Communication method and communication apparatus

Abstract

A communication method and a communication apparatus. A network device can specify, for a terminal device, configuration parameters of a generation model, so that the terminal device can, on the basis of the configuration parameters of the generation model specified by the network device, generate and report a probability distribution of channel measurement results. Alternatively, a terminal device can generate, on the basis of a generation model, a probability distribution of channel measurement results, and report to a network device the probability distribution of the channel measurement results and configuration parameters of the corresponding generation model. Compared with channel measurement results that have been transmitted over an air interface, the channel measurement results referenced by the terminal device are more accurate, so that the probability distribution of the channel measurement results generated by the terminal device on the basis of the generation model has higher accuracy. Thus, the solution of the present application helps to improve system performance.

Description

Communication method and communication device

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on December 12, 2023, with application number 202311712155.3 and invention name “A Communication Method and Communication Device”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of communications, and in particular, to a communication method and a communication device. Background Art

[0003] In downlink channel measurement based on a generative model, a network device can send a reference signal to a terminal device. The terminal device then performs channel measurement based on the reference signal and obtains a channel measurement result. The network device then calculates a probability distribution of the channel measurement result based on the channel measurement result reported by the terminal device and its local generative model, and communicates with the terminal device based on the obtained probability distribution. Currently, the probability distribution of the channel measurement result obtained by the network device is not very accurate, resulting in poor system performance. Summary of the Invention

[0004] The embodiments of the present application provide a communication method and a communication device to improve the accuracy of the probability distribution of channel measurement results and improve system performance.

[0005] In a first aspect, a communication method is provided. The method can be executed by a terminal device or by a module or unit (such as a chip, etc.) in the terminal device.

[0006] The method includes: receiving first information from an access network device, the first information being used to indicate configuration parameters of a generation model; processing a channel measurement result using the first model to obtain a probability distribution of the channel measurement result, the first model being determined based on the configuration parameters; and sending second information to the access network device, the second information being used to indicate the probability distribution.

[0007] Based on the above method, the access network device can specify the configuration parameters of the generation model to the terminal device, allowing the terminal device to generate and report the probability distribution of channel measurement results based on the configuration parameters of the generation model specified by the access network device. Compared to the channel measurement results after over-the-air transmission, the channel measurement results used by the terminal device are more accurate. Therefore, the probability distribution of the channel measurement results generated by the terminal device based on the generation model is more accurate, which helps improve system performance. Furthermore, based on the above method, the access network device and the terminal device can align the configuration parameters of the generation model. The resulting probability distribution of channel measurement results can meet the requirements of the access network device, which helps further improve system performance.

[0008] In combination with the first aspect, in some possible implementations, the method further includes: receiving third information from the access network device, where the third information is used to indicate the type of the generation model and / or the function of the generation model.

[0009] Based on the above implementation method, when there are multiple types of generation models and / or functions of generation models, the terminal device can be accurately informed of the type and / or function of the generation model configuration parameters indicated by the first information.

[0010] In combination with the first aspect or any implementation thereof, in some other possible implementations, the method further includes: receiving fourth information from the access network device, the fourth information being used to indicate a judgment criterion, and the judgment criterion being used to determine whether to report the second information.

[0011] Based on the above implementation method, the access network device can provide the terminal device with a judgment criterion for determining whether to report the above-mentioned second information. The terminal device determines whether to report the second information based on the judgment criterion provided by the access network device. The probability distribution of the channel measurement results is obtained only when the terminal device determines that the second information needs to be reported, which helps to reduce unnecessary training or fitting overhead of the generated model.

[0012] In combination with the first aspect or any implementation manner thereof, in some other possible implementation manners, the configuration parameter includes a first configuration parameter and a second configuration parameter. Alternatively, the first information indicates the first configuration parameter, and the second configuration parameter is determined based on a mapping relationship, where the mapping relationship indicates a corresponding relationship between the first configuration parameter and the second configuration parameter.

[0013] Based on the above implementation, the access network device may indicate the configuration parameters of the generation model to the terminal device directly or indirectly.

[0014] In combination with the first aspect or any implementation thereof, in some other possible implementations, the mapping relationship is configured or pre-configured.

[0015] In combination with the first aspect or any implementation manner thereof, in some other possible implementation manners, the first information indicates the first configuration parameter and the second configuration parameter, and the receiving the first information from the access network device includes: receiving the first information from the access network device via first signaling. Configuration information for the first configuration parameter is carried in a first part of the first signaling, and configuration information for the second configuration parameter is carried in a second part of the first signaling.

[0016] In combination with the first aspect or any implementation manner thereof, in some other possible implementation manners, the receiving of the first information from the access network device through the first signaling includes: receiving configuration information of the first configuration parameter from the access network device through the first part of the first signaling at a first moment, and receiving configuration information of the second configuration parameter from the access network device through the second part of the first signaling at a second moment; wherein the first moment and the second moment are the same, or the first moment and the second moment are different.

[0017] In a second aspect, a communication method is provided. The method can be executed by an access network device or by a module or unit (e.g., a chip) in the access network device. In the second aspect or its implementations, terms or features identical to those in the first aspect or its implementations can refer to the first aspect or its implementations. The technical effects of the second aspect or its implementations can refer to the technical effects of the first aspect or its implementations and are not further described in the second aspect.

[0018] The method includes: sending first information to a terminal device, where the first information is used to indicate configuration parameters of a generation model; and receiving second information from the terminal device, where the second information is used to indicate a probability distribution of a channel measurement result, and the second information is related to the configuration parameters.

[0019] In combination with the second aspect, in some possible implementations, the method further includes: obtaining multiple values ​​of the channel measurement result based on the second information; and communicating with the terminal device based on the multiple values.

[0020] Based on the above implementation method, since the access network device can obtain multiple values ​​of the channel measurement results based on the probability distribution of the channel measurement results reported by the terminal device, the number of reports can be reduced compared to reporting a single channel measurement result, which helps to reduce signaling overhead.

[0021] In combination with the second aspect or any implementation thereof, in some other possible implementations, the method further includes: sending third information to the terminal device, where the third information is used to indicate the type of the generation model and / or the function of the generation model.

[0022] In combination with the second aspect or any implementation thereof, in some other possible implementations, the method further includes: sending fourth information to the terminal device, the fourth information being used to indicate a judgment criterion, and the judgment criterion being used to determine whether to report the second information.

[0023] In conjunction with the second aspect or any implementation manner thereof, in some other possible implementation manners, the configuration parameter includes a first configuration parameter and a second configuration parameter, wherein the first information indicates the first configuration parameter and the second configuration parameter; or, the first information indicates the first configuration parameter, wherein the second configuration parameter is determined according to a mapping relationship, and the mapping relationship is used to indicate a corresponding relationship between the first configuration parameter and the second configuration parameter.

[0024] In combination with the second aspect or any implementation thereof, in some other possible implementations, the mapping relationship is configured or pre-configured.

[0025] In combination with the second aspect or any implementation manner thereof, in some other possible implementation manners, the first information indicates the first configuration parameter and the second configuration parameter, and the sending of the first information to the terminal device includes: sending the first information to the terminal device through a first signaling; wherein the configuration information of the first configuration parameter is carried in the first part of the first signaling, and the configuration information of the second configuration parameter is carried in the second part of the first signaling.

[0026] In combination with the second aspect or any implementation manner thereof, in some other possible implementation manners, sending the first information to the terminal device through the first signaling includes: sending configuration information of the first configuration parameter to the terminal device through the first part of the first signaling at a first moment, and sending configuration information of the second configuration parameter to the terminal device through the second part of the first signaling at a second moment; wherein, the first moment and the second moment are the same, or the first moment and the second moment are different.

[0027] In a third aspect, a communication method is provided, which can be executed by a terminal device or by a module or unit (such as a chip, etc.) in the terminal device.

[0028] The method includes: processing channel measurement results using a generation model to obtain a probability distribution of the channel measurement results; and sending first information and second information to an access network device, wherein the first information is used to indicate configuration parameters of the generation model, and the second information is used to indicate the probability distribution.

[0029] Based on the above method, a terminal device can generate a probability distribution of channel measurement results based on a generation model and report this probability distribution of channel measurement results and the corresponding generation model configuration parameters to the access network device. Compared to channel measurement results transmitted over the air interface, the channel measurement results used by the terminal device are more accurate. Therefore, the probability distribution of channel measurement results generated by the terminal device based on the generation model is more accurate, helping to improve system performance. Furthermore, based on the above method, the access network device and the terminal device can align the configuration parameters of the generation model. The resulting probability distribution of channel measurement results can meet the requirements of the access network device, further helping to improve system performance.

[0030] In combination with the third aspect, in some possible implementations, the method further includes: sending third information to the access network device, where the third information is used to indicate the type of the generation model and / or the function of the generation model.

[0031] Based on the above implementation method, when there are multiple types of generation models and / or functions of generation models, the access network device can be accurately informed of the type and / or function of the generation model configuration parameters indicated by the first information.

[0032] In combination with the third aspect or any implementation thereof, in some other possible implementations, the method further includes: receiving fourth information from the access network device, the fourth information being used to indicate a judgment criterion, and the judgment criterion being used to determine whether to report the first information and the second information.

[0033] Based on the above implementation method, the access network device can provide the terminal device with judgment criteria for determining whether to report the above-mentioned first information and second information. The terminal device determines whether to report the first information and the second information based on the judgment criteria provided by the access network device. When the terminal device determines that the first information and the second information need to be reported, the probability distribution of the channel measurement results is obtained, which helps to reduce unnecessary training or fitting overhead of the generated model.

[0034] In combination with the third aspect or any implementation manner thereof, in some other possible implementation manners, the configuration parameter includes a first configuration parameter and a second configuration parameter, wherein the first information indicates the first configuration parameter and the second configuration parameter; or, the first information indicates the first configuration parameter, wherein the second configuration parameter is determined according to a mapping relationship, and the mapping relationship is used to indicate a corresponding relationship between the first configuration parameter and the second configuration parameter.

[0035] In combination with the third aspect or any implementation thereof, in some other possible implementations, the mapping relationship is configured or pre-configured.

[0036] In combination with the third aspect or any implementation manner thereof, in some other possible implementation manners, the first information indicates the first configuration parameter and the second configuration parameter, and the sending of the first information to the access network device includes: sending the first information to the access network device through a first signaling; wherein the configuration information of the first configuration parameter is carried in the first part of the first signaling, and the configuration information of the second configuration parameter is carried in the second part of the first signaling.

[0037] In combination with the third aspect or any implementation manner thereof, in some other possible implementation manners, the sending of the first information to the access network device through the first signaling includes: sending configuration information of the first configuration parameter to the access network device through the first part of the first signaling at a first moment, and sending configuration information of the second configuration parameter to the access network device through the second part of the first signaling at a second moment; wherein the first moment and the second moment are the same, or the first moment and the second moment are different.

[0038] In a fourth aspect, a communication method is provided. The method can be performed by an access network device or by a module or unit (e.g., a chip) in the access network device. Terms or features in the fourth aspect or its implementations that are identical to those in the third aspect or its implementations can refer to the third aspect or its implementations. The technical effects of the fourth aspect or its implementations can refer to the technical effects of the third aspect or its implementations and are not further described in the fourth aspect.

[0039] The method includes: receiving first information and second information from a terminal device, wherein the first information is used to indicate configuration parameters of a generation model, the second information is used to indicate a probability distribution of a channel measurement result, and the second information is related to the configuration parameters; and communicating with the terminal device based on the first information and the second information.

[0040] In combination with the fourth aspect, in some possible implementations, communicating with the terminal device based on the first information and the second information includes: obtaining multiple values ​​of the channel measurement results based on the first information and the second information; and communicating with the terminal device according to the multiple values.

[0041] Based on the above implementation, since the access network device can perform multiple sampling based on the probability distribution of the channel measurement results reported by the terminal device, obtaining multiple values ​​of the channel measurement results, the number of reports can be reduced compared to reporting a single channel measurement result, thereby helping to reduce signaling overhead. Furthermore, based on the first information reported by the terminal device, the access network device can select second information with configuration parameters that meet the required accuracy for communication with the terminal device. Therefore, the access network device can communicate with the terminal device using the second information with higher accuracy, which helps to further improve system performance.

[0042] In combination with the fourth aspect or any implementation thereof, in some other possible implementations, the method further includes: receiving third information from the terminal device, wherein the third information is used to indicate the type of the generation model and / or the function of the generation model.

[0043] In conjunction with the fourth aspect or any implementation manner thereof, in some other possible implementation manners, the configuration parameter includes a first configuration parameter and a second configuration parameter, wherein the first information indicates the first configuration parameter and the second configuration parameter; or, the first information indicates the first configuration parameter, wherein the second configuration parameter is determined according to a mapping relationship, and the mapping relationship is used to indicate a corresponding relationship between the first configuration parameter and the second configuration parameter.

[0044] In combination with the fourth aspect or any implementation thereof, in some other possible implementations, the mapping relationship is configured or pre-configured.

[0045] In combination with the fourth aspect or any implementation manner thereof, in some other possible implementation manners, the first information indicates the first configuration parameter and the second configuration parameter, and the receiving of the first information sent from the terminal device includes: receiving the first information from the terminal device through a first signaling; wherein the configuration information of the first configuration parameter is carried in the first part of the first signaling, and the configuration information of the second configuration parameter is carried in the second part of the first signaling.

[0046] In combination with the fourth aspect or any implementation manner thereof, in some other possible implementation manners, the receiving of the first information from the terminal device through the first signaling includes: receiving configuration information of the first configuration parameter from the terminal device through the first part of the first signaling at a first moment, and receiving configuration information of the second configuration parameter from the terminal device through the second part of the first signaling at a second moment; wherein the first moment and the second moment are the same, or the first moment and the second moment are different.

[0047] In combination with the fourth aspect or any implementation thereof, in some other possible implementations, the method further includes: sending fourth information to the terminal device, the fourth information being used to indicate a judgment criterion, and the judgment criterion being used to determine whether to report the first information and the second information.

[0048] In combination with any one of the foregoing aspects or any implementation manner, in other implementation manners, the judgment criterion includes: a feature of the channel measurement result and a first threshold corresponding to the feature of the channel measurement result.

[0049] In combination with any one of the above aspects or any implementation methods, in other implementation methods, the characteristics of the channel measurement results include at least one of the following parameters: signal-to-noise ratio; signal-to-interference-plus-noise ratio; reference signal received power; probability distribution of the channel measurement results.

[0050] In combination with any one of the above aspects or any implementation methods, in other implementation methods, the generation model is a Gaussian mixture model, and the configuration parameters include at least one of the following information: the generation method of the Gaussian mixture model; the convergence threshold of the Gaussian mixture model; the maximum number of iterations of the Gaussian mixture model; the maximum number M of single Gaussian models included in the Gaussian mixture model, where M is a positive integer; the maximum value N of the expected value of the single Gaussian model included in the Gaussian mixture model, where N is a positive number; the maximum value A of the variance or covariance of the single Gaussian model included in the Gaussian mixture model, where A is a positive number; and the proportion of one or more single Gaussian models included in the Gaussian mixture model in the Gaussian mixture model.

