Communication method and related apparatus

Abstract

A communication method and a related apparatus. In the method, when a first communication apparatus sends first information indicating that the first communication apparatus supports communication by means of N types of communication parameters, the first communication apparatus receives second information used for indicating K types of communication parameters among the N types of communication parameters. In other words, a sender of the second information can indicate, on the basis of the N types of communication parameters supported by the first communication apparatus, the K types of communication parameters among the N types of communication parameters. In this way, when determining the K types of communication parameters on the basis of an AI mode, the first communication apparatus can perform communication on the basis of the K types of communication parameters, to optimize the communication parameters on the basis of the AI mode. The optimization mode can quickly determine the communication parameters to shorten the communication delay, and can also achieve intelligence of the communication apparatuses by means of the communication parameters that are determined on the basis of the AI mode, thereby improving communication performance.

Description

A communication method and related apparatus

[0001] This application claims priority to Chinese Patent Application No. 202411458512.2, filed on October 17, 2024, entitled "A Communication Method and Related Device", the entire contents of which are incorporated herein by reference. Technical Field

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

[0003] Wireless communication can be a transmission communication between two or more communication nodes that does not propagate through conductors or cables. These communication nodes generally include network devices and terminal devices.

[0004] To address the vision of a future of intelligent and inclusive access, intelligence will further evolve at the wireless network architecture level. Taking artificial intelligence (AI) as an example, AI will have the potential to be more deeply integrated with wireless networks, achieving inherent intelligence within the network, as well as intelligence in terminals. For instance, AI can be used for various wireless tasks, such as time-frequency domain channel estimation, prediction, and beam prediction related to wireless channels. AI models for specific tasks can undergo data collection, model training, selection, switching, and retraining based on applicable conditions.

[0005] AI models deployed in wireless communication systems can operate without participating in signal processing; for example, they can design and optimize communication parameters. However, managing these AI models remains a technical challenge. Summary of the Invention

[0006] This application provides a communication method and related apparatus for communicating using communication parameters determined based on artificial intelligence (AI) to improve communication performance.

[0007] The first aspect of this application provides a communication method applied to a first communication device, for example, the method being executed by the first communication device. The first communication device may be a communication equipment (such as a terminal device or network device), or it may be a component of the communication equipment (e.g., a circuit or chip responsible for communication functions (such as a modem chip, also known as a baseband chip, or a system-on-a-chip (SoC) chip containing a modem core, or a system-in-package (SIP) chip), or it may be a logic module or software capable of implementing all or part of the functions of the communication equipment).

[0008] In this method, a first communication device sends first information, which indicates that the first communication device supports communication through N types of communication parameters, where N is a positive integer; wherein the N types of communication parameters are determined based on AI; the first communication device receives second information, which indicates K types of communication parameters, where K is a positive integer less than or equal to N; wherein the K types of communication parameters are included in the N types of communication parameters.

[0009] Based on the above scheme, after the first communication device sends first information indicating that it supports communication via N types of communication parameters, the second information received by the first communication device is used to indicate K types of communication parameters among the N types of communication parameters. In other words, the sender of the second information can indicate K types of communication parameters based on the N types of communication parameters supported by the first communication device. Therefore, after determining the K types of communication parameters based on AI, the first communication device can communicate based on these K types of communication parameters to optimize the communication parameters through AI. This optimization method can quickly determine communication parameters to reduce communication latency, and also enables the communication device to become intelligent through the communication parameters determined by AI, thereby improving communication performance.

[0010] Furthermore, the recipient of the first information (e.g., a second communication device) can determine through the first information that the first communication device supports communication using N types of communication parameters determined by AI, and the K types of communication parameters indicated by the second information are some or all of the N types of communication parameters. In this way, the second information can indicate / schedule / configure communication parameters that are compatible with the capabilities of the first communication device, avoiding situations where the second information indicates communication parameters that the first communication device does not support, leading to indication / schedule / configuration failures, thereby improving communication efficiency.

[0011] Optionally, the communication parameters involved in this application may include parameters involved in the processing of communication signals (e.g., generation, transmission, parsing, etc.). For example, the communication parameters may include, but are not limited to, one or more of the following: constellation diagrams (which can be used for modulation and / or demodulation), polar code sequences (which can be used for polar code encoding and decoding), low-density parity check (LDPC) code base diagrams (which can be used for LDPC code encoding and decoding), filter parameters (which can be used for filtering), digital predistortion (DPD) parameters, modulation parameters, pilot parameters, waveform parameters, precoding parameters, power control parameters, or scheduling parameters.

