a non-real-time RIC.

[0246] Optionally, the processor 701 and / or memory 702 may also store data. The processor and memory may be configured separately or integrated together.

[0247] Optionally, the communication device 700 may further include a transceiver 705 and / or an antenna 706. The processor 701, sometimes referred to as a processing unit, controls the communication device (e.g., a RAN node or terminal). The transceiver 705, sometimes referred to as a transceiver unit, transceiver, transceiver circuit, or transceiver, is used to implement the transmission and reception functions of the communication device via the antenna 706.

[0248] In this context, the processing unit 302 shown in Figure 7 can be a processor 701. The transceiver unit 301 shown in Figure 7 can be a communication interface, which can be the transceiver 705 in Figure 11. The transceiver 705 can include an input interface and an output interface. Alternatively, the transceiver 705 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.

[0249] This application also provides a computer-readable storage medium for storing one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor performs the method described in the possible implementations of the first or second communication device in the foregoing embodiments.

[0250] This application also provides a computer program product (or computer program) that, when executed by a processor, executes the method described above for the possible implementation of the first or second communication device.

[0251] This application also provides a chip system including at least one processor for supporting a communication device in implementing the functions involved in the possible implementations of the communication device described above. Optionally, the chip system further includes an interface circuit that provides program instructions and / or data to the at least one processor. In one possible design, the chip system may also include a memory for storing the program instructions and data necessary for the communication device. The chip system may be composed of chips or may include chips and other discrete devices, wherein the communication device may specifically be the first communication device or the second communication device in the aforementioned method embodiments.

[0252] This application also provides a communication system, which includes a first communication device and / or a second communication device in any of the above embodiments.

[0253] It should also be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and 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 modules can be selected to achieve the purpose of this embodiment according to actual needs. In addition, in the device embodiment drawings provided in this application, the connection relationship between modules indicates that they have a communication connection, which can be implemented as one or more communication buses or signal lines.

[0254] Through the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary general-purpose hardware, or it can be implemented by special-purpose hardware including application-specific integrated circuits, special-purpose CPUs, special-purpose memory, special-purpose components, etc. Generally, any function performed by a computer program can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be diverse, such as analog circuits, digital circuits, or special-purpose circuits. However, for this application, software program implementation is more often the preferred implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, ROM, RAM, magnetic disk, or optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, training equipment, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0255] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product.

[0256] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in this application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, training device, or data center to another website, computer, training device, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0257] In the various embodiments of this application, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions between different embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

Claims

A communication method, characterized in that, include: Obtain first information, which indicates cached business data, and the business data is associated with a first process, which is the processing of a first artificial intelligence (AI) model; Obtain second information, which indicates that the business data is used for second processing; The business data is then subjected to the second processing. The method according to claim 1, characterized in that, The second processing includes processing of the second AI model, or processing of the non-AI model. The method according to claim 1 or 2, characterized in that, The acquisition of the first information includes: Receive the first information; or, The first information is determined based on the first performance of the first AI model. The method according to any one of claims 1 to 3, characterized in that, The acquisition of the second information includes: Receive the second information; or, The second information is determined based on the second performance of the first AI model. The method according to any one of claims 1 to 4, characterized in that, The method further includes: Receive or send third information, the third information being used to indicate a first resource, the first resource being used to carry the data associated with the second processing. The method according to any one of claims 1 to 5, characterized in that, The method further includes: Receive or send a fourth message, which indicates auxiliary parameters for the second processing. The method according to any one of claims 1 to 6, characterized in that, The business data includes data from one or more time units, and the second information further indicates one of the one or more time units; And / or, The second information also indicates the starting position of the data in the business data used for the second processing. The method according to any one of claims 1 to 7, characterized in that, The second information is carried in the Media Access Control (MAC) control element CE. The method according to any one of claims 1 to 8, characterized in that, The second information includes at least one of the following: The identifier of the function enabled by the first processing; The identifier of the rollback level to which the second process belongs; The identifier of the second process; or, Identifiers for auxiliary parameters used in the second process. The method according to any one of claims 1 to 9, characterized in that, The method further includes: A fifth message is received, the fifth message indicating a first data format for caching the service data, the first data format being the same as the second data format used to perform the second processing. A communication method, characterized in that, include: Send a first message, the first message indicating cached business data, the business data being associated with a first process, the first process being the processing of a first artificial intelligence (AI) model; Send a second message, which instructs the service data to be used for a second process. The method according to claim 11, characterized in that, The second processing includes processing of the second AI model, or processing of the non-AI model. The method according to claim 11 or 12 is characterized in that, The method further includes: Send a third message, the third message being used to instruct a first resource, the first resource being used to carry the data associated with the second processing. The method according to any one of claims 11 to 13 is characterized in that, The method further includes: A fourth message is sent, which indicates auxiliary parameters for the second processing. The method according to any one of claims 11 to 14, characterized in that, The business data includes data from one or more time units, and the second information further indicates one of the one or more time units; And / or, The second information also indicates the starting position of the data in the business data used for the second processing. The method according to any one of claims 11 to 15, characterized in that, The second information is carried in the Media Access Control (MAC) control element CE. The method according to any one of claims 11 to 16, characterized in that, The second information includes at least one of the following: The identifier of the function enabled by the first processing; The identifier of the rollback level to which the second process belongs; The identifier of the second process; or Identifiers for auxiliary parameters used in the second process. The method according to any one of claims 11 to 17, characterized in that, The method further includes: A fifth message is sent, the fifth message indicating a first data format for caching the service data, the first data format being the same as the second data format used to perform the second processing. A communication device, characterized in that, The communication device includes a module for performing the method as described in any one of claims 1 to 10; or, the communication device includes a module for performing the method as described in any one of claims 11 to 18. A communication device, characterized in that, The device includes at least one processor coupled to a memory; the memory is used to store computer programs or instructions, and the processor is used to execute the computer programs or instructions in the memory such that the method of any one of claims 1 to 10 is performed, or that the method of any one of claims 11 to 18 is performed. The communication device according to claim 20 is characterized in that, The communication device is a chip or chip system. A readable storage medium, characterized in that, The storage medium stores computer programs or instructions, which are executed by the communication device. Implement the method as described in any one of claims 1 to 10; or, Implement the method as described in any one of claims 11 to 18. A computer program product, characterized in 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 10; or, This causes the computer to perform the method as described in any one of claims 11 to 18.

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