[0051] In combination with any one of the above aspects or any implementation methods, in other implementation methods, the generative model is a variational autoencoder, and the configuration parameters include at least one of the following information: structural parameters of the variational autoencoder; the type of neural network used by the variational autoencoder; the input and / or output dimensions of the variational autoencoder; and the values ​​of the model parameters of the variational autoencoder.

[0052] Among them, the structural parameters of the variational autoencoder include one or more of the following: the number of neural network layers used by the variational autoencoder, the number of neurons contained in the neural network used by the variational autoencoder, parameters related to the input layer of the variational autoencoder, parameters related to the hidden layer of the variational autoencoder, or parameters related to the output layer of the variational autoencoder.

[0053] In combination with any one of the above aspects or any implementations, in other implementations, the generative model includes at least one of the following models: a Gaussian mixture model; a variational autoencoder; a generative adversarial network.

[0054] In combination with any one of the above aspects or any implementation methods, in other implementation methods, the generation model is a Gaussian mixture model, and the second information includes at least one of the following information: K expected values; K variance or covariance values; the proportion of K single Gaussian models in the Gaussian mixture model; wherein the K expectations, the K variances or covariances correspond one-to-one to the K single Gaussian models, and K is a positive integer.

[0055] In combination with any one of the above aspects or any implementation methods, in other implementation methods, the generative model is a variational autoencoder, and the second information includes at least one of the following information: the values ​​of the model parameters of the variational autoencoder; the values ​​of the probability distribution output by the variational autoencoder.

[0056] In a fifth aspect, a communication device is provided. The communication device may be a terminal device, or a device, module, circuit or chip configured and arranged in the terminal device, or a device that can be used in conjunction with the terminal device. In one design, the communication device may include a module that performs the method / operation / step / action described in the first aspect or its implementation, or a module that performs the method / operation / step / action described in the third aspect or its implementation. The module may be a hardware circuit, software, or a combination of a hardware circuit and software. In one design, the communication device may include a processing module and a communication module.

[0057] Among them, the sending module is used to execute the sending action in the method described in the first aspect or its implementation method above, and the processing module is used to execute the processing-related actions in the method described in the first aspect or its implementation method above; or, the sending module is used to execute the sending action in the method described in the third aspect or its implementation method above, and the processing module is used to execute the processing-related actions in the method described in the third aspect or its implementation method above.

[0058] In a sixth aspect, a communication device is provided. The communication device may be an access network device, or a device, module, circuit, or chip configured and arranged in the access network device, or a device that can be used in conjunction with a network device. In one design, the communication device may include a module that executes the method / operation / step / action described in the second aspect or its implementation, or a module that executes the method / operation / step / action described in the fourth aspect or its implementation. The module may be a hardware circuit, software, or a combination of a hardware circuit and software. In one design, the communication device may include a processing module and a communication module.

[0059] Among them, the receiving module is used to perform the receiving action in the method described in the second aspect or its implementation method above, and the processing module is used to perform the processing-related actions in the method described in the second aspect or its implementation method above; or, the receiving module is used to perform the receiving action in the method described in the fourth aspect or its implementation method above, and the processing module is used to perform the processing-related actions in the method described in the fourth aspect or its implementation method above.

[0060] In the seventh aspect, a communication device is provided, comprising a processing circuit and a storage medium, the storage medium storing instructions, which, when executed by the processing circuit, causes the method in the first aspect or any possible implementation of the first aspect to be implemented, or causes the method in the second aspect or any possible implementation of the second aspect to be implemented, or causes the method in the third aspect or any possible implementation of the third aspect to be implemented, or causes the method in the fourth aspect or any possible implementation of the fourth aspect to be implemented.

[0061] Optionally, the communication device may be a terminal device or an access network device.

[0062] Optionally, the communication device may be a chip applied to a terminal device or an access network device.

[0063] In an eighth aspect, a communication device is provided, comprising a processing circuit, wherein the processing circuit is used to process data and / or information so that the method in the first aspect or any possible implementation of the first aspect is implemented, or the method in the second aspect or any possible implementation of the second aspect is implemented, or the method in the third aspect or any possible implementation of the third aspect is implemented, or the method in the fourth aspect or any possible implementation of the fourth aspect is implemented. Optionally, the device may further include a memory, wherein the memory is used to store programs or instructions. Optionally, the communication device may further include a communication interface, wherein the communication interface is used to receive data and / or information and transmit the received data and / or information to the processing circuit. Optionally, the communication interface is also used to output data and / or information processed by the processing circuit. The communication interface may also be referred to as an interface circuit or a transceiver circuit. Optionally, the processing circuit is one or more processors, or all or part of the circuit in one or more processors used for processing functions.

[0064] Optionally, the communication device may be a terminal device or an access network device.

[0065] Optionally, the communication device may be a chip applied to a terminal device or an access network device.

[0066] In the ninth aspect, a chip system is provided, comprising a processing circuit, wherein the processing circuit is used to run a program or instruction so that the method in the first aspect or any possible implementation of the first aspect is implemented, or the method in the second aspect or any possible implementation of the second aspect is implemented, or the method in the third aspect or any possible implementation of the third aspect is implemented, or the method in the fourth aspect or any possible implementation of the fourth aspect is implemented. Optionally, the chip system may further include an input / output interface. Optionally, the chip system may further include a memory, wherein the memory is used to store programs or instructions. The processing circuit may also be referred to as a logic circuit. Optionally, the processing circuit is one or more processors, or all or part of the circuit in one or more processors used for processing functions.

[0067] In the tenth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes instructions, which, when executed by a processing circuit, enables the method in the first aspect or any possible implementation of the first aspect to be implemented, or enables the method in the second aspect or any possible implementation of the second aspect to be implemented, or enables the method in the third aspect or any possible implementation of the third aspect to be implemented, or enables the method in the fourth aspect or any possible implementation of the fourth aspect to be implemented.

[0068] In the eleventh aspect, a computer program product is provided, which includes computer program code or instructions. When the computer program code or instructions are executed, the method in the first aspect or any possible implementation of the first aspect is implemented, or the method in the second aspect or any possible implementation of the second aspect is implemented, or the method in the third aspect or any possible implementation of the third aspect is implemented, or the method in the fourth aspect or any possible implementation of the fourth aspect is implemented.

[0069] In the twelfth aspect, a communication system is provided, which includes a combination of one or more of the following devices: a communication device that performs the first aspect or any possible implementation of the first aspect, or a communication device that performs the second aspect or any possible implementation of the second aspect, or a communication device that performs the third aspect or any possible implementation of the third aspect, or a communication device that performs the fourth aspect or any possible implementation of the fourth aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] FIG1 is a schematic diagram of a communication system applicable to the communication method of an embodiment of the present application.

[0071] FIG2 is a schematic diagram of an artificial intelligence (AI) / machine learning (ML) network element or module.

[0072] FIG3 is a schematic diagram of another communication system applicable to the communication method of an embodiment of the present application.

[0073] FIG4 is a schematic diagram of an AI model framework.

[0074] FIG5 is a schematic flow chart of a method 300 provided in an embodiment of the present application.

[0075] FIG6 is a schematic flow chart of a method 400 provided in an embodiment of the present application.

[0076] FIG7 is a schematic flow chart of a method 500 provided in an embodiment of the present application.

[0077] FIG8 is a schematic flow chart of a method 600 provided in an embodiment of the present application.

[0078] FIG9 is a schematic flow chart of a method 700 provided in an embodiment of the present application.

[0079] FIG10 is a schematic block diagram of a communication device 1000 provided in an embodiment of the present application.

[0080] FIG11 is a schematic block diagram of a communication device 1100 provided in an embodiment of the present application. DETAILED DESCRIPTION

[0081] To facilitate understanding of the embodiments of the present application, the following explanation is made before introducing the embodiments of the present application.

[0082] "Indication" includes direct indication (also known as explicit indication) and implicit indication. Direct indication of information A refers to the inclusion of information A; implicit indication of information A refers to the indication of information A through the correspondence between information A and information B, as well as the direct indication of information B. The correspondence between information A and information B can be predefined, pre-stored, pre-burned, or pre-configured. Information C is used to determine information D, including situations where information D is determined solely based on information C or based on information C and other information. Furthermore, information C can also be used to determine information D indirectly, for example, when information D is determined based on information E, and information E is determined based on information C. "Network element A sends information A to network element B" can be understood as network element B being the destination of information A or an intermediate network element in the transmission path between the destination and the network element, and can include direct or indirect transmission of information to network element B. "Network element B receives information A from network element A" can be understood as network element A being the source of information A or an intermediate network element in the transmission path between the source and the network element, and can include direct or indirect receipt of information from network element A. Information may undergo necessary processing between the source and destination of information transmission, such as format changes, but the destination can still understand the valid information from the source. The various numerical numbers such as first, second, etc. are merely distinctions for ease of description and are not intended to limit the scope of the embodiments of this application, for example, to distinguish between different messages, different information, etc. "Pre-definition" can be implemented by pre-saving corresponding codes, tables, or other methods that can be used to indicate relevant information in the device, and this application does not limit its specific implementation. The "protocol" involved may refer to a standard protocol in the field of communications, such as the Long Term Evolution (LTE) protocol, the New Radio (NR) protocol, and related protocols used in future communication systems, but this application does not limit this. Words such as "exemplary," "for example," "exemplarily," and "as (another) example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "example" in this application should not be construed as preferred or advantageous over other embodiments or designs. The terms "including," "comprising," "having," and their variations all mean "including but not limited to," unless otherwise specifically emphasized. "Multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single or plural items.For example, at least one of a, b, and c can represent: a, or b, or c, or a and b, or a and c, or b and c, or a, b, and c. A, b, and c can each be single or multiple. Phrases such as "when," "in the case of," "if," and "if" all imply that the device will perform a corresponding action under certain objective circumstances. They do not limit the time, do not require the device to perform a judgment action during implementation, and do not imply any other limitations.

[0083] In addition, the network architecture and business scenarios described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Ordinary technicians in this field can know that with the evolution of network architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0084] A communication system to which the embodiments of the present application can be applied is described below.

[0085] The technical solutions provided in this application can be applied to various communication systems, such as: fifth generation (5G) or NR systems, LTE systems, LTE frequency division duplex (FDD) systems, LTE time division duplex (TDD) systems, wireless local area networks (WLAN) systems, satellite communication systems, future communication systems, or integrated systems of multiple systems. The technical solutions provided in this application can also be applied to device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-to-machine (M2M) communication, machine type communication (MTC), and Internet of Things (IoT) communication systems or other communication systems.

[0086] A device in a communication system can send signals to or receive signals from another device. These signals may include information, signaling, or data. The term "device" can also be replaced by an entity, network entity, communication device, communication module, node, communication node, etc. This application uses devices as an example for description. For example, a communication system may include at least one terminal device and at least one network device. A network device can send downlink signals to a terminal device, and / or a terminal device can send uplink signals to a network device.

[0087] In an embodiment of the present application, the terminal device may also be referred to as user equipment (UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device.

[0088] The terminal device may be a device that provides voice / data, such as a handheld device or vehicle-mounted device with a wireless connection function. Currently, some examples of terminals include: mobile phones, tablet computers, laptop computers, PDAs, mobile internet devices (MIDs), wearable devices, virtual reality (VR) devices, augmented reality (AR) devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in remote medical surgery, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, cellular phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), customer-premises equipment (CPE), handheld devices with wireless communication capabilities, computing devices or other processing devices connected to wireless modems, terminal devices in 5G networks or future evolved public land mobile communication networks (PLMNs), and so on. The terminal equipment in the network (PLMN), etc., is not limited to this in the embodiments of the present application.

[0089] As an example and not a limitation, in the embodiment of the present application, the terminal device may also be a wearable device. Wearable devices may also be called wearable smart devices, which are a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing, and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets and smart jewelry for vital sign monitoring.

[0090] In the embodiments of the present application, the device for realizing the function of the terminal device can be a terminal device, or a device capable of supporting the terminal device to realize the function, such as a chip system, which can be installed in the terminal device or used in combination with the terminal device. In the embodiments of the present application, the chip system can be composed of a chip, or it can include a chip and other discrete devices. In the embodiments of the present application, only the terminal device is used as an example for description, and the embodiments of the present application are not limited to the solutions of the embodiments of the present application.

[0091] The network device in the embodiments of the present application may be a device for communicating with a terminal device, and may also be referred to as an access network device or a radio access network device. For example, the network device may be a base station. The network device in the embodiments of the present application may refer to a radio access network (RAN) node (or device) that connects a terminal device to a wireless network. The term "base station" may broadly cover or be replaced by the following names, such as: NodeB, evolved NodeB (eNB), next generation NodeB (gNB), relay station, access point, transmitting and receiving point (TRP), transmitting point (TP), master station, secondary station, motor slide retainer (MSR) node, home base station, network controller, access node, wireless node, access point (AP), transmission node, transceiver node, baseband unit (BBU), remote radio unit (RRU), active antenna unit (AAU), remote radio head (RRH), central unit (CU), distributed unit (DU), radio unit (RU), positioning node, RAN intelligent controller (RIC), etc. The base station can be a macro base station, a micro base station, a relay node, a donor node or the like, or a combination thereof. The base station can also refer to a communication module, a modem or a chip used to be set in the aforementioned equipment or device. The base station can also be a mobile switching center and a device that performs the base station function in D2D, V2X, and M2M communications, a network side device in a future network, a device that performs the base station function in a future communication system, etc. The base station can support networks with the same or different access technologies. Optionally, the RAN node can also be a server, a wearable device, a vehicle or an on-board device, etc. For example, the access network device in the vehicle to everything (V2X) technology can be a road side unit (RSU). The embodiments of the present application do not limit the specific technology and specific device form adopted by the network equipment.

[0092] Base stations can be fixed or mobile. For example, a helicopter or drone can be configured to act as a mobile base station, and one or more cells can move based on the location of the mobile base station. In other examples, a helicopter or drone can be configured to act as a device that communicates with another base station.

[0093] In some deployments, the network devices mentioned in the embodiments of the present application may include a CU, a DU, or both a CU and a DU, or a control plane CU node (central unit-control plane (CU-CP)), a user plane CU node (central unit-user plane (CU-UP)), and a DU node. For example, the network devices may include a gNB-CU-CP, a gNB-CU-UP, and a gNB-DU.

[0094] In some deployments, multiple RAN nodes collaborate to assist terminals in achieving wireless access, with different RAN nodes implementing portions of the base station's functionality. For example, a RAN node can be a CU, DU, CU-CP, CU-UP, or RU. The CU and DU can be separate or included in the same network element, such as the BBU. The RU can be included in a radio frequency device or radio unit, such as an RRU, AAU, or RRH.