[0012] Optionally, determining communication parameters through AI can be understood as determining communication parameters in an intelligent manner. This intelligent manner includes, but is not limited to, technologies enabling AI, neural networks, machine learning, reinforcement learning, deep learning, and large language models (LLM). Accordingly, the AI ​​method involved in this application can be replaced by technologies enabling intelligent methods, neural networks, machine learning, reinforcement learning, deep learning, or LLM. Similarly, the non-AI methods mentioned below can be replaced by non-intelligent methods, non-neural network methods, non-machine learning methods, non-reinforcement learning methods, non-deep learning methods, or non-LLM methods; or, non-AI methods can also be replaced by traditional methods or conventional methods.

[0013] Optionally, in the above scheme, K equals 0. In other words, the sender of the second information (e.g., the second communication device) determines, based on certain information (e.g., the fifth information mentioned below, including but not limited to one or more of scene information, environmental information, and system information), that the first communication device is not suitable for communication based on communication parameters determined by AI. In this case, the second information indicates that the communication parameters are determined by a non-AI method, or the second information indicates that the communication parameters are not determined by AI, so that the first communication device can fall back to the non-AI method for communication based on the indication, and that the indication / scheduling / configuration of the second information can be adapted to the inference configuration information.

[0014] In one possible implementation of the first aspect, the second information includes K pieces of information, and the i-th piece of information among the K pieces of information includes first indication information and second indication information, where i takes the value from 1 to K; wherein, the first indication information is used to indicate the i-th type of communication parameter among the K types of communication parameters, and the second indication information is used to indicate that the i-th type of communication parameter is determined by AI (or, the second indication information is used to activate the i-th type of communication parameter, or, the second indication information is used to activate the determination of the i-th type of communication parameter by AI).

[0015] Based on the above scheme, the second information may include K pieces of information. Each of the K pieces of information is used to configure and activate one of the K types of communication parameters determined by AI. That is, the first communication device can determine the K types of communication parameters by means of the indication of the second information (i.e., single-level indication), and can quickly realize the scheduling / indication / configuration of the K types of communication parameters to reduce communication latency.

[0016] In one possible implementation of the first aspect, the second information further includes M pieces of information, wherein the j-th piece of information includes a third indication information and a fourth indication information, where j takes values ​​from 1 to M, and M is a positive integer; wherein the third indication information is used to indicate the j-th type of communication parameter among the M types of communication parameters, and the second indication information is used to indicate that the j-th type of communication parameter is determined by a non-AI method.

[0017] Optionally, the value of M is less than or equal to NK, that is, the value of N is greater than or equal to the sum of M and K.

[0018] Based on the above scheme, the second information may also include M pieces of information. Each of the M pieces of information is used to configure and activate one of the M types of communication parameters determined by a non-AI method. That is, the first communication device can realize the use of K types of communication parameters and M types of communication parameters through the indication of the second information, and can quickly realize the scheduling / indication / configuration of these communication parameters to reduce communication latency.

[0019] In one possible implementation of the first aspect, the method further includes: the first communication device receiving third information for configuring the K-type communication parameters; wherein the second information is used to indicate the activation of the K-type communication parameters.

[0020] Based on the above scheme, the first communication device can also receive third information for configuring K-type communication parameters, and the second information is used to indicate the activation of the K-type communication parameters (i.e., two-level indication). That is, the first communication device determines the K-type communication parameters through the configuration of the third information and the indication of the second information (i.e., two-level indication). In this way, flexible scheduling of various communication parameters can be achieved.

[0021] In addition, the second information can indicate the activation or deactivation of one or more types of communication parameters, which can reduce the overhead of activation or deactivation indication.

[0022] In one possible implementation of the first aspect, the first information includes fifth indication information; wherein the fifth indication information is used to indicate that the first communication device has the ability to communicate through communication parameters determined by an AI-based method.

[0023] Based on the above scheme, the first information sent by the first communication device may include the aforementioned fifth indication information, so that the recipient of the first information (e.g., the second communication device) can clearly understand that the first communication device has the ability to communicate through communication parameters determined by AI based on the fifth indication information, and subsequently activate or deactivate the capability through the second information.

[0024] Optionally, the first information also includes sixth indication information, which indicates the N types of communication parameters. In this way, the recipient of the first information can determine, based on the sixth indication information, that the first communication device supports communication via the N types of communication parameters determined by AI, and can subsequently activate or deactivate some or all of the N types of communication parameters through the second information.