[0095] The RAN node may support one or more types of fronthaul interfaces, and different fronthaul interfaces correspond to DUs and RUs with different functions. If the fronthaul interface between the DU and the RU is a common public radio interface (CPRI), the DU is configured to implement one or more baseband functions, and the RU is configured to implement one or more radio frequency functions. If the fronthaul interface between the DU and the RU is another interface, relative to the CPRI, it moves part of the downlink and / or uplink baseband functions, such as, for downlink, precoding, digital beamforming (BF), or one or more of inverse fast Fourier transform (IFFT) / adding cyclic prefix (CP), from the DU to the RU for implementation, and for uplink, one or more of BF, or fast Fourier transform (FFT) / removing CP, from the DU to the RU for implementation. In one possible implementation, the interface may be an enhanced common public radio interface (eCPRI). In the eCPRI architecture, the division between DU and RU is different, corresponding to different types (category, Cat) of eCPRI, such as eCPRI Cat A, B, C, D, E, and F.

[0096] Taking eCPRI Cat A as an example, for downlink transmission, based on layer mapping, the DU is configured to implement layer mapping and one or more functions preceding it (i.e., one or more of coding, rate matching, scrambling, modulation, and layer mapping), while other functions after layer mapping (e.g., resource element (RE) mapping, digital BF, or one or more of IFFT / CP addition) are moved to the RU for implementation. For uplink transmission, based on RE demapping, the DU is configured to implement demapping and one or more functions preceding it (i.e., one or more of decoding, derate matching, descrambling, demodulation, inverse discrete Fourier transform (IDFT), channel equalization, and one or more of RE demapping), while other functions after demapping (e.g., one or more of digital BF or FFT / CP removal) are moved to the RU for implementation. It is understood that for the functional description of the DU and RU corresponding to various types of eCPRI, please refer to the eCPRI protocol and will not be repeated here.

[0097] In one possible design, the processing unit for implementing baseband functions in the BBU is called a baseband high layer (BBH) unit, and the processing unit for implementing baseband functions in the RRU / AAU / RRH is called a baseband low layer (BBL) unit.

[0098] In different systems, CU (or CU-CP and CU-UP), DU or RU may also have different names, but those skilled in the art can understand their meanings. For example, in the ORAN system, CU may also be called O-CU (Open CU), DU may also be called O-DU, CU-CP may also be called O-CU-CP, CU-UP may also be called O-CU-UP, and RU may also be called O-RU. Any unit of CU (or CU-CP, CU-UP), DU and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0099] In the embodiments of the present application, the device for implementing the functions of the network device can be a network device; it can also be a device that can support the network device to implement the functions, such as a chip system, a hardware circuit, a software module, or a hardware circuit and a software module. The device can be installed in the network device or used in conjunction with the network device. In the embodiments of the present application, only the device for implementing the functions of the network device is used as an example to illustrate, and does not constitute a limitation on the solutions of the embodiments of the present application.

[0100] The network device and / or terminal device can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; it can also be deployed on the water surface; it can also be deployed on aircraft, balloons and satellites in the air. The embodiments of this application do not limit the scenarios in which the network device and the terminal device are located. In addition, the terminal device and the network device can be hardware devices, or they can be software functions running on dedicated hardware, software functions running on general-purpose hardware, such as virtualization functions instantiated on a platform (e.g., a cloud platform), or entities including dedicated or general-purpose hardware devices and software functions. This application does not limit the specific forms of the terminal device and the network device.

[0101] FIG1 is a schematic diagram of a communication system applicable to the communication method of an embodiment of the present application. As shown in FIG1 , the communication system 100 may include at least one network device, such as the network device 110 shown in FIG1 ; the communication system 100 may also include at least one terminal device, such as the terminal device 120 and the terminal device 130 shown in FIG1 . The network device 110 and the terminal device (such as the terminal device 120 and the terminal device 130) can communicate via a wireless link. The communication devices in the communication system, for example, the network device 110 and the terminal device 120, can communicate via multi-antenna technology.

[0102] In addition, in order to support AI technology in wireless communication systems, AI nodes may also be introduced into wireless communication systems.

[0103] Optionally, the communication system also includes at least one AI node.

[0104] Optionally, the AI ​​node is deployed in one or more of the following: a network device, a terminal device, or a core network element; or the AI ​​node may be deployed separately, such as in a location other than any of the aforementioned devices. The AI ​​node may communicate with other devices in the communication system, such as one or more of the following: a network device, a terminal device, or a core network element.

[0105] Optionally, the AI ​​node is used to perform AI-related operations. As an example, the AI-related operations may include one or more of: model failure testing, model performance testing, model training testing, or data collection.

[0106] For example, a network device may forward AI model-related data reported by a terminal device to an AI node, which may then execute AI-related operations. For another example, a network device or a terminal device may forward AI model-related data to an AI node, which may then execute AI-related operations. For another example, an AI node may send the output of an AI-related operation, such as one or more of a trained neural network model, model evaluation, or test results, to the network device and / or the terminal device. For example, the AI ​​node may directly send the output of an AI-related operation to the network device and the terminal device. For another example, the AI ​​node may send the output of an AI-related operation to the terminal device via the network device. For another example, the AI ​​node may send the output of an AI-related operation to the network device via the terminal device.

[0107] It is understood that this application does not limit the number of AI nodes. For example, when there are multiple AI nodes, the multiple AI nodes can be divided based on function, such as different AI nodes are responsible for different functions.

[0108] It can also be understood that AI nodes can be independent devices, or they can be integrated into the same device to implement different functions, or they can be network elements in hardware devices, or they can be software functions running on dedicated hardware, or they can be virtualized functions instantiated on a platform (for example, a cloud platform). This application does not limit the specific form of the above-mentioned AI nodes.

[0109] Exemplarily, the AI ​​node may be an AI network element or an AI module.

[0110] Figure 2 is a schematic diagram of an AI / ML network element or module. If an AI network element is introduced, it means that the AI ​​network element corresponds to an independent network element; if an AI module is introduced, the AI ​​module can be located inside a network element. As described above, the network elements involved in the embodiments of the present application include terminal devices and network devices. An AI module can be set up inside one or more of these terminal devices or network devices, or one or more of the terminal devices or network devices can introduce corresponding AI network elements, or a combination of these two methods, which is not limited in this application.

[0111] The AI ​​module is used to implement the corresponding AI function. The AI ​​modules deployed in different network elements can be the same or different. The model of the AI ​​module can implement different functions according to different parameter configurations. The model of the AI ​​module can be configured based on one or more of the following parameters: structural parameters (such as the number of neural network layers, the width of the neural network, the connection relationship between layers, the weight of the neuron, the activation function of the neuron, or at least one of the bias in the activation function), input parameters (such as the type of input parameters and / or the dimension of input parameters), hidden layer parameters (such as the type of hidden layer parameters and / or the dimension of hidden layer parameters), or output parameters (such as the type of output parameters and / or the dimension of output parameters).

[0112] An AI module can have one or more models. A model can infer an output, which includes one or more parameters. The learning, training, or inference processes of different models can be deployed on different nodes or devices, or on the same node or device.

[0113] It should be understood that if one or more network devices or terminal devices introduce a corresponding AI network element, and the AI ​​operation is performed by the corresponding AI network element, the network device or terminal device needs to send information related to the AI ​​operation to the corresponding AI network element. For example, if a terminal device introduces a corresponding AI network element, and the AI ​​network element performs the inference operation of the AI ​​model, then after the terminal device obtains the channel measurement result, it sends the channel measurement result to the corresponding AI network element.

[0114] Figure 3 is a schematic diagram of another communication system applicable to the communication method of an embodiment of the present application. Compared to the communication system 100 shown in Figure 1, the communication system 200 shown in Figure 3 also includes an AI network element 140. AI network element 140 is used to perform AI-related operations, such as constructing a training dataset or training an AI model.

[0115] In one possible implementation, the network device 110 may send data related to the training of the AI ​​model to the AI ​​network element 140, which constructs a training data set and trains the AI ​​model. For example, the data related to the training of the AI ​​model may include data reported by the terminal device. The AI ​​network element 140 may send the results of the operations related to the AI ​​model to the network device 110, and forward them to the terminal device through the network device 110. For example, the results of the operations related to the AI ​​model may include at least one of the following: an AI model that has completed training, an evaluation result or a test result of the model, etc. Exemplarily, a portion of the trained AI model may be deployed on the network device 110, and another portion may be deployed on the terminal device. Alternatively, the trained AI model may be deployed on the network device 110. Alternatively, the trained AI model may be deployed on the terminal device.

[0116] It should be understood that Figure 3 illustrates only the example of a direct connection between AI network element 140 and network device 110. In other scenarios, AI network element 140 may also be connected to a terminal device. Alternatively, AI network element 140 may be connected to both network device 110 and a terminal device simultaneously. Alternatively, AI network element 140 may be connected to network device 110 through a third-party network element. This embodiment of the present application does not limit the connection relationship between the AI ​​network element and other network elements.

[0117] The AI ​​network element 140 may also be provided as a module in a network device and / or a terminal device, for example, in the network device 110 or the terminal device shown in FIG1 .

[0118] It should be noted that Figures 1 to 3 are simplified schematic diagrams for ease of understanding. For example, the communication system may also include other devices, such as wireless relay devices and / or wireless backhaul devices, which are not shown in Figures 1 to 3. In actual applications, the communication system may include multiple network devices and multiple terminal devices. The embodiments of the present application do not limit the number of network devices and terminal devices included in the communication system.

[0119] In order to facilitate understanding of the embodiments of the present application, several concepts or terms involved in the embodiments of the present application are briefly explained.

[0120] The concepts and terms introduced below are explained with reference to those specified in the protocol. However, this does not mean that the embodiments of this application are applicable only to existing systems. The concepts and terms involved in the embodiments of this application can be applied to future systems. Furthermore, the specific names of the concepts and terms (for example, concepts and terms describing functionality) may be adjusted as future systems develop.

[0121] 1. Artificial Intelligence (AI)

[0122] Artificial intelligence (AI) is the ability for machines to learn, accumulate experience, and solve problems that humans can solve through experience, such as natural language understanding, image recognition, and chess. AI can be understood as the intelligence exhibited by machines created by humans. Generally, AI refers to the technology that represents human intelligence through computer programs. The goals of AI include understanding intelligence by constructing computer programs that can perform symbolic reasoning or deduction.

[0123] 2. Machine Learning (ML)

[0124] Machine learning is an implementation of artificial intelligence. It's a method that empowers machines to learn, enabling them to perform tasks that are impossible to accomplish through direct programming. In practical terms, machine learning involves training models using data and then using these models to make predictions. There are many machine learning methods, such as neural networks (NNs), decision trees, and support vector machines. Machine learning theory primarily involves the design and analysis of algorithms that enable computers to learn automatically. Machine learning algorithms automatically analyze data to identify patterns and use these patterns to make predictions about unknown data.

[0125] 3. AI Model

[0126] An AI model is an algorithm or computer program that can implement AI functions. The AI ​​model characterizes the mapping relationship between the input and output of the model, or in other words, the AI ​​model is a function model that maps inputs of a certain dimension to outputs of a certain dimension, and the parameters of the function model can be obtained through machine learning training. For example, f(x)=mx2+n is a quadratic function model, which can be regarded as an AI model. m and n are parameters of the AI ​​model, and m and n can be obtained through machine learning training. For example, the AI ​​models mentioned in the embodiments below of this application are not limited to neural networks, linear regression models, decision tree models, support vector machines (SVMs), Bayesian networks, Q learning models, or other ML models.

[0127] AI model design primarily includes a data collection phase (e.g., collecting training data and / or inference data), a model training phase, and a model inference phase. It can also include an inference result application phase. In the aforementioned data collection phase, a data source is used to provide training data sets and inference data. In the model training phase, an AI model is obtained by analyzing or training the training data provided by the data source. Learning the AI ​​model through the model training node is equivalent to using the training data to learn the mapping relationship between the AI ​​model's input and output. In the model inference phase, the AI ​​model, trained in the model training phase, performs inference based on the inference data provided by the data source to obtain an inference result. This phase can also be understood as inputting the inference data into the AI ​​model and obtaining an output from the AI ​​model, which is the inference result. The inference result can indicate configuration parameters used (executed) by the execution object and / or the operations performed by the execution object. In the inference result application phase, the inference result is published. For example, the inference result can be centrally planned by an actor entity, such as an actor entity that sends the inference result to one or more actors (e.g., core network devices, access network devices, or terminal devices) for execution. For example, the execution entity can also provide feedback on the performance of the AI ​​model to the data source, facilitating subsequent update and training of the AI ​​model.

[0128] It is understood that the AI ​​model can be implemented as a hardware circuit, software, or a combination of software and hardware, without limitation. Non-limiting examples of software include: program code, program, subroutine, instruction, instruction set, code, code segment, software module, application, or software application.

[0129] 4. Neural network (NN)

[0130] Neural networks are a specific implementation of AI or machine learning. According to the universal approximation theorem, neural networks can theoretically approximate any continuous function, giving them the ability to learn arbitrary mappings.

[0131] A neural network can be composed of neural units, which can be represented by x s A neural network is a network formed by connecting many of the above-mentioned single neural units together, that is, the output of one neural unit can be the input of another neural unit. The input of each neural unit can be connected to the local receptive field of the previous layer to extract features of the local receptive field. The local receptive field can be an area composed of several neural units.

[0132] Common neural networks include 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). These network structures are all constructed based on neurons. Each neuron performs a weighted summation operation on its input values, and the weighted summation result generates an output through a nonlinear function. The weights of the weighted summation operation of neurons in the neural network and the nonlinear function are called the parameters of the neural network. Taking the neuron with max{0,x} as the nonlinear function as an example, The parameters of the neuron to be operated are weights w=[w0,…,w n ], the weighted sum bias is b, and the nonlinear function max{0,x}. The parameters of all neurons in a neural network constitute the parameters of the neural network.

[0133] 5. Training data set and inference data;

[0134] In the field of machine learning, ground truth usually refers to data that is believed to be accurate or real.

[0135] A training dataset is used to train an AI model. It may include the input to the AI ​​model, or the input and target output of the AI ​​model. A training dataset includes one or more training data. Training data may include training samples input to the AI ​​model, or the target output of the AI ​​model. The target output may also be referred to as a label, sample label, or labeled sample. A label is the true value.

[0136] In the communications field, training datasets can include simulated data collected through simulation platforms, experimental data collected in experimental scenarios, or measured data collected in actual communication networks. Because the geographical environments and channel conditions in which data are generated vary, such as indoor and outdoor locations, mobile speeds, frequency bands, or antenna configurations, the collected data can be categorized during acquisition. For example, data with the same channel propagation environment and antenna configuration can be grouped together.

[0137] Model training essentially involves learning certain characteristics from training data. When training an AI model (such as a neural network), the goal is to ensure that the model's output is as close as possible to the desired predicted value. This is done by comparing the network's predictions with the desired target values. The weight vectors of each layer of the AI ​​model are then updated based on the difference between the two. (Of course, before the first update, there's usually an initialization process, which pre-configures the parameters for each layer of the AI ​​model.) For example, if the network's prediction is too high, the weight vectors are adjusted to predict a lower value. This adjustment is repeated until the AI ​​model predicts the desired target value, or a value very close to it. Therefore, it's necessary to predefine how to compare the difference between the predicted and target values. This is known as the loss function, or objective function. These are important equations used to measure the difference between the predicted and target values. For example, a higher loss function indicates a greater difference. Therefore, training an AI model becomes a process of minimizing this loss, keeping the loss function below a threshold or ensuring that the loss function meets the target requirement. For example, the AI ​​model is a neural network, and adjusting the model parameters of the neural network includes adjusting at least one of the following parameters: the number of layers, width, weights of neurons, or parameters in the activation function of neurons of the neural network.

[0138] Inference data can be used as input to a trained AI model for inference. During the inference process, the inference data is input into the AI ​​model, and the corresponding output is the inference result.

[0139] Figure 4 shows an AI application framework.

[0140] In the aforementioned data collection phase, the data source is used to provide training datasets and inference data. In the model training phase, an AI model is obtained by analyzing or training the training data provided by the data source. The AI ​​model represents the mapping relationship between the model's input and output. Learning the AI ​​model through the model training node is equivalent to learning the mapping relationship between the model's input and output using the training data. In the model inference phase, the AI ​​model trained in the model training phase is used to perform inference based on the inference data provided by the data source, obtaining an inference result. This phase can also be understood as inputting the inference data into the AI ​​model and obtaining an output from the AI ​​model, which is the inference result. The inference result can indicate the configuration parameters used (executed) by the execution object and / or the operations performed by the execution object. In the inference result application phase, the inference result is published. For example, the inference result can be centrally planned by the execution (actor) entity, for example, the execution entity can send the inference result to one or more execution objects (e.g., access network equipment or terminal devices) for execution. Alternatively, the execution entity can provide feedback on the model's performance to the data source to facilitate subsequent model update and training.

[0141] It is understandable that a communication system may include network elements with artificial intelligence capabilities. The above-mentioned AI model design-related steps can be performed by one or more network elements with artificial intelligence capabilities. In one possible design, AI functions (such as AI modules or AI entities) can be configured in existing network elements in the communication system to implement AI-related operations, such as AI model training and / or inference. For example, the existing network element can be an access network device or a terminal device. Alternatively, in another possible design, an independent network element can be introduced into the communication system to perform AI-related operations, such as training an AI model. The independent network element can be referred to as an AI network element or an AI node, etc., and the embodiments of the present application are not limited to these names. For example, the AI ​​network element can be directly connected to the network equipment in the communication system, or it can be indirectly connected to the network equipment through a third-party network element. The third-party network element can be a core network element such as an authentication management function (AMF) network element, a user plane function (UPF) network element, an operation administration and maintenance (OAM) network element, a cloud server, or other network element, without limitation. Exemplarily, the independent network element may be deployed on one or more of the network device side, the terminal device side, or the core network side. Optionally, it may be deployed on a cloud server.

[0142] The training process of different models can be deployed in different devices or nodes, or in the same device or node. The reasoning process of different models can be deployed in different devices or nodes, or in the same device or node. Exemplarily, the model parameters of the AI ​​model may include one or more of the following structural parameters of the model (such as the number of layers of the model, and / or weights, etc.), the input parameters of the model (such as input dimension, number of input ports), or the output parameters of the model (such as output dimension, number of output ports). It can be understood that the input dimension may refer to the size of an input data. For example, when the input data is a sequence, the input dimension corresponding to the sequence may indicate the length of the sequence. The number of input ports may refer to the number of input data. Similarly, the output dimension may refer to the size of an output data. For example, when the output data is a sequence, the output dimension corresponding to the sequence may indicate the length of the sequence. The number of output ports may refer to the number of output data.

[0143] 6. Generative Model

[0144] A generative model is a model that can generate observations randomly, typically given certain implicit parameters. In machine learning, generative models can be used to directly model data (e.g., sampling data based on the probability density function of a variable) or to construct conditional probability distributions across variables. Conditional probability distributions can be generated from generative models using Bayes' theorem.

[0145] Exemplarily, the data generation step of the generative model may include the following steps:

[0146] 1) Obtain a probability distribution model of the training samples based on the training sample data and a specific generative learning method;

[0147] 2) Performing data sampling on the obtained probability distribution model to obtain newly generated data samples;

[0148] The generative model can represent the distribution of data from a statistical perspective and reflect the similarity of similar data itself.

[0149] For example, the generative model includes but is not limited to: Naive Bayes method, Markov model, Gaussian Mixture Model (GMM), and is generally based on statistics and Bayesian theory.

[0150] For another example, generative models based on deep learning concepts include, but are not limited to, variational auto-encoders (VAEs) and generative adversarial networks (GANs). For ease of understanding and description, the embodiments of this application focus on using GMMs and VAEs as examples for generative models.

[0151] 7. Gaussian Mixture Model

[0152] A Gaussian mixture model is a type of generative model. It can be viewed as a model composed of K individual Gaussian models. These K sub-models are the latent variables of the Gaussian mixture model, where K is an integer greater than or equal to 1. The sub-models of a Gaussian mixture model can also be called sub-distributions.

[0153] The definition of a single Gaussian model is:

[0154] 1) When the sample data X is one-dimensional data, the Gaussian distribution follows the following probability density function:

[0155] Among them, μ is the expectation of the data, σ is the variance of the data;

[0156] 2) When the sample data X is multidimensional data, the Gaussian distribution follows the following probability density function:

[0157] Among them, μ is the expectation of the data, Σ is the covariance of the data, and D is the dimension of the data.

[0158] The probability distribution of the Gaussian mixture model is:

[0159] Among them, in general, the complete mixed Gaussian model includes the covariance matrix, parameter mean vector and mixing weight, which can be expressed as θ, that is, That is, the expectation of each sub-model, the variance (or covariance) of each sub-model, and the proportion of each sub-model in the mixed model.

[0160] 8. Variational Autoencoder

[0161] A variational autoencoder is a type of generative model. It consists of an encoder and a decoder, trained to minimize the reconstruction error between the output data after passing through the encoder and decoder and the original input data. A variational autoencoder uses two neural networks to build two probability density distribution models: one, called the inference network, performs variational inference on the original input data to generate a variational probability distribution of latent variables; the other, called the generation network, uses the generated variational probability distribution of latent variables to reconstruct an approximate probability distribution of the original data. After encoding by the variational autoencoder, each feature of the original input data is no longer a single value but a probability distribution.

[0162] 9. Generative Adversarial Networks

[0163] Generative adversarial networks (GANs) are a typical unsupervised learning method that automatically extracts features and generates data. GANs consist of two key components: the generator (which generates data through a neural network). Its goal is to create data that is as similar as possible to the original data, thereby deceiving the discriminator. The discriminator (which uses a neural network to determine whether the data is real or machine-generated) aims to identify "fake data" created by the generator.

[0164] The essence of generative adversarial networks is to use the powerful nonlinear fitting ability of neural networks to learn the nonlinear mapping from an arbitrary prior noise distribution to the real data distribution, so that the generator has the ability to produce realistic samples.

[0165] 10. Expectation maximization (EM)

[0166] The expectation maximization algorithm is an iterative optimization strategy. Its basic idea is: first, estimate the value of the model parameters based on the given observation data, then estimate the value of the missing data based on the parameter value estimated in the previous step, and then re-estimate the parameter value based on the estimated missing data plus the previously observed data, and then iterate repeatedly until convergence and the iteration ends.

[0167] In downlink channel measurement based on a generative model, a network device can send a reference signal to a terminal device. The terminal device then performs channel measurement based on the reference signal to obtain a channel measurement result. The network device then obtains a probability distribution of the channel measurement result based on the channel measurement result reported by the terminal device and the local generative model of the network device. Based on the probability distribution of the channel measurement result, the network device can calculate the possible values ​​of multiple samples h. The network device then communicates with the terminal device based on the multiple values ​​obtained by the sampling. For example, the multiple values ​​are used as input information for the transmitter model to improve the robustness of precoding, resource allocation, constellation diagram selection, etc. However, the probability distribution of the channel measurement result obtained based on this method is not very accurate, resulting in poor system performance.

[0168] In view of this, embodiments of the present application provide a communication method and communication apparatus, in which a network device can specify configuration parameters of a generation model to a terminal device, so that the terminal device can generate and report a probability distribution of channel measurement results based on the configuration parameters of the generation model specified by the network device. Alternatively, the terminal device can generate a probability distribution of channel measurement results based on the generation model and report the probability distribution of the channel measurement results and the corresponding configuration parameters of the generation model to the network device. Compared to the channel measurement results after transmission over the air interface, the channel measurement results used by the terminal device are more accurate, and thus the probability distribution of the channel measurement results generated by the terminal device based on the generation model is more accurate. Therefore, the solution of the present application helps improve system performance. Furthermore, based on the solution of the present application, the network device and the terminal device can align the configuration parameters of the generation model. The probability distribution of the channel measurement results obtained based on this can meet the requirements of the network device, helping to further improve system performance. In addition, because the network device can perform multiple sampling based on the probability distribution of the channel measurement results reported by the terminal device to obtain multiple values ​​of the channel measurement results, the number of reports can be reduced compared to reporting a single channel measurement result, thereby helping to reduce signaling overhead.

[0169] The following describes the method embodiment of the present application by taking the network device as an access network device as an example.

[0170] It should be noted that in the embodiments of the present application, the training / fitting of the model occurs on the terminal device side, and the model reasoning / use occurs on the access network device side, or the training / fitting of the model occurs on the over the top (OTT) or third-party device or cloud device side, and the model reasoning / use occurs on the OTT or third-party device or cloud device side. This application does not limit this. The following is an example of the various embodiments of the present application being executed by terminal devices and access network devices. Unless otherwise specified, "terminal device" or "access network device" may refer to the terminal device or access network device itself, or may refer to a device that can support the terminal device or access network device to realize its functions. For the sake of convenience, the following description will uniformly use terminal devices or access network devices. It should be noted that in the embodiments of the present application, the terminal device may be a network element for generating model training or fitting, and the access network device may be a network element for generating model reasoning or use.

[0171] FIG5 is a schematic flow chart of a method 300 provided in an embodiment of the present application.

[0172] In method 300, the access network device specifies configuration parameters of a generation model to the terminal device, and the terminal device generates and reports a probability distribution of a channel measurement result based on the configuration parameters of the generation model specified by the access network device. Method 300 includes at least part of the following content.

[0173] In step 301, an access network device sends first information to a terminal device. Correspondingly, the terminal device receives the first information from the access network device.

[0174] The first information is used to indicate the configuration parameters of the generation model. The description of the generation model can be referred to above and will not be described in detail.

[0175] The embodiments of the present application do not limit the type of the generative model. Exemplarily, the generative model of the embodiments of the present application may include at least one of the following models: a Gaussian mixture model, a variational autoencoder, or a generative adversarial network.

[0176] For different generation models, their configuration parameters may be different.

[0177] Taking the Gaussian mixture model as an example, its configuration parameters may include at least one of the following information:

[0178] 1) Method for generating Gaussian mixture models: such as the expectation maximization algorithm (EM) mentioned above.

[0179] 2) Convergence threshold of Gaussian mixture model: It can refer to the convergence threshold in the process of fitting the Gaussian mixture model.

[0180] 3) The maximum number of iterations of the Gaussian mixture model: This may refer to the maximum number of iterations in the process of fitting the Gaussian mixture model.

[0181] For example, if the upper limit of the number of iterations is 50, it means that during the training or fitting of the Gaussian mixture model by the terminal device, the number of iterations is less than or equal to 50. For example, the number of iterations can be 10, 20, or 50. It should be understood that, to a certain extent, the greater the number of iterations, the better the convergence effect.

[0182] 4) The maximum number M of single Gaussian models included in the Gaussian mixture model, where M is a positive integer.

[0183] For example, the upper limit of the number of single Gaussian models included in the Gaussian mixture model is 5, which means that the number of single Gaussian models included in the Gaussian mixture model is less than or equal to 5, that is, M≤5. For example, the number of single Gaussian models included in the Gaussian mixture model can be 2 or 3.

[0184] 5) The maximum value N of the expected value of the single Gaussian model included in the Gaussian mixture model, where N is a positive number.

[0185] Exemplarily, assuming that the Gaussian mixture model includes three single Gaussian models, the expected values ​​of these three single Gaussian models are all less than or equal to N.

[0186] 6) The maximum value A of the variance or covariance of the single Gaussian model included in the Gaussian mixture model, where A is a positive number.

[0187] For example, assuming that the Gaussian mixture model includes three single Gaussian models, the variances or covariances of the three single Gaussian models are all less than or equal to A.

[0188] 7) The proportion of one or more single Gaussian models included in the Gaussian mixture model.

[0189] For example, assuming that the Gaussian mixture model includes three single Gaussian models, the proportion of these three single Gaussian models in the entire GMM is less than or equal to X.

[0190] 8) Model parameters of Gaussian mixture model.

[0191] Exemplarily, the model parameters of the Gaussian mixture model include one or more of the following: the expectation of the K single Gaussian models contained in the Gaussian mixture model, the variance or covariance of the K single Gaussian models, and the proportion of the K single Gaussian models in the GMM. For specific explanations, please refer to the relevant description in the term explanation section.

[0192] Taking the generative model as a variational autoencoder as an example, its configuration parameters may include at least one of the following information:

[0193] 1) The value of the model parameters of the variational autoencoder

[0194] The model parameters of the variational autoencoder include one or more of the following: neuron weights, neuron activation functions, or biases in neuron activation functions, where the bias in the activation function can also be referred to as the bias of the neural network. It should be understood that the model parameters of the variational autoencoder refer to pre-trained model parameters, i.e., the terminal device can determine the pre-trained variational autoencoder based on the model parameters of the VAE.

[0195] For example, suppose the input of a neuron is x = [x0, x1, ..., x n ], the corresponding weights are w=[w,w1,…,w n ], the bias of the weighted sum is b. Among them, b can be an integer, a decimal, or a complex number. The form of the activation function can be diversified. For example, if the activation function of a neuron is: y = f(z) = max(0,z), then the output of the neuron is: y = For another example, assuming that the activation function of a neuron is: y = f(z) = z, then the output of the neuron is: The activation functions of different neurons in a neural network can be the same or different.

[0196] 2) Structural parameters of variational autoencoder

[0197] Exemplarily, the structural parameters of the variational autoencoder include one or more of the following: the number of neural network layers used by the variational autoencoder, the number of neurons contained in the neural network used by the variational autoencoder, parameters related to the input layer of the variational autoencoder, i.e., input parameters, parameters related to the hidden layer of the variational autoencoder, or parameters related to the output layer of the variational autoencoder, i.e., output parameters. For specific explanations, please refer to the description related to the above AI model.