[0025] Optionally, the N-type communication parameters can be pre-configured or pre-defined, meaning the first information may not include the sixth indication information to reduce overhead.

[0026] In one possible implementation of the first aspect, the method further includes: the first communication device receiving or sending fourth information, the fourth information being used to indicate the value of the K-type communication parameter.

[0027] Based on the above scheme, the first communication device can also receive or send fourth information, so that the recipient of the fourth information can determine the value of the K-type communication parameters through the fourth information, so that the recipient can process the communication signal based on the value of the K-type communication parameters.

[0028] Optionally, the sender of the fourth information can determine the value of the K-type communication parameters using AI. For example, the value of the K-type communication parameters can be determined using an AI model.

[0029] In one possible implementation of the first aspect, the method further includes: the first communication device receiving or sending fifth information, the fifth information being used to determine an AI model, the AI ​​model being used to determine the values ​​of the K-type communication parameters.

[0030] Based on the above scheme, the first communication device can also receive or send fifth information, so that the recipient of the fifth information can determine the AI ​​model through the fifth information, so that the recipient can determine the value of the K-type communication parameter based on the AI ​​model.

[0031] Optionally, the fifth information includes at least one of the following: scene information corresponding to the K-type communication parameters, model information of the AI ​​model used to determine the parameter values ​​of the K-type communication parameters, system information corresponding to the K-type communication parameters, resource information used to transmit the parameter values ​​of the K-type communication parameters, or parameter type information of the K-type communication parameters.

[0032] For example, the fifth piece of information is used to determine the AI ​​model. This can be understood as the inference configuration of the AI ​​model being determined through the fifth piece of information. Accordingly, the fifth piece of information can be called inference configuration information or AI configuration information, etc.

[0033] In one possible implementation of the first aspect, the method further includes: the first communication device receiving sixth information, the sixth information being used to indicate that a P-type communication parameter is determined by a non-AI method, the P-type communication parameter being included in the K-type communication parameter, where P is a positive integer less than or equal to K.

[0034] Based on the above scheme, the sixth information received by the first communication device is used to instruct the determination of the P-type communication parameters in the K-type communication parameters through a non-AI method. That is, the sender of the sixth information (e.g., the second communication device) can activate some or all of the K-type communication parameters through the sixth information, so as to achieve flexible scheduling of the determination method of communication parameters through activation / deactivation signaling.

[0035] In one possible implementation of the first aspect, the sixth information is determined based on communication performance information of K types of communication parameters determined by AI. The method further includes: the first communication device sending a seventh information, which is used to determine the performance information.

[0036] Based on the above scheme, during the communication process of the first communication device using K types of communication parameters determined by AI, the performance information of this process can be used to determine the activation / deactivation of some or all of the K types of communication parameters. Accordingly, the first communication device can send a seventh message indicating the performance information, enabling the recipient of the seventh message to flexibly schedule the method of determining communication parameters based on the performance information through activation / deactivation signaling.

[0037] The second aspect of this application provides a communication method applied to a second communication device, such as being executed by the second communication device, which may be a communication device (e.g., a terminal device or a network device), or the second communication device may be a component of the communication device (e.g., a circuit or chip responsible for communication functions (e.g., a modem chip, also known as a baseband chip, or a SoC chip or SIP chip containing a modem core, etc.), or the second communication device may also be a logic module or software capable of implementing all or part of the functions of the communication device.

[0038] In this method, the second communication device receives first information, which indicates that the first communication device supports communication through N types of communication parameters, where N is a positive integer; wherein the N types of communication parameters are determined based on AI; the second communication device sends second information, which indicates K types of communication parameters, where K is a positive integer less than or equal to N; wherein the K types of communication parameters are included in the N types of communication parameters.

[0039] Based on the above scheme, after receiving first information indicating that the first communication device supports communication via N types of communication parameters, the second communication device sends second information to the first communication device to indicate K types of communication parameters among the N types of communication parameters. In other words, the second communication device can indicate K types of communication parameters based on the N types of communication parameters supported by the first communication device. Therefore, after determining the K types of communication parameters using AI, the first communication device can communicate based on these K types of communication parameters. This AI-based optimization of communication parameters can quickly determine communication parameters to reduce communication latency, and also enables the communication device to become intelligent through AI-determined communication parameters, thereby improving communication performance.