[0198] It should be understood that the neural network used in the variational autoencoder may include a multi-layer structure, and each layer may include one or more logical judgment units, which may be called neurons. For example, a neural network includes an input layer and an output layer. The input layer of the neural network processes the received input through neurons and passes the result to the output layer, and the output layer obtains the output result of the neural network. For another example, a neural network includes an input layer, a hidden layer, and an output layer. The input layer of the neural network processes the received input through neurons and passes the result to the middle hidden layer. The hidden layer then passes the calculation result to the output layer or the adjacent hidden layer, and finally the output layer obtains the output result of the neural network. A neural network may include one or more hidden layers connected in sequence, without limitation.

[0199] For example, the number of neural network layers used by a variational autoencoder can be referred to as the depth of the neural network. Increasing the depth of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems.

[0200] For example, the neural network used in a variational autoencoder has a multi-layered structure, where the number of neurons in each layer is referred to as the layer width. Increasing the width of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems.

[0201] For example, the input dimension of a variational autoencoder can refer to the size of an input data set. For example, when the input data is a sequence, the input dimension corresponding to the sequence can indicate the length of the sequence. The output dimension of a variational autoencoder can refer to the size of an output data set. For example, when the output data is a sequence, the output dimension corresponding to the sequence can indicate the length of the sequence. In other words, a variational autoencoder can represent the mapping relationship between the input and output of a model, or a variational autoencoder is a function model that maps inputs of a certain dimension to outputs of a certain dimension.

[0202] 3) Type of neural network used by variational autoencoders

[0203] Exemplarily, the type of neural network used by the variational autoencoder may be a deep neural network DNN or other neural network, wherein the DNN may include one or more of the following: a feedforward neural network FNN, a convolutional neural network CNN, or a recurrent neural network RNN.

[0204] When the generative model is a generative adversarial network, its configuration parameters can refer to the configuration parameters of the variational autoencoder and will not be described in detail.

[0205] It should be noted that the configuration parameters indicated in the first information, i.e., the configuration parameters indicated by the access network device, are recommended configuration parameters. The terminal device can select appropriate values ​​for the configuration parameters based on its own capabilities. For example, the maximum number of iterations of the Gaussian mixture model indicated in the first information is 50, and the terminal device determines the number of iterations to be 40 based on its own capabilities.

[0206] It should also be noted that the configuration parameters may be parameters required for configuring the corresponding generation model. The information included in the above configuration parameters is only an example. In fact, the configuration parameters may include more or fewer parameters without limitation.

[0207] In an embodiment of the present application, the first information may directly or indirectly indicate the configuration parameters of the generation model. Taking the case where the configuration parameters of the generation model include the first configuration parameter and the second configuration parameter as an example, the first information may include configuration information indicating the first configuration parameter and configuration information indicating the second configuration parameter, that is, the first information directly indicates the first configuration parameter and the second configuration parameter. Alternatively, the first information includes configuration information indicating the first configuration parameter but does not include configuration information indicating the second configuration parameter, and the second configuration parameter may be determined based on a mapping relationship, wherein the mapping relationship is used to indicate the correspondence between the first configuration parameter and the second configuration parameter, that is, the first information directly indicates the first configuration parameter and indirectly indicates the second configuration parameter. This implementation method can save signaling overhead. In other words, the configuration information in the embodiment of the present application may indicate all the configuration parameters of the generation model or may indicate part of the configuration parameters of the generation model, and this application does not limit this.

[0208] In the present application, the mapping relationship between the first configuration parameter and the second configuration parameter can be predefined, and the predefinition can include pre-definition, such as protocol definition; or, the mapping relationship can be configured or pre-configured through signaling, and the pre-configuration can be implemented by pre-saving the corresponding code, table or other methods that can be used to indicate relevant information in the device. This application does not limit its specific implementation method.

[0209] Optionally, the mapping relationship can exist in the form of a table, function, text, or string, such as for storage or transmission.

[0210] Below, the mapping relationship between the first configuration parameter and the second configuration parameter is exemplified in the form of a table. As shown in Table 1, taking GMM as an example, assuming that the configuration information is used to indicate the first configuration parameter, that is, the maximum number of single Gaussian models contained in the GMM is M=5, and the generation method of the GMM is the EM algorithm, then according to the mapping relationship shown in Table 1, the convergence threshold p=0.01 of the GMM and the maximum number of iterations of the GMM are 100 can also be determined. Based on these configuration parameters, a specific GMM can be fitted. Taking VAE as an example, assuming that the configuration information is used to indicate the second configuration parameter, that is, the number of neural network layers used by the VAE is 5, and the number of neurons contained in the neural network used by the VAE is 100, then according to the mapping relationship shown in Table 1, it can also be determined that the neural network used by the VAE is DNN. Based on these configuration parameters, a specific VAE can be obtained.

[0211] Optionally, this application does not limit the number of first configuration parameters and second configuration parameters corresponding to each generation model in Table 1.

[0212] Table 1

[0213] It should be understood that the mapping relationship between the first configuration parameter and the second configuration parameter of the GMM shown in Table 1 above, and the mapping relationship between the first configuration parameter and the second configuration parameter of the VAE, can be implemented independently or in combination. For example, a row corresponding to the GMM and a row corresponding to the VAE in Table 1 can be respectively reflected in two tables, and this application is not limited to this.

[0214] It should be understood that Table 1 above is merely an example provided for ease of understanding and should not constitute any limitation to the technical solution of the present application.

[0215] In addition, it is understandable that the configuration parameters not indicated may be predefined by the protocol or obtained in other ways, which is not limited here.

[0216] The embodiments of the present application do not limit the manner in which the first information is sent. In one possible implementation, the access network device may send the first information through the first signaling, and accordingly, the terminal device receives the first information through the first signaling, and different configuration parameters of the generation model may correspond to different parts of the first signaling. Taking the example in which the configuration parameters of the generation model include the first configuration parameter and the second configuration parameter, and the first information indicates the first configuration parameter and the second configuration parameter, the configuration information indicating the first configuration parameter in the first information is carried in the first part of the first signaling, and the configuration information indicating the second configuration parameter in the first information is carried in the second part of the first signaling.

[0217] The embodiments of the present application do not limit the sending method of the multiple parts of the first signaling. The multiple parts of the first signaling can be sent at the same time or at different times without limitation. Taking the first information indicating the first configuration parameter and the second configuration parameter as an example, the access network device can send configuration information indicating the first configuration parameter to the terminal device through the first part of the first signaling at the first moment, and send configuration information indicating the second configuration parameter to the terminal device through the second part of the first signaling at the second moment. Accordingly, the terminal device receives the configuration information indicating the first configuration parameter from the access network device through the first part of the first signaling at the first moment, and receives the configuration information indicating the second configuration parameter from the access network device through the second part of the first signaling at the second moment. The first moment and the second moment are the same, or the first moment and the second moment are different.

[0218] Optionally, in step 302, the access network device sends third information to the terminal device, and accordingly, the terminal device receives the third information from the access network device.

[0219] The third information is used to indicate the type of the generated model and / or the function of the generated model.

[0220] The type of the generative model can be any one of the following types: a Gaussian mixture model, a variational autoencoder, or a generative adversarial network. There are multiple ways for the third information to indicate the type of the generative model. For example, the third information can include an identifier or number of the type of the generative model.

[0221] The function of the generation model may refer to what the generation model is used for. There are various ways in which the third information indicates the function of the generation model. For example, the third information may indicate the function of the generation model by indicating the input parameters and / or output parameters of the generation model. There are also various ways in which the third information may indicate the input parameters and / or output parameters of the generation model. For example, the third information may include identifiers or numbers of the input parameters and / or output parameters of the generation model.

[0222] Step 302 is an optional step. For example, when there is only one default generation model type and / or generation model function, or when the generation model type and / or generation model function have been agreed in advance between the access network device and the terminal device, 302 may not be executed.

[0223] It should be noted that the first information and the third information can be transmitted through the same signaling or through different signaling, without limitation. When the first information and the third information can be transmitted through the same signaling, step 301 and step 302 can be one step.

[0224] In step 303 , the terminal device processes the channel measurement result using the first model to obtain a probability distribution of the channel measurement result.

[0225] The first model is determined based on the configuration parameters indicated by the first information.

[0226] Taking the configuration parameters of the Gaussian mixture model indicated by the first information as an example, the first model is a Gaussian mixture model, and the Gaussian mixture model can be a probability density function as described in the term explanation section. Among them, the number K of single Gaussian models included in the Gaussian mixture model can be determined based on M in the configuration parameters, the K expected values ​​in the first model, the values ​​of the K variances or covariances, and the values ​​of the proportions of the K single Gaussian models in the Gaussian mixture model are unknown quantities, and K is a positive integer. The process in which the terminal device uses the first model to process the channel measurement results to obtain the probability distribution of the channel measurement results is the process of determining the values ​​of the unknown quantities in the Gaussian mixture model based on the channel measurement results. The generation method, convergence threshold or number of iterations used in the process of determining the unknown quantities in the Gaussian mixture model based on the channel measurement results are determined based on the first information.

[0227] Taking the configuration parameters of the variational autoencoder indicated by the first information as an example, the first model is a variational autoencoder, and at least one of the structural parameters, input and / or output dimensions, neural network type, number of neural network layers, number of neurons contained in the neural network, or values ​​of the model parameters of the variational autoencoder is determined based on the configuration parameters indicated by the first information.

[0228] Optionally, when the terminal device also receives third information, the first model is determined based on the first information and the third information.

[0229] The channel measurement result refers to the result obtained after performing channel estimation on the reference signal, that is, the frequency domain channel estimation result H.

[0230] Based on different first models, the types of probability distributions of the channel measurement results obtained by the terminal device are also different. Taking the first model as a Gaussian mixture model as an example, the probability distribution of the channel measurement results obtained by the terminal device is a Gaussian probability distribution. The Gaussian probability distribution can refer to the probability density function after all the values ​​of the unknown quantities are determined. Taking the first model as a variational autoencoder as an example, the probability distribution of the channel measurement result obtained by the terminal device is a variational probability distribution.

[0231] It should be noted that when the terminal device uses the first model to process the channel measurement results to obtain the probability distribution of the channel measurement results, it may be to process one or more channel measurement results to obtain the probability distribution of the channel measurement results. Among them, the multiple channel measurement results may be multiple channel measurement results obtained by measuring the channel between the terminal device and the access network device multiple times, or multiple channel measurement results between multiple terminal devices and the access network device, or a combination of the two. Taking the first model as a Gaussian mixture model as an example, the terminal device can perform fitting based on multiple channel measurement results to determine the value of the unknown quantity in the Gaussian mixture model. Taking the first model as a variational autoencoder as an example, the terminal device can obtain a variational probability distribution based on a pre-trained variational autoencoder and a channel measurement result.

[0232] In some embodiments, the terminal device also obtains a channel measurement result. The embodiments of the present application do not limit the manner in which the terminal device obtains the channel measurement result. In one possible implementation, the access network device sends a reference signal to the terminal device, and the terminal device measures the reference signal to obtain a channel measurement result, wherein the reference signal can be any one of the following signals: the reference signal can include a channel state information reference signal (CSI-RS), a synchronization signal / physical broadcast channel block (SSB), or a demodulation reference signal (DMRS). In another possible implementation, the channel measurement result can also be obtained by measuring the reference signal by other devices or network elements (such as a network element that performs channel measurement, or a channel measurement network element, or other devices such as a reference signal measurement node), and the terminal device receives the channel measurement result from other devices or network elements.

[0233] Step 304: The terminal device sends the second information to the access network device. Correspondingly, the access network device receives the second information from the terminal device.

[0234] The second information is used to indicate the probability distribution of the channel measurement result.

[0235] For probability distributions obtained from different first models, the second information is also different.

[0236] Taking the first model as a Gaussian mixture model as an example, the probability distribution of the channel measurement result is a Gaussian probability distribution. The second information may include at least one of the following information: K expected values, K variance or covariance values, or the proportion of K single Gaussian models in the Gaussian mixture model. The K expectations, K variances, or covariances correspond one-to-one to the K single Gaussian models.

[0237] Taking the first model as a variational autoencoder as an example, the probability distribution of the channel measurement result is a variational probability distribution, and the second information may include at least one of the following information: the value of the model parameter of the variational autoencoder, or the value of the probability distribution output by the variational autoencoder.

[0238] It should be noted that in an embodiment of the present application, the terminal device can obtain the first information from the access network device before processing the channel measurement results using the first model each time, or it can process the channel measurement results based on the same first information within a period of time, without limitation.

[0239] Thus, based on method 300, the access network device can specify the configuration parameters of the generation model to the terminal device, allowing the terminal device to generate and report a probability distribution of channel measurement results based on the configuration parameters of the generation model specified by the access network device. Compared to channel measurement results transmitted over the air interface, the channel measurement results used by the terminal device are more accurate. Therefore, the probability distribution of channel measurement results generated by the terminal device based on the generation model is more accurate, which helps improve system performance. Furthermore, based on method 300, the access network device and the terminal device can align the configuration parameters of the generation model. The resulting probability distribution of channel measurement results can meet the requirements of the access network device, which helps further improve system performance.

[0240] Optionally, in some other embodiments, after receiving the second information from the terminal device, method 300 may further include step 305. Step 305 is as follows.

[0241] Step 305: The access network device communicates with the terminal device based on the second information.

[0242] Exemplarily, the access network device obtains multiple values ​​of the channel measurement result based on the second information, and communicates with the terminal device based on the multiple values, such as using the multiple values ​​as input information for a transmitter (e.g., a robust transmitter) model to improve the robustness of precoding, resource allocation, constellation selection, etc. Exemplarily, one implementation manner in which the access network device obtains the multiple values ​​of the channel measurement result based on the second information is as follows: the access network device samples multiple possible values ​​of the channel measurement result based on a probability distribution indicated by the second information.

[0243] Based on the above embodiment, since the access network device can perform multiple samplings based on the probability distribution of the channel measurement results reported by the terminal device to obtain multiple values ​​of the channel measurement results, compared to reporting a single channel measurement result, the number of reports can be reduced, thereby helping to reduce signaling overhead.

[0244] Optionally, in other embodiments, method 300 may further include: the terminal device sending the channel measurement result to the access network device, and the access network device correspondingly receiving the channel measurement result from the terminal device. In this case, when obtaining multiple values ​​of the channel measurement result, the access network device may refer to the channel measurement result reported by the terminal device to obtain multiple values ​​near the channel measurement result reported by the terminal device.

[0245] Optionally, in other embodiments, before the terminal device uses the first model to process the channel measurement results to obtain the probability distribution of the channel measurement results, the access network device may also provide the terminal device with a judgment criterion for determining whether to report the above-mentioned second information. The terminal device determines whether to report the second information based on the judgment criterion provided by the access network device. That is, before step 303, method 300 may also include steps 306 and 307. Steps 306 and 307 are as follows.

[0246] Step 306: The access network device sends the fourth information to the terminal device. Correspondingly, the terminal device receives the fourth information from the access network device.

[0247] The fourth information is used to indicate a judgment criterion, and the judgment criterion is used to determine whether to report the second information.

[0248] The embodiments of the present application do not limit the implementation of the judgment criterion. In one possible implementation, the judgment criterion includes: a characteristic of the channel measurement result and a first threshold corresponding to the characteristic of the channel measurement result. One meaning of the judgment criterion is: when the change in the characteristic of the channel measurement result exceeds the first threshold corresponding to the characteristic of the channel measurement result, the second information is reported; when the change in the characteristic of the channel measurement result does not exceed the first threshold corresponding to the characteristic of the channel measurement result, the second information is not reported. The characteristic of the channel measurement result can also be referred to as a characteristic quantity or measurement quantity of the channel measurement result.

[0249] In one possible implementation, the characteristics of the channel measurement result include at least one of the following parameters:

[0250] 1) Signal to noise ratio (SNR);

[0251] 2) Signal to interference plus noise ratio (SINR);

[0252] 3) Reference signal received power (RSRP);

[0253] 4) Probability distribution of channel measurement results.

[0254] Among them, the probability distribution of the channel measurement results can refer to the data distribution of the channel measurement results, which can include one or more of the following situations: the latent variable variational probability distribution of the data after variational inference, the probability distribution of the data after Gaussian mixture model fitting, or the potential distribution of the data obtained through adversarial learning.

[0255] Step 307: The terminal device determines to report the second information based on the acquired channel measurement result and the received judgment criterion.

[0256] Exemplarily, the terminal device determines, based on the acquired channel measurement results, that a change in a characteristic of the channel measurement results exceeds a first threshold value indicated by the judgment criteria. In this case, the terminal device determines that the probability distribution of the channel measurement results needs to be updated to the access network device, i.e., determines to report the second information.

[0257] In another case, the terminal device determines, based on the acquired channel measurement results, that the change in the characteristics of the channel measurement results does not exceed the first threshold indicated by the judgment criteria, and the terminal device determines that there is no need to update the probability distribution of the channel measurement results to the access network device, that is, it determines not to report the second information.

[0258] Based on the above embodiment, the access network device can provide the terminal device with a judgment criterion for determining whether to report the above-mentioned second information. The terminal device determines whether to report the second information based on the judgment criterion provided by the access network device. The probability distribution of the channel measurement results is obtained only when the terminal device determines that the second information needs to be reported, which helps to reduce unnecessary training or fitting overhead of the generated model.

[0259] FIG6 is a schematic flow chart of a method 400 provided in an embodiment of the present application.

[0260] In method 400, a terminal device generates a probability distribution of channel measurement results based on a generation model and reports the probability distribution of the channel measurement results and corresponding configuration parameters of the generation model to an access network device. Method 400 includes at least part of the following content.

[0261] In step 401, the terminal device processes the channel measurement result using the generative model to obtain the probability distribution of the channel measurement result.

[0262] Step 401 may refer to step 303 , but differs from step 303 in that the configuration parameters of the generated model are determined by the terminal device itself.

[0263] Step 402: The terminal device sends first information and second information to the access network device. Correspondingly, the access network device receives the first information and second information from the terminal device.

[0264] The first information indicates the configuration parameters of the generation model. The configuration parameters of the generation model indicated by the first information are the configuration parameters of the generation model used to obtain the probability distribution of the channel measurement results, which differs from the first information in method 300. The specific implementation and transmission method of the first information, etc., can be found in step 301 and will not be described in detail here. The second information indicates the probability distribution of the channel measurement results. The specific implementation and transmission method of the second information, etc., can be found in step 304 and will not be described in detail here.

[0265] Optionally, in step 403, the terminal device sends third information to the access network device. Accordingly, the access network device receives the third information from the terminal device. The third information indicates the type of generation model and / or the function of the generation model. For the third information, refer to the description of step 302. Step 403 is optional. For example, if there is only one default generation model type and / or generation model function, or if the generation model type and / or generation model function have been agreed upon in advance between the access network device and the terminal device, step 403 may not be performed.

[0266] It should be noted that the first information, the second information, and the third information can be transmitted through the same signaling or through different signaling, without limitation. When the first information, the second information, and the third information can be transmitted through the same signaling, step 402 and step 403 can be one step.

[0267] In this way, based on method 400, a terminal device can generate a probability distribution of channel measurement results based on the generation model and report the probability distribution of the channel measurement results and the corresponding configuration parameters of the generation model to the access network device. Compared to channel measurement results transmitted over the air interface, the channel measurement results used by the terminal device are more accurate. Therefore, the probability distribution of channel measurement results generated by the terminal device based on the generation model is more accurate, which helps improve system performance. Furthermore, based on method 400, the access network device and the terminal device can align the configuration parameters of the generation model. The resulting probability distribution of channel measurement results can meet the requirements of the access network device, which helps further improve system performance.

[0268] Optionally, in some other embodiments, after receiving the second information from the terminal device, the method 400 may further include step 404, and step 404 is as follows.

[0269] Step 404: The access network device communicates with the terminal device based on the first information and the second information.

[0270] Exemplarily, the access network device determines whether to use the second information to communicate with the terminal device based on the first information. When it is determined to use the second information to communicate with the terminal device, multiple values ​​of the channel measurement results are obtained based on the second information, and communication is performed with the terminal device based on the multiple values, such as using the multiple values ​​as input information of a transmitter (such as a robust transmitter) model to improve the robustness of precoding, resource allocation, constellation diagram selection, etc. An implementation method for the access network device to determine whether to use the second information to communicate with the terminal device based on the first information is: the access network device receives a combination of multiple first information and second information, and the access network device can select the second information under the configuration parameters with the required accuracy to communicate with the terminal device, such as selecting the second information under the configuration parameters corresponding to the highest convergence accuracy. Taking the generation model as a Gaussian mixture model as an example, the access network device can select the second information under the configuration parameters with the largest number of single Gaussian models, and / or the largest number of iterations, and / or the smallest convergence threshold.

[0271] Exemplarily, one implementation manner in which the access network device obtains multiple values ​​of the channel measurement result based on the second information is: the access network device samples multiple possible values ​​of the channel measurement result based on the probability distribution indicated by the second information.

[0272] Based on the above embodiment, since the access network device can perform multiple sampling based on the probability distribution of the channel measurement results reported by the terminal device, obtaining multiple values ​​of the channel measurement results, the number of reports can be reduced compared to reporting a single channel measurement result, thereby helping to reduce signaling overhead. Furthermore, based on the first information reported by the terminal device, the access network device can select second information with configuration parameters that meet the required accuracy for communication with the terminal device. Therefore, the access network device can communicate with the terminal device using the second information with higher accuracy, which helps to further improve system performance.

[0273] Optionally, in other embodiments, method 400 may further include: the terminal device sending the channel measurement result to the access network device, and the access network device correspondingly receiving the channel measurement result from the terminal device. In this case, when obtaining multiple values ​​of the channel measurement result, the access network device may refer to the channel measurement result reported by the terminal device to obtain multiple values ​​near the channel measurement result reported by the terminal device.

[0274] Optionally, in other embodiments, before the terminal device uses the generative model to process the channel measurement results to obtain the probability distribution of the channel measurement results, the access network device may also provide the terminal device with a judgment criterion for determining whether to report the above-mentioned first information and second information. The terminal device determines whether to report the first information and the second information based on the judgment criterion provided by the access network device. That is, before step 401, method 400 may also include steps 405 and 406. Steps 405 and 406 are as follows.

[0275] In step 405, the access network device sends fourth information to the terminal device. In response, the terminal device receives the fourth information from the access network device. The fourth information indicates a judgment criterion for determining whether to report the first and second information. The implementation of the judgment criterion can be found in step 306.

[0276] Step 406: The terminal device determines to report the first information and the second information based on the acquired channel measurement result and the received judgment criterion.

[0277] Exemplarily, the terminal device determines, based on the acquired channel measurement results, that the change in the characteristics of the channel measurement results exceeds the first threshold indicated by the judgment criteria. In this case, the terminal device determines the probability distribution of updating the channel measurement results to the access network device that needs to be accessed, that is, determines to report the first information and the second information.

[0278] Based on the above embodiment, the access network device can provide the terminal device with judgment criteria for determining whether to report the above-mentioned first information and second information. The terminal device determines whether to report the first information and the second information based on the judgment criteria provided by the access network device. When the terminal device determines that the first information and the second information need to be reported, the probability distribution of the channel measurement results is obtained, which helps to reduce unnecessary training or fitting overhead of the generated model.

[0279] Optionally, in some other embodiments, before step 401, method 400 may further include: the access network device providing the terminal device with configuration parameters of the generation model recommended by the access network device. In other words, method 300 and method 400 may be used in combination.

[0280] The following describes methods 300 and 400 in detail by taking a Gaussian mixture model as the generation model and a CSI-RS as the reference signal as an example.

[0281] FIG7 is a schematic flow chart of a method 500 provided in an embodiment of the present application.

[0282] In method 500, for downlink measurement, a probability distribution fitting method of CSI-RS measurement results based on a Gaussian mixture model is used. Method 500 includes at least part of the following content.

[0283] Optionally, in step 501, the access network device sends information #1 to the terminal device. In response, the terminal device receives information #1 from the access network device. Information #1 indicates configuration parameters for a Gaussian mixture model. The configuration parameters for the Gaussian mixture model indicated in information #1 are recommended by the access network device. The configuration parameters for the Gaussian mixture model can be found in the description of step 301 and are not further described.

[0284] Step 502: The access network device sends a CSI-RS to the terminal device. Correspondingly, the terminal device receives the CSI-RS from the access network device.

[0285] Among them, CSI-RS is used for downlink channel measurement.

[0286] In step 503, the terminal device measures the CSI-RS and obtains a CSI-RS measurement result. The CSI-RS measurement result can be referred to the description in step 303 and will not be described in detail.

[0287] In step 504, the terminal device processes the CSI-RS measurement result using the first model to obtain a Gaussian probability distribution of the CSI-RS measurement result. Step 504 can refer to step 303 and will not be described in detail.

[0288] Step 505: The terminal device sends the CSI-RS measurement result to the access network device. Correspondingly, the access network device receives the CSI-RS measurement result from the terminal device.

[0289] Step 506: The terminal device sends information #2 to the access network device. Correspondingly, the access network device receives information #2 from the terminal device.

[0290] Information #2 is used to indicate the Gaussian probability distribution of the CSI-RS measurement result. Information #2 can refer to the description of the second information in step 304 and will not be described in detail.

[0291] Optionally, in step 507, the terminal device sends information #3 to the access network device. Accordingly, the access network device receives information #3 from the terminal device. Information #3 indicates the configuration parameters of the Gaussian mixture model. Unlike information #1, the configuration parameters of the Gaussian mixture model indicated in information #3 are the configuration parameters actually used when obtaining the Gaussian probability distribution of the CSI-RS measurement results. The configuration parameters of the Gaussian mixture model can be found in the description of step 301 and will not be further described.

[0292] In step 508, the access network device samples multiple possible values ​​of the CSI-RS channel measurement results based on information #2 reported by the terminal device, and inputs the sampled values ​​into the transmitter model to improve the robustness of modules such as precoding, resource allocation, or constellation selection.

[0293] The method implemented by steps 501, 502, 503, 504, 505, 506, and 508 may correspond to method 300 described above, and the method implemented by steps 502, 503, 504, 505, 506, 507, and 508 may correspond to method 400 described above. When step 507 is executed, step 508 may be for the access network device to select, based on information #2 and information #3 reported by the terminal device, a Gaussian probability distribution for CSI-RS measurement results under configuration parameters with higher convergence accuracy (e.g., a Gaussian probability distribution for CSI-RS measurement results under configuration parameters with a greater number of sub-distributions and a greater number of iterations), sample possible values ​​of the CSI-RS channel measurement results multiple times based on the selected Gaussian probability distribution, and input the sampled values ​​into the transmitter model to improve the robustness of modules such as precoding, resource allocation, or constellation selection.

[0294] It should be noted that step 501 and step 507 may also be performed simultaneously.

[0295] Based on method 500, the configuration parameters of the generation model can be aligned through interaction between the access network device and the terminal device. The terminal device then obtains a Gaussian probability distribution of the CSI-RS measurement results based on the configuration parameters of the generation model and the CSI-RS measurement results, and then reports it to the access network device. When the probability distribution of the CSI-RS measurement results of the access network device samples multiple possible values ​​to optimize the transmitter design, the access network device can obtain more accurate downlink channel estimation information, thereby improving system spectral efficiency.

[0296] The following describes methods 300 and 400 in detail by taking a variational autoencoder as the generation model and a CSI-RS as the reference signal as an example.

[0297] FIG8 is a schematic flow chart of a method 600 provided in an embodiment of the present application.

[0298] In method 600, for downlink measurement, a probability distribution fitting method of CSI-RS measurement results based on a variational autoencoder is used. Method 600 includes at least part of the following content.

[0299] Optionally, in step 601, the access network device sends information #4 to the terminal device. In response, the terminal device receives information #4 from the access network device. Information #4 indicates configuration parameters of the variational autoencoder. The variational autoencoder configuration parameters indicated in information #4 are recommended by the access network device. The variational autoencoder configuration parameters can be found in the description of step 301 and are not further described.

[0300] Step 602: The access network device sends a CSI-RS to the terminal device. Correspondingly, the terminal device receives the CSI-RS from the access network device.

[0301] Among them, CSI-RS is used for downlink channel measurement.

[0302] In step 603, the terminal device measures the CSI-RS and obtains a CSI-RS measurement result. The CSI-RS measurement result can be referred to the description in step 303 and will not be described in detail.

[0303] In step 604, the terminal device processes the CSI-RS measurement result using the first model to obtain a variational probability distribution of the CSI-RS measurement result. Step 604 can refer to step 303 and will not be described in detail.

[0304] Step 605: The terminal device sends the CSI-RS measurement result to the access network device. Correspondingly, the access network device receives the CSI-RS measurement result from the terminal device.

[0305] Step 606: The terminal device sends information #5 to the access network device. Correspondingly, the access network device receives information #5 from the terminal device.

[0306] Information #5 is used to indicate the variational probability distribution of the CSI-RS measurement result. Information #5 can refer to the description of the second information in step 304 and will not be described in detail.

[0307] Optionally, in step 607, the terminal device sends information #6 to the access network device. Accordingly, the access network device receives information #6 from the terminal device. Information #6 indicates the configuration parameters of the variational autoencoder. Unlike information #4, the configuration parameters of the variational autoencoder indicated in information #6 are the configuration parameters actually used when obtaining the variational probability distribution of the CSI-RS measurement results. The configuration parameters of the variational autoencoder can be referred to the description in step 301 and will not be further described.

[0308] In step 608, the access network device samples multiple possible values ​​of the CSI-RS channel measurement results based on information #5 reported by the terminal device, and inputs the sampled values ​​into the transmitter model to improve the robustness of modules such as precoding, resource allocation, or constellation selection.