[0040] Furthermore, the second communication device can determine, through the first information, that the first communication device supports communication via N types of communication parameters determined using an AI-based method, and the K types of communication parameters indicated by the second information are some or all of the N types of communication parameters. In this way, the second information can indicate / schedule / configure communication parameters that are compatible with the capabilities of the first communication device, avoiding situations where the second information indicates communication parameters that the first communication device does not support, leading to indication / scheduling / configuration failures, thereby improving communication efficiency.

[0041] In one possible implementation of the second aspect, the second information includes K pieces of information, and the i-th piece of information among the K pieces of information includes first indication information and second indication information, where i takes the value from 1 to K; wherein, the first indication information is used to indicate the i-th type of communication parameter among the K types of communication parameters, and the second indication information is used to indicate that the i-th type of communication parameter is determined by AI.

[0042] Based on the above scheme, the second information may include K pieces of information. Each of the K pieces of information is used to configure and activate one of the K types of communication parameters determined by AI. That is, the first communication device can determine the K types of communication parameters by means of the indication of the second information (i.e., single-level indication), and can quickly realize the scheduling / indication / configuration of the K types of communication parameters to reduce communication latency.

[0043] In one possible implementation of the second aspect, the second information further includes M pieces of information, wherein the j-th piece of information includes a third indication information and a fourth indication information, where j takes values ​​from 1 to M, and M is a positive integer; wherein the third indication information is used to indicate the j-th type of communication parameter among the M types of communication parameters, and the second indication information is used to indicate that the j-th type of communication parameter is determined by a non-AI method.

[0044] Based on the above scheme, the second information may also include M pieces of information. Each of the M pieces of information is used to configure and activate one of the M types of communication parameters determined by a non-AI method. That is, the first communication device can realize the use of K types of communication parameters and M types of communication parameters through the indication of the second information, and can quickly realize the scheduling / indication / configuration of these communication parameters to reduce communication latency.

[0045] In one possible implementation of the second aspect, the method further includes: the second communication device sending third information for configuring the K-type communication parameters; wherein the second information is used to indicate the activation of the K-type communication parameters.

[0046] Based on the above scheme, the second communication device can also send third information to the first communication device for configuring K-type communication parameters. The second information is used to indicate the activation of the K-type communication parameters (i.e., two-level indication). That is, the first communication device determines the K-type communication parameters through the configuration of the third information and the indication of the second information (i.e., two-level indication). In this way, flexible scheduling of various communication parameters can be achieved.

[0047] In addition, the second information can indicate the activation or deactivation of one or more types of communication parameters, which can reduce the overhead of activation or deactivation indication.

[0048] In one possible implementation of the second aspect, the first information includes fifth indication information; wherein the fifth indication information is used to indicate that the first communication device has the ability to communicate through communication parameters determined by an AI-based method.

[0049] Based on the above scheme, the first information received by the second communication device may include the aforementioned fifth indication information, enabling the second communication device to clearly understand, based on the fifth indication information, that the first communication device has the ability to communicate through communication parameters determined by AI, and subsequently activate or deactivate this ability through the second information.

[0050] Optionally, the first information also includes sixth indication information, which indicates the N types of communication parameters. In this way, the recipient of the first information can determine, based on the sixth indication information, that the first communication device supports communication via the N types of communication parameters determined by AI, and can subsequently activate or deactivate some or all of the N types of communication parameters through the second information.

[0051] Optionally, the N-type communication parameters can be pre-configured or pre-defined, meaning the first information may not include the sixth indication information to reduce overhead.

[0052] In one possible implementation of the second aspect, the method further includes: the second communication device receiving or sending fourth information, the fourth information being used to indicate the value of the K-type communication parameter.

[0053] Based on the above scheme, the second communication device can also receive or send fourth information, so that the recipient of the fourth information can determine the value of the K-type communication parameters through the fourth information, so that the recipient can process the communication signal based on the value of the K-type communication parameters.

[0054] Optionally, the sender of the fourth information can determine the value of the K-type communication parameters using AI. For example, the value of the K-type communication parameters can be determined using an AI model.

[0055] In one possible implementation of the second aspect, the method further includes: the second communication device receiving or sending fifth information, the fifth information being used to determine an AI model, the AI ​​model being used to determine the values ​​of the K-type communication parameters.

[0056] Based on the above scheme, the second communication device can also receive or send fifth information, so that the recipient of the fifth information can determine the AI ​​model through the fifth information, so that the recipient can determine the value of the K-type communication parameter based on the AI ​​model.

[0057] Optionally, the fifth information includes at least one of the following: scene information corresponding to the K-type communication parameters, model information of the AI ​​model used to determine the parameter values ​​of the K-type communication parameters, system information corresponding to the K-type communication parameters, resource information used to transmit the parameter values ​​of the K-type communication parameters, or parameter type information of the K-type communication parameters.

[0058] For example, the fifth piece of information is used to determine the AI ​​model. This can be understood as the inference configuration of the AI ​​model being determined through the fifth piece of information. Accordingly, the fifth piece of information can be called inference configuration information or AI configuration information, etc.

[0059] In one possible implementation of the second aspect, the method further includes: the second communication device sending a sixth message, the sixth message being used to instruct the determination of a P-type communication parameter by a non-AI method, the P-type communication parameter being included in the K-type communication parameter, where P is a positive integer less than or equal to K.

[0060] Based on the above scheme, the sixth message sent by the second communication device to the first communication device is used to instruct the determination of the P-type communication parameters in the K-type communication parameters through a non-AI method. That is, the second communication device can activate some or all of the K-type communication parameters through the sixth message, so as to achieve flexible scheduling of the determination method of communication parameters through activation / deactivation signaling.

[0061] In one possible implementation of the second aspect, the sixth information is determined based on communication performance information of K types of communication parameters determined by AI. The method further includes: the second communication device receiving seventh information, which is used to determine the performance information.

[0062] Based on the above scheme, during the communication process of the first communication device using K types of communication parameters determined by AI, the performance information of this process can be used to determine the activation / deactivation of some or all of the K types of communication parameters. Correspondingly, the second communication device can receive a seventh message from the first communication device indicating this performance information, enabling the second communication device to flexibly schedule the method of determining communication parameters based on this performance information through activation / deactivation signaling.

[0063] A third aspect of this application provides a communication device, which includes a processing unit and a transceiver unit. The processing unit is used to determine first information. The transceiver unit is used to send the first information, which indicates that a first communication device supports communication through N types of communication parameters, where N is a positive integer. The N types of communication parameters are determined based on an AI method. The transceiver unit is also used to receive second information, which indicates K types of communication parameters, where K is a positive integer less than or equal to N. The K types of communication parameters are included in the N types of communication parameters.

[0064] In the third aspect of this application, the constituent modules of the communication device can also be used to execute the steps performed in various possible implementations of the first aspect and achieve the corresponding technical effects. For details, please refer to the first aspect, which will not be repeated here.

[0065] A fourth aspect of this application provides a communication device, which includes a transceiver unit and a processing unit. The transceiver unit is used to receive first information, which indicates that a first communication device supports communication through N types of communication parameters, where N is a positive integer. The N types of communication parameters are determined based on an AI method. The processing unit is used to determine second information. The transceiver unit is also used to send the second information, which indicates K types of communication parameters, where K is a positive integer less than or equal to N. The K types of communication parameters are included in the N types of communication parameters.

[0066] In the fourth aspect of this application, the constituent modules of the communication device can also be used to perform the steps executed in various possible implementations of the second aspect and achieve the corresponding technical effects. For details, please refer to the second aspect, which will not be repeated here.

[0067] The fifth aspect of this application provides a communication device including at least one processor for executing computer programs or instructions to enable the device to implement the method described in any one of the first to second aspects and any possible implementation thereof.

[0068] Optionally, the at least one memory is coupled to a memory used to store computer programs or instructions.

[0069] Optionally, the communication device includes the memory.

[0070] The sixth aspect of this application provides a communication device including at least one logic circuit and an input / output interface; the logic circuit is used to perform the method as described in any one of the possible implementations of the first to second aspects described above.

[0071] The seventh aspect of this application provides a communication system, which includes the first communication device and the second communication device described above.

[0072] An eighth aspect of this application provides a computer-readable storage medium for storing one or more computer-executable instructions, which, when executed by a processor, perform the method as described in any possible implementation of any of the first to second aspects described above.

[0073] The ninth aspect of this application provides a computer program product (or computer program) that, when executed by a processor, performs the method described in any possible implementation of any of the first to second aspects described above.

[0074] The tenth aspect of this application provides a chip or chip system including at least one processor for supporting a communication device in implementing the methods described in any possible implementation of any of the first to second aspects. For example, the chip may be a baseband chip, a modem chip, a system-on-a-chip (SoC) chip containing a modem core, a system-in-package (SIP) chip, or a communication module, etc.