[0309] The method implemented by steps 601, 602, 603, 604, 605, 606, and 608 may correspond to method 300 described above, and the method implemented by steps 602, 603, 604, 605, 606, 607, and 608 may correspond to method 400 described above. When step 607 is executed, step 608 may be for the access network device to select, based on information #5 and information #6 reported by the terminal device, a variational probability distribution of CSI-RS measurement results under configuration parameters with higher convergence accuracy (for example, a variational probability distribution of CSI-RS measurement results under configuration parameters with a greater number of neural network layers and greater complexity), sample possible values ​​of the CSI-RS channel measurement results multiple times based on the selected variational probability distribution, and input the sampled values ​​into the transmitter model to improve the robustness of modules such as precoding, resource allocation, or constellation selection.

[0310] It should be noted that step 601 and step 607 may also be performed simultaneously.

[0311] Based on method 600, the configuration parameters of the generation model can be aligned through interaction between the access network device and the terminal device. The terminal device then obtains a variational probability distribution of the CSI-RS measurement results based on the configuration parameters of the generation model and the CSI-RS measurement results, and then reports it to the access network device. When the probability distribution of the CSI-RS measurement results of the access network device samples multiple possible values ​​to optimize the transmitter design, the access network device can obtain more accurate downlink channel estimation information, thereby improving system spectral efficiency.

[0312] Below, taking the generation model as a Gaussian mixture model, the reference signal as CSI-RS, and the access network device indicating the configuration parameters of the Gaussian mixture model to the terminal device as an example, a scheme in which the access network device in methods 300 and 400 can provide the terminal device with a judgment criterion for the Gaussian probability distribution of whether to report the CSI-RS measurement result is described in detail.

[0313] FIG9 is a schematic flow chart of a method 700 provided in an embodiment of the present application.

[0314] Method 700 includes at least part of the following.

[0315] In step 701, the access network device sends information #1 to the terminal device. In response, the terminal device receives information #1 from the access network device. Information #1 indicates configuration parameters of the Gaussian mixture model. Step 701 may refer to step 501.

[0316] Step 702: The access network device sends information #7 to the terminal device. Correspondingly, the terminal device receives information #7 from the access network device.

[0317] Information #7 is used to indicate the judgment criteria for determining whether to report information #2. Information #7 can refer to the description of the fourth information in step 306, and information #2 can refer to the description of the second information in step 304, which will not be described in detail.

[0318] Step 703: The access network device sends a CSI-RS to the terminal device. Correspondingly, the terminal device receives the CSI-RS from the access network device.

[0319] Among them, CSI-RS is used for downlink channel measurement.

[0320] The embodiment of the present application does not limit the order of steps 701 , 702 and 703 .

[0321] In step 704, the terminal device measures the CSI-RS and obtains a CSI-RS measurement result. The CSI-RS measurement result can be referred to the description in step 303 and will not be described in detail.

[0322] In step 705, the terminal device determines whether to report information #2 based on the CSI-RS measurement result and the judgment criteria indicated by information #7. Step 705 can refer to step 307 and will not be described in detail.

[0323] Step 706: When it is determined that information #2 needs to be reported, the terminal device processes the CSI-RS measurement result using the first model to obtain a Gaussian probability distribution of the CSI-RS measurement result. Step 704 can refer to step 303 and will not be described in detail.

[0324] Step 707: The terminal device sends the CSI-RS measurement result to the access network device. Correspondingly, the access network device receives the CSI-RS measurement result from the terminal device.

[0325] Step 708: The terminal device sends information #2 to the access network device. Correspondingly, the access network device receives information #2 from the terminal device.

[0326] Information #2 is used to indicate the Gaussian probability distribution of the CSI-RS measurement result. Information #2 can refer to the description of the second information in step 304 and will not be described in detail.

[0327] In step 709, the access network device samples multiple possible values ​​of the CSI-RS channel measurement results based on information #2 reported by the terminal device, and inputs the sampled values ​​into the transmitter model to improve the robustness of modules such as precoding, resource allocation, or constellation selection.

[0328] Based on method 700, the configuration parameters of the generation model can be aligned through interaction between the access network device and the terminal device. The terminal device then obtains a Gaussian probability distribution of the CSI-RS measurement result based on the configuration parameters of the generation model and the CSI-RS measurement result, and then reports it to the access network device. When the probability distribution of the CSI-RS measurement result of the access network device samples multiple possible values ​​to optimize the transmitter design, the access network device can obtain more accurate downlink channel estimation information to improve system spectrum efficiency. The access network device can provide the terminal device with a judgment criterion for determining whether to report information #2. The terminal device determines whether to report information #2 based on the judgment criterion provided by the access network device, which helps reduce unnecessary training or fitting overhead of the generation model.

[0329] The method provided in the embodiments of the present application is described in detail above with reference to Figures 1 to 9 . Below, the apparatus provided in the embodiments of the present application is described in detail with reference to Figures 10 and 11 . It should be understood that the description of the apparatus embodiment corresponds to the description of the method embodiment. Therefore, for matters not described in detail, reference can be made to the method embodiment above, and for the sake of brevity, they will not be repeated here.

[0330] Figure 10 is a schematic diagram of a communication device 1000 provided in an embodiment of the present application. As shown in Figure 10, the communication device 1000 includes a processing module 1001 and a communication module 1002. The communication device 1000 can be a network device (i.e., an access network device), or a communication device applied to a network device or used in conjunction with a network device and capable of implementing a method executed by the network device, such as a chip, a chip system, or a circuit. Alternatively, the communication device 1000 can be a terminal device, or a communication device applied to a terminal device or used in conjunction with a terminal device and capable of implementing a method executed by the terminal device, such as a chip, a chip system, or a circuit.

[0331] The communication module may also be referred to as a transceiver module, transceiver, transceiver, or transceiver device. The processing module may also be referred to as a processor, processing board, processing unit, or processing device. Optionally, the communication module is used to perform the sending and receiving operations of the network device or terminal device in the above method. The device used to implement the receiving function in the communication module can be considered a receiving unit, and the device used to implement the sending function in the communication module can be considered a sending unit. That is, the communication module includes a receiving unit and a sending unit.

[0332] When the communication device 1000 is applied to a network device, the processing module 1001 may be used to implement the processing functions of the network device in the above embodiments, and the communication module 1002 may be used to implement the transceiver functions of the network device in the above embodiments.

[0333] When the communication apparatus 1000 is applied to a terminal device, the processing module 1001 may be used to implement the processing functions of the terminal device in the above embodiments, and the communication module 1002 may be used to implement the transceiver functions of the terminal device in the above embodiments.

[0334] In addition, it should be noted that the aforementioned communication module and / or processing module can be implemented by a virtual module, for example, the processing module can be implemented by a software functional unit or a virtual device, and the communication module can be implemented by a software function or a virtual device. Alternatively, the processing module or the communication module can also be implemented by a physical device, for example, if the device is implemented using a chip / circuit (such as an integrated circuit or a logic circuit, etc.). The communication module can be an input and output circuit and / or a communication interface, performing input operations (corresponding to the aforementioned receiving operations) and output operations (corresponding to the aforementioned sending operations); the processing module is an integrated processor or microprocessor or circuit (such as an integrated circuit or a logic circuit, etc.).

[0335] The division of modules in this application is illustrative and represents only a logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in the examples of this application may be integrated into a single processor, exist physically as separate modules, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in either hardware or software functional modules.

[0336] Figure 11 is a schematic diagram of another communication device 1100 provided in an embodiment of the present application. As shown in Figure 11, communication device 1100 can optionally be the aforementioned network device or terminal device, or a chip or chip system for the aforementioned network device or terminal device. Optionally, the chip system in this application can be composed of a chip, or can also include a chip and other discrete devices.

[0337] The communication device 1100 can be used to implement the functions of any network element (such as a network device or a terminal device) in the communication system described in the above examples. The communication device 1100 may include a processing circuit 1110. Optionally, the processing circuit 1110 is coupled to a memory, and the memory may be located within the device, or the memory may be integrated with the processor, or the memory may be located outside the device. For example, the communication device 1100 may also include at least one memory 1120. The memory 1120 stores the necessary computer programs, computer programs or instructions and / or data for implementing any of the above examples; the processing circuit 1110 may execute the computer program stored in the memory 1120 to complete the method in any of the above examples.

[0338] The communication device 1100 may also include a transceiver circuit 1130, and the communication device 1100 can exchange information with other devices through the transceiver circuit 1130. Exemplarily, the transceiver circuit 1130 can be a transceiver, circuit, bus, module, pin or other type of communication interface. When the communication device 1100 is a chip-type device or circuit, the transceiver circuit 1130 in the device 1100 can also be an input-output circuit, or an interface circuit, which can input information (or receive information) and output information (or send information). When the communication device 1100 is a core network element, a network device or a terminal device, the transceiver circuit can be a transmitter, a receiver or a transceiver, or a communication interface, which is not limited here.

[0339] The processing circuit 1110 may be one or more processors, or all or part of the processing circuits in one or more processors. The processing circuit 1110 may be an integrated processor, microprocessor, integrated circuit, or logic circuit, and the processor may determine output information based on input information.

[0340] Coupling in this application refers to an indirect coupling or communication connection between devices, units, or modules, which can be electrical, mechanical, or other forms, and is used for information exchange between devices, units, or modules. Processing circuit 1110 may operate in conjunction with memory 1120 and transceiver circuit 1130. This application does not limit the specific connection medium between the processing circuit 1110, memory 1120, and transceiver circuit 1130.

[0341] Optionally, as shown in FIG11 , the processing circuit 1110, the memory 1120, and the transceiver circuit 1130 are interconnected via a bus 1140. Optionally, the bus may include an address bus, a data bus, a control bus, or other types of buses. Furthermore, for ease of illustration, FIG11 shows one bus 1140, but this does not mean that there is only one bus or only one type of bus.

[0342] It should be understood that the processors mentioned in the embodiments of the present application may be the following devices or the circuit portions of the following devices used for processing functions: a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0343] It should also be understood that the memory mentioned in the embodiments of the present application may be a volatile memory and / or a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM). For example, RAM can be used as an external cache. By way of example and not limitation, RAM includes the following forms: static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM).

[0344] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, the memory (storage module) can be integrated into the processor.

[0345] It should also be noted that the memory described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0346] In an embodiment of the present application, the method described in the above embodiment can be executed by a network device or a terminal device, or can be executed by a chip, chip system or circuit of the network device or the terminal device, and the chip, chip system or circuit can be installed in the network device or the terminal device.

[0347] An embodiment of the present application provides a computer-readable storage medium on which computer instructions for implementing the methods executed by a network device or a terminal device in the above-mentioned method embodiments are stored.

[0348] For example, when the computer program is executed by a computer, the computer can implement the methods executed by the network device or the terminal device in each embodiment of the above method.

[0349] An embodiment of the present application provides a computer program product, comprising instructions, which, when executed by a computer, implement the methods performed by a network device or a terminal device in the above-mentioned method embodiments.

[0350] An embodiment of the present application provides a communication system, which includes the network device and / or terminal device in the above embodiments.

[0351] The explanation of the relevant contents and beneficial effects of any of the above-mentioned devices can be referred to the corresponding method embodiments provided above, which will not be repeated here.

[0352] To facilitate understanding of the above embodiments provided in this application, the following points are explained:

[0353] In this application, unless otherwise specified or there is a logical conflict, the terms and / or descriptions between different embodiments are consistent and can be referenced to each other, and the technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0354] It can be understood that some optional features in the various embodiments of the present application may not depend on other features in certain scenarios, and may also be combined with other features in certain scenarios, without limitation.

[0355] It can also be understood that in some of the above embodiments, the AI ​​model is mainly used as an example to illustrate the determination of the probability distribution of the channel measurement results. It can be understood that the above AI model can also be used for other purposes.

[0356] It can also be understood that the solutions in the various embodiments of the present application can be reasonably combined and used, and the explanations or descriptions of the various terms appearing in the embodiments can be referenced or explained with each other in the various embodiments, without limitation to this.

[0357] It can also be understood that in the above-mentioned various method embodiments, the methods and operations implemented by the network device or terminal device can also be implemented by components (such as chips or circuits) that can be implemented by the network device or terminal device, without limitation.

[0358] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0359] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0360] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0361] 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0362] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0363] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0364] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A communication method, characterized in that: The method is performed by a terminal device or a module or unit in the terminal device, and the method includes: Receiving first information from an access network device, where the first information is used to indicate configuration parameters of a generation model; Processing the channel measurement result using a first model to obtain a probability distribution of the channel measurement result, wherein the first model is determined based on the configuration parameter; Sending second information to the access network device, where the second information is used to indicate the probability distribution.

2. The method according to claim 1, characterized in that The method further comprises: Receive third information from the access network device, where the third information is used to indicate the type of the generation model and / or the function of the generation model.

3. The method according to claim 1 or 2, characterized in that: The method further comprises: Receive fourth information from the access network device, where the fourth information is used to indicate a judgment criterion, and the judgment criterion is used to judge whether to report the second information.

4. The method according to any one of claims 1 to 3, characterized in that The configuration parameters include a first configuration parameter and a second configuration parameter; The first information indicates the first configuration parameter and the second configuration parameter; or the first information indicates the first configuration parameter, wherein the second configuration parameter is determined according to a mapping relationship, and the mapping relationship is used to indicate a corresponding relationship between the first configuration parameter and the second configuration parameter.

5. The method according to claim 4, characterized in that The mapping relationship is configured or pre-configured.

6. The method according to any one of claims 1 to 5, characterized in that The first information indicates the first configuration parameter and the second configuration parameter; and the receiving the first information from the access network device includes: Receiving the first information from the access network device through a first signaling; The configuration information of the first configuration parameter is carried in the first part of the first signaling, and the configuration information of the second configuration parameter is carried in the second part of the first signaling.

7. The method according to claim 6, characterized in that The receiving the first information from the access network device through the first signaling includes: receiving configuration information of the first configuration parameter from the access network device through the first part of the first signaling at a first moment, and, Receiving configuration information of the second configuration parameter from the access network device through the second part of the first signaling at a second moment; The first moment and the second moment are the same, or the first moment and the second moment are different.

8. The method according to any one of claims 1 to 7, characterized in that The judgment criterion includes: a feature of the channel measurement result and a first threshold corresponding to the feature of the channel measurement result.

9. The method according to any one of claims 1 to 8, characterized in that The characteristics of the channel measurement result include at least one of the following parameters: Signal-to-noise ratio; Signal-to-interference-noise ratio; reference signal received power; The probability distribution of the channel measurement results.