[0075] In one possible design, the chip or chip system may further include a memory for storing program instructions and data necessary for the communication device. The chip system may be composed of chips or may include chips and other discrete devices. Optionally, the chip system may also include interface circuitry that provides program instructions and / or data to the at least one processor.

[0076] The technical effects of any of the design methods in aspects three through ten can be found in the technical effects of the different design methods in aspects one through two above, and will not be repeated here. Attached Figure Description

[0077] Figures 1a to 1c are schematic diagrams of the communication system provided in this application;

[0078] Figures 2a to 2g are schematic diagrams of the AI ​​processing involved in this application;

[0079] Figure 3 is an interactive schematic diagram of the communication method provided in this application;

[0080] Figures 4a to 4h are some schematic diagrams showing the application of the communication method provided in this application;

[0081] Figures 5 to 9 are schematic diagrams of the communication device provided in this application. Detailed Implementation

[0082] First, some terms used in the embodiments of this application will be explained to facilitate understanding by those skilled in the art.

[0083] (1) Terminal device: can be a wireless terminal device that can receive network device scheduling and instruction information. The wireless terminal device can be a device that provides voice and / or data connectivity to the user, or a handheld device with wireless connection function, or other processing device connected to a wireless modem.

[0084] Terminal devices can communicate with one or more core networks or the Internet via a radio access network (RAN). Terminal devices can be mobile terminal devices, such as mobile phones (or "cellular" phones), computers, and data cards. For example, they can be portable, pocket-sized, handheld, computer-embedded, or vehicle-mounted mobile devices that exchange voice and / or data with the RAN. Examples include personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablets, and computers with wireless transceiver capabilities. Wireless terminal equipment can also be referred to as a system, subscriber unit, subscriber station, mobile station (MS), remote station, access point (AP), remote terminal, access terminal, user terminal, user agent, subscriber station (SS), customer premises equipment (CPE), terminal, user equipment (UE), mobile terminal (MT), etc.

[0085] By way of example and not limitation, in this embodiment, the terminal device can also be a wearable device. Wearable devices, also known as wearable smart devices or smart wearable devices, are a general term for devices that utilize wearable technology to intelligently design and develop everyday wearables, such as glasses, gloves, watches, clothing, and shoes. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not merely hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are feature-rich, large in size, and can achieve complete or partial functions without relying on a smartphone, such as smartwatches or smart glasses, as well as those that focus on a specific type of application function and require the use of other devices such as smartphones, such as various smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.

[0086] Terminals can also be drones, robots, devices in device-to-device (D2D) communication, vehicles to everything (V2X) communication, virtual reality (VR) terminal devices, augmented reality (AR) terminal devices, wireless terminals in industrial control, wireless terminals in self-driving, wireless terminals in telemedicine or telehealth services, wireless terminals in smart grids, wireless terminals in transportation safety, wireless terminals in smart cities, wireless terminals in smart homes, etc.

[0087] Furthermore, the terminal device can also be a terminal device for a communication system evolved from the fifth generation (5G) communication system (such as 5G Advanced or future communication systems). For example, the form and function of the communication terminal can be further expanded, including but not limited to vehicles, cellular network terminals (integrating satellite terminal functions), drones, and Internet of Things (IoT) devices.

[0088] In this embodiment, the terminal device can also obtain artificial intelligence (AI) services provided by the network device. Optionally, the terminal device can also have AI processing capabilities.

[0089] (2) Network equipment: This can be equipment within a wireless network, such as RAN nodes (or devices) that connect terminal devices to the wireless network. Examples of RAN equipment currently include: base stations, evolved NodeBs (eNodeBs), gNBs (gNodeBs) in 5G communication systems, transmission reception points (TRPs), evolved Node Bs (eNBs), radio network controllers (RNCs), Node Bs (NBs), home base stations (e.g., home evolved Node Bs, or home Node Bs (HNBs), base band units (BBUs), or wireless fidelity (Wi-Fi) access points (APs). Additionally, in a network architecture, network equipment may include central unit (CU) nodes, distributed unit (DU) nodes, or RAN equipment comprising both CU and DU nodes.

[0090] Optionally, the RAN node can also be a macro base station, micro base station, indoor station, relay node, donor node, or a radio controller in a cloud radio access network (CRAN) scenario. The RAN node can also be a server, wearable device, vehicle, or in-vehicle equipment. For example, the access network equipment in vehicle-to-everything (V2X) technology can be a roadside unit (RSU).