10. The method according to any one of claims 1 to 9, characterized in that The generation model is a Gaussian mixture model, and the configuration parameters include at least one of the following information: A method for generating the Gaussian mixture model; A convergence threshold of the Gaussian mixture model; The maximum number of iterations of the Gaussian mixture model; Model parameters of the Gaussian mixture model; The maximum number M of single Gaussian models included in the Gaussian mixture model, where M is a positive integer; The Gaussian mixture model includes a maximum value N of the expected value of a single Gaussian model, where N is a positive number; The maximum value A of the variance or covariance of the single Gaussian model included in the Gaussian mixture model, where A is a positive number; The proportion of one or more single Gaussian models included in the Gaussian mixture model in the Gaussian mixture model; Alternatively, the generative model is a variational autoencoder, and the configuration parameters include at least one of the following information: Structural parameters of the variational autoencoder; The type of neural network used by the variational autoencoder; The number of neural network layers used by the variational autoencoder; The number of neurons contained in the neural network used by the variational autoencoder; The input and / or output dimensions of the variational autoencoder; The values ​​of the model parameters of the variational autoencoder.

11. The method according to any one of claims 1 to 10, characterized in that The generative model includes at least one of the following models: Gaussian mixture models; Variational Autoencoder; Generative Adversarial Networks.

12. The method according to any one of claims 1 to 11, characterized in that The generation model is a Gaussian mixture model, and the second information includes at least one of the following information: K expected values; K variance or covariance values; The proportion of K single Gaussian models in the Gaussian mixture model; The K expectations, the K variances or covariances correspond one-to-one to the K single Gaussian models, and K is a positive integer; Alternatively, the generative model is a variational autoencoder, and the second information includes at least one of the following information: The values ​​of the model parameters of the variational autoencoder; The value of the probability distribution output by the variational autoencoder.

13. A communication method, characterized in that: The method is performed by an access network device or a module or unit in the access network device, and the method includes: Sending first information to a terminal device, where the first information is used to indicate configuration parameters of a generated model; Second information is received from the terminal device, where the second information is used to indicate a probability distribution of a channel measurement result, and the second information is related to the configuration parameter.

14. The method according to claim 13, characterized in that The method further comprises: Obtaining multiple values ​​of the channel measurement result according to the second information; Communicate with the terminal device according to the multiple values.

15. The method according to claim 13 or 14, characterized in that The method further comprises: Sending third information to the terminal device, where the third information is used to indicate the type of the generation model and / or the function of the generation model.

16. The method according to any one of claims 13 to 15, characterized in that The method further comprises: Send fourth information to the terminal device, where the fourth information is used to indicate a judgment criterion, and the judgment criterion is used to determine whether to report the second information.

17. The method according to any one of claims 13 to 16, characterized in that The configuration parameters include a first configuration parameter and a second configuration parameter; The first information indicates the first configuration parameter and the second configuration parameter; or the first information indicates the first configuration parameter, wherein the second configuration parameter is determined according to a mapping relationship, and the mapping relationship is used to indicate a corresponding relationship between the first configuration parameter and the second configuration parameter.

18. The method according to claim 17, characterized in that The mapping relationship is configured or pre-configured.

19. The method according to any one of claims 13 to 16, characterized in that The first information indicates the first configuration parameter and the second configuration parameter; The sending of the first information to the terminal device includes: sending the first information to the terminal device via a first signaling; wherein the configuration information of the first configuration parameter is carried in a first part of the first signaling, and the configuration information of the second configuration parameter is carried in a second part of the first signaling.

20. The method according to claim 19, characterized in that The sending the first information to the terminal device through the first signaling includes: sending configuration information of the first configuration parameter to the terminal device through the first part of the first signaling at a first moment, and, Sending configuration information of the second configuration parameter to the terminal device through the second part of the first signaling at a second moment; The first moment and the second moment are the same, or the first moment and the second moment are different.

21. The method according to any one of claims 13 to 20, characterized in that The judgment criterion includes: a feature of the channel measurement result and a first threshold corresponding to the feature of the channel measurement result.

22. The method according to any one of claims 13 to 21, characterized in that The characteristics of the channel measurement result include at least one of the following parameters: Signal-to-noise ratio; Signal-to-interference-noise ratio; reference signal received power; The probability distribution of the channel measurement results.

23. The method according to any one of claims 13 to 22, characterized in that The generation model is a Gaussian mixture model, and the configuration parameters include at least one of the following information: A method for generating the Gaussian mixture model; A convergence threshold of the Gaussian mixture model; The maximum number of iterations of the Gaussian mixture model; Model parameters of the Gaussian mixture model; The maximum number M of single Gaussian models included in the Gaussian mixture model, where M is a positive integer; The Gaussian mixture model includes a maximum value N of the expected value of a single Gaussian model, where N is a positive number; The maximum value A of the variance or covariance of the single Gaussian model included in the Gaussian mixture model, where A is a positive number; The proportion of one or more single Gaussian models included in the Gaussian mixture model in the Gaussian mixture model; Alternatively, the generative model is a variational autoencoder, and the configuration parameters include at least one of the following information: Structural parameters of the variational autoencoder; The type of neural network used by the variational autoencoder; The number of neural network layers used by the variational autoencoder; The number of neurons contained in the neural network used by the variational autoencoder; The input and / or output dimensions of the variational autoencoder; The values ​​of the model parameters of the variational autoencoder.

24. The method according to any one of claims 13 to 23, characterized in that The generative model includes at least one of the following models: Gaussian mixture models; Variational Autoencoder; Generative Adversarial Networks.

25. The method according to any one of claims 13 to 24, characterized in that The generation model is a Gaussian mixture model, and the second information includes at least one of the following information: K expected values; K variance or covariance values; The proportion of K single Gaussian models in the Gaussian mixture model; The K expectations, the K variances or covariances correspond one-to-one to the K single Gaussian models, and K is a positive integer; Alternatively, the generative model is a variational autoencoder, and the second information includes at least one of the following information: The values ​​of the model parameters of the variational autoencoder; The value of the probability distribution output by the variational autoencoder.

26. A communication method, characterized in that: The method is performed by a terminal device or a module or unit in the terminal device, and the method includes: Processing the channel measurement results using the generative model to obtain a probability distribution of the channel measurement results; First information and second information are sent to an access network device, where the first information is used to indicate configuration parameters of the generation model, and the second information is used to indicate the probability distribution.

27. The method according to claim 26, characterized in that The method further comprises: Sending third information to the access network device, where the third information is used to indicate the type of the generation model and / or the function of the generation model.

28. The method according to claim 26 or 27, characterized in that The method further comprises: Receive fourth information from the access network device, where the fourth information is used to indicate a judgment criterion, and the judgment criterion is used to judge whether to report the first information and the second information.

29. The method according to any one of claims 26 to 28, characterized in that The configuration parameters include a first configuration parameter and a second configuration parameter; The first information indicates the first configuration parameter and the second configuration parameter; or the first information indicates the first configuration parameter, wherein the second configuration parameter is determined according to a mapping relationship, and the mapping relationship is used to indicate a corresponding relationship between the first configuration parameter and the second configuration parameter.

30. The method according to claim 29, characterized in that The mapping relationship is configured or pre-configured.

31. The method according to any one of claims 26 to 30, characterized in that The first information indicates the first configuration parameter and the second configuration parameter; and the sending the first information to the access network device includes: Sending the first information to the access network device through a first signaling; The configuration information of the first configuration parameter is carried in the first part of the first signaling, and the configuration information of the second configuration parameter is carried in the second part of the first signaling.

32. The method according to claim 31, characterized in that The sending the first information to the access network device through the first signaling includes: sending configuration information of the first configuration parameter to the access network device through the first part of the first signaling at a first moment, and, Sending configuration information of the second configuration parameter to the access network device through the second part of the first signaling at a second moment; The first moment and the second moment are the same, or the first moment and the second moment are different.

33. The method according to any one of claims 26 to 32, characterized in that The judgment criterion includes: a feature of the channel measurement result and a first threshold corresponding to the feature of the channel measurement result.

34. The method according to any one of claims 26 to 33, characterized in that The characteristics of the channel measurement result include at least one of the following parameters: Signal-to-noise ratio; Signal-to-interference-noise ratio; reference signal received power; The probability distribution of the channel measurement results.

35. The method according to any one of claims 26 to 34, characterized in that The generation model is a Gaussian mixture model, and the configuration parameters include at least one of the following information: A method for generating the Gaussian mixture model; A convergence threshold of the Gaussian mixture model; The maximum number of iterations of the Gaussian mixture model; Model parameters of the Gaussian mixture model; The maximum number M of single Gaussian models included in the Gaussian mixture model, where M is a positive integer; The Gaussian mixture model includes a maximum value N of the expected value of a single Gaussian model, where N is a positive number; The maximum value A of the variance or covariance of the single Gaussian model included in the Gaussian mixture model, where A is a positive number; The proportion of one or more single Gaussian models included in the Gaussian mixture model in the Gaussian mixture model; Alternatively, the generative model is a variational autoencoder, and the configuration parameters include at least one of the following information: Structural parameters of the variational autoencoder; The type of neural network used by the variational autoencoder; The number of neural network layers used by the variational autoencoder; The number of neurons contained in the neural network used by the variational autoencoder; The input and / or output dimensions of the variational autoencoder; The values ​​of the model parameters of the variational autoencoder.

36. The method according to any one of claims 26 to 35, characterized in that The generative model includes at least one of the following models: Gaussian mixture models; Variational Autoencoder; Generative Adversarial Networks.

37. The method according to any one of claims 26 to 36, characterized in that The generation model is a Gaussian mixture model, and the second information includes at least one of the following information: K expected values; K variance or covariance values; The proportion of K single Gaussian models in the Gaussian mixture model; The K expectations, the K variances or covariances correspond one-to-one to the K single Gaussian models, and K is a positive integer; Alternatively, the generative model is a variational autoencoder, and the second information includes at least one of the following information: The values ​​of the model parameters of the variational autoencoder; The value of the probability distribution output by the variational autoencoder.

38. A communication method, characterized in that: The method is performed by an access network device or a module or unit in the access network device, and the method includes: Receiving first information and second information from a terminal device, wherein the first information is used to indicate configuration parameters of a generation model, and the second information is used to indicate a probability distribution of a channel measurement result, and the second information is related to the configuration parameters; Communicate with the terminal device based on the first information and the second information.

39. The method according to claim 38, characterized in that The communicating with the terminal device based on the first information and the second information includes: Based on the first information and the second information, multiple values ​​of the channel measurement result are obtained; and according to the multiple values, communication is performed with the terminal device.

40. The method according to claim 38 or 39, characterized in that The method further comprises: Receive third information from the terminal device, where the third information is used to indicate the type of the generation model and / or the function of the generation model.

41. The method according to any one of claims 38 to 40, characterized in that The configuration parameters include a first configuration parameter and a second configuration parameter; The first information indicates the first configuration parameter and the second configuration parameter; or the first information indicates the first configuration parameter, wherein the second configuration parameter is determined according to a mapping relationship, and the mapping relationship is used to indicate a corresponding relationship between the first configuration parameter and the second configuration parameter.

42. The method according to claim 41, characterized in that The mapping relationship is configured or pre-configured.

43. The method according to any one of claims 38 to 42, characterized in that The first information indicates the first configuration parameter and the second configuration parameter, and the receiving the first information sent from the terminal device includes: receiving the first information from the terminal device through a first signaling; The configuration information of the first configuration parameter is carried in the first part of the first signaling, and the configuration information of the second configuration parameter is carried in the second part of the first signaling.

44. The method according to any one of claims 38 to 43, characterized in that The receiving the first information from the terminal device through the first signaling includes: receiving configuration information of the first configuration parameter from the terminal device through the first part of the first signaling at a first moment, and, receiving configuration information of the second configuration parameter from the terminal device through the second part of the first signaling at a second moment; The first moment and the second moment are the same, or the first moment and the second moment are different.

45. The method according to any one of claims 38 to 44, characterized in that The method further comprises: Send fourth information to the terminal device, where the fourth information is used to indicate a judgment criterion, and the judgment criterion is used to judge whether to report the first information and the second information.

46. ​​The method according to any one of claims 38 to 45, characterized in that The judgment criterion includes: a feature of the channel measurement result and a first threshold corresponding to the feature of the channel measurement result.

47. The method according to any one of claims 38 to 46, characterized in that The characteristics of the channel measurement result include at least one of the following parameters: Signal-to-noise ratio; Signal-to-interference-noise ratio; reference signal received power; The probability distribution of the channel measurement results.

48. The method according to any one of claims 38 to 47, characterized in that The generation model is a Gaussian mixture model, and the configuration parameters include at least one of the following information: A method for generating the Gaussian mixture model; A convergence threshold of the Gaussian mixture model; The maximum number of iterations of the Gaussian mixture model; Model parameters of the Gaussian mixture model; The maximum number M of single Gaussian models included in the Gaussian mixture model, where M is a positive integer; The Gaussian mixture model includes a maximum value N of the expected value of a single Gaussian model, where N is a positive number; The maximum value A of the variance or covariance of the single Gaussian model included in the Gaussian mixture model, where A is a positive number; The proportion of one or more single Gaussian models included in the Gaussian mixture model in the Gaussian mixture model; Alternatively, the generative model is a variational autoencoder, and the configuration parameters include at least one of the following information: Structural parameters of the variational autoencoder; The type of neural network used by the variational autoencoder; The number of neural network layers used by the variational autoencoder; The number of neurons contained in the neural network used by the variational autoencoder; The input and / or output dimensions of the variational autoencoder; The values ​​of the model parameters of the variational autoencoder.

49. The method according to any one of claims 38 to 48, characterized in that The generative model includes at least one of the following models: Gaussian mixture models; Variational Autoencoder; Generative Adversarial Networks.

50. The method according to any one of claims 38 to 49, characterized in that The generation model is a Gaussian mixture model, and the second information includes at least one of the following information: K expected values; K variance or covariance values; The proportion of K single Gaussian models in the Gaussian mixture model; The K expectations, the K variances or covariances correspond one-to-one to the K single Gaussian models, and K is a positive integer; Alternatively, the generative model is a variational autoencoder, and the second information includes at least one of the following information: The values ​​of the model parameters of the variational autoencoder; The value of the probability distribution output by the variational autoencoder.

51. A communication device, characterized in that: Comprising modules or units for performing the method as claimed in any one of claims 1 to 50.

52. A communication device, characterized in that: It includes a processor and an interface circuit, wherein the interface circuit is used to receive signals from other communication devices outside the communication device and transmit them to the processor or send signals from the processor to other communication devices outside the communication device, and the processor is used to implement the method as described in any one of claims 1 to 50 through logic circuits or executing code instructions.

53. The communication device according to claim 52, characterized in that The communication device is a terminal device, an access network device, a chip or a chip system.

54. A computer-readable storage medium, characterized in that: The storage medium stores a computer program or instruction, and when the computer program or instruction is executed by the communication device, the method as claimed in any one of claims 1 to 50 is implemented.

55. A computer program product, characterized in that The invention comprises a computer program which, when being executed, implements the method according to any one of claims 1 to 50.

56. A communication system, characterized in that: include: A communication device for executing the method according to any one of claims 1 to 12, and a communication device for executing the method according to any one of claims 13 to 25; or, A communication device for performing the method according to any one of claims 26 to 37, and a communication device for performing the method according to any one of claims 38 to 50.

Images