[0091] In another possible scenario, multiple RAN nodes collaborate to assist the terminal in achieving wireless access, with different RAN nodes each implementing some of the base station's functions. For example, RAN nodes can be CUs, DUs, CUs (control plane, CP), CUs (user plane, UP), or radio units (RUs). CUs and DUs can be configured separately or included in the same network element, such as a baseband unit (BBU). RUs can be included in radio equipment or radio units, such as remote radio units (RRUs), active antenna units (AAUs), radio heads (RHs), or remote radio heads (RRHs).

[0092] In different systems, CU (or CU-CP and CU-UP), DU, or RU may have different names, but those skilled in the art will understand their meaning. For example, in an open access network (open RAN, O-RAN, or ORAN) system, CU can also be called O-CU (open CU), DU can also be called O-DU, CU-CP can also be called O-CU-CP, CU-UP can also be called O-CU-UP, and RU can also be called O-RU. For ease of description, this application uses CU, CU-CP, CU-UP, DU, and RU as examples. Any of the units among CU (or CU-CP, CU-UP), DU, and RU in this application can be implemented through software modules, hardware modules, or a combination of software modules and hardware modules.

[0093] Communication between access network devices and terminal devices follows a specific protocol layer structure. This protocol layer may include a control plane protocol layer and a user plane protocol layer. The control plane protocol layer may include at least one of the following: radio resource control (RRC) layer, packet data convergence protocol (PDCP) layer, radio link control (RLC) layer, media access control (MAC) layer, or physical (PHY) layer, etc. The user plane protocol layer may include at least one of the following: service data adaptation protocol (SDAP) layer, PDCP layer, RLC layer, MAC layer, or physical layer, etc.

[0094] The correspondence between network elements and their achievable protocol layer functions in the ORAN system can be found in Table 1 below.

[0095] Table 1

[0096] Network devices can be other devices that provide wireless communication functions for terminal devices. The embodiments of this application do not limit the specific technology or form of the network device. For ease of description, the embodiments of this application are not limited.

[0097] Network equipment may also include core network equipment, such as the Mobility Management Entity (MME), Home Subscriber Server (HSS), Serving Gateway (S-GW), Policy and Charging Rules Function (PCRF), and Public Data Network Gateway (PDN gateway or P-GW) in 4th generation (4G) networks; and access and mobility management function (AMF), user plane function (UPF), or session management function (SMF) in 5G networks. Furthermore, this core network equipment may also include other core network equipment in 5G networks and future networks.

[0098] In this embodiment of the application, the network device may also have network nodes with AI capabilities, which can provide AI services to terminals or other network devices. For example, it may be an AI node, computing node, RAN node with AI capabilities, or core network element with AI capabilities on the network side (access network or core network).

[0099] In this application embodiment, the device for implementing the function of the network device can be the network device itself, or it can be a device capable of supporting the network device in implementing the function, such as a chip system. This device can be disposed within the network device. In the technical solutions provided in this application embodiment, the example of a network device being used to implement the function of the network device is used to describe the technical solutions provided in this application embodiment.

[0100] (3) Configuration and Pre-configuration: In this application, both configuration and pre-configuration are used. Configuration refers to the network device / server sending configuration information or parameter values ​​to the terminal via messages or signaling, so that the terminal can determine communication parameters or resources for transmission based on these values ​​or information. Pre-configuration is similar to configuration; it can be parameter information or parameter values ​​pre-negotiated between the network device / server and the terminal device, parameter information or parameter values ​​specified by standard protocols for use by the base station / network device or terminal device, or parameter information or parameter values ​​pre-stored in the base station / server or terminal device. This application does not limit this.

[0101] Furthermore, these values ​​and parameters can be changed or updated.

[0102] (4) The terms "system" and "network" in the embodiments of this application can be used interchangeably. "Multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, or B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the related objects before and after are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, "at least one of A, B and C" includes A, B, C, AB, AC, BC or ABC. And, unless otherwise specified, the ordinal numbers such as "first" and "second" mentioned in the embodiments of this application are used to distinguish multiple objects and are not used to limit the order, sequence, priority or importance of multiple objects.

[0103] (5) In the embodiments of this application, "send" and "receive" indicate the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information being XX, which may include sending directly through the air interface or sending indirectly through the air interface by other units or modules. "Receive information from YY" can be understood as the source of the information being YY, which may include receiving directly from YY through the air interface or receiving indirectly from YY through the air interface by other units or modules. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface.

[0104] Optionally, sending and receiving can be performed between devices, such as between network devices and terminal devices, or within a device, such as between components, modules, chips, software modules, or hardware modules within the device via a bus, wiring, or interface.

[0105] Optionally, the information may undergo necessary processing, such as encoding or modulation, between the source and destination, but the destination can still understand the valid information from the source. Similar statements in this application can be understood in a similar way and will not be elaborated further.

[0106] (6) In the embodiments of this application, "instruction" may include direct instruction and indirect instruction, as well as explicit instruction and implicit instruction. The information indicated by a certain piece of information (as described below, the instruction information) is called the information to be instructed. In the specific implementation process, there are many ways to indicate the information to be instructed, such as, but not limited to, directly indicating the information to be instructed, such as the information to be instructed itself or its index. It can also indirectly indicate the information to be instructed by indicating other information, where there is an association between the other information and the information to be instructed; or it can only indicate a part of the information to be instructed, while the other parts of the information to be instructed are known or pre-agreed upon. For example, the instruction of specific information can be achieved by using a pre-agreed (e.g., protocol predefined) arrangement order of various information, thereby reducing the instruction overhead to a certain extent. This application does not limit the specific method of instruction. Optionally, for the sender of the instruction information, the instruction information can be used to indicate the information to be instructed; for the receiver of the instruction information, the instruction information can be used to determine the information to be instructed.

[0107] In this application, unless otherwise specified, the same or similar parts between the various embodiments can be referred to each other. In the various embodiments of this application, and the various methods / designs / implementations within each embodiment, unless otherwise specified or logically conflicting, the terminology and / or descriptions between different embodiments and between the various methods / designs / implementations within each embodiment are consistent and can be mutually referenced. The technical features in different embodiments and the various methods / designs / implementations within each embodiment can be combined to form new embodiments, methods, or implementations based on their inherent logical relationships. The following descriptions of the embodiments of this application do not constitute a limitation on the scope of protection of this application.

[0108] This application can be applied to long-term evolution (LTE) systems, new radio (NR) systems, or future communication systems beyond 5G. These communication systems include at least one network device and / or at least one terminal device.

[0109] Please refer to Figure 1a, which is a schematic diagram of the architecture of the communication system 1000 used in the embodiments of this application. As shown in Figure 1a, the communication system may include a radio access network (RAN) 100. Optionally, the communication system 1000 may also include a core network 200 and an Internet 300. The RAN 100 includes at least one RAN node (110a and 110b in Figure 1a, collectively referred to as 110) and at least one terminal (120a-120j in Figure 1a, collectively referred to as 120). The RAN 100 may also include other RAN nodes, such as wireless relay devices and / or wireless backhaul devices (not shown in Figure 1a). The terminal 120 is wirelessly connected to the RAN node 110, and the RAN node 110 is wirelessly or wiredly connected to the core network 200. The core network equipment in the core network 200 and the RAN node 110 in the RAN 100 may be independent and different physical devices, or they may be the same physical device integrating the logical functions of the core network equipment and the logical functions of the RAN node. Terminals can be connected to each other, as can RAN nodes, via wired or wireless means.

[0110] Taking the communication system shown in Figure 1a as an example, in addition to performing communication-related services, different devices (including network devices and network devices, network devices and terminal devices, and / or terminal devices and terminal devices) may also perform AI-related services.

[0111] As shown in Figure 1b, taking a network device as a base station as an example, the base station can perform communication-related services and AI-related services with one or more terminal devices, and different terminal devices can also perform communication-related services and AI-related services.

[0112] As shown in Figure 1c, taking terminal devices including televisions and mobile phones as an example, communication-related services and AI-related services can also be performed between televisions and mobile phones.

[0113] The technical solutions provided in this application can be applied to wireless communication systems (such as the systems shown in Figures 1a, 1b, or 1c). For example, AI network elements can be introduced into the communication system provided in this application to realize some or all AI-related operations. AI network elements can also be called AI nodes, AI devices, AI entities, AI modules, AI models, or AI units, etc. The AI ​​network element can be built into a network element within the communication system. For example, the AI ​​network element can be an AI module built into: access network equipment, core network equipment, cloud server, or operation, administration, and maintenance (OAM) to realize AI-related functions. The OAM can act as the network management system for the core network equipment and / or the access network equipment. Alternatively, the AI ​​network element can also be an independently set network element in the communication system. Optionally, the terminal or its built-in chip can also include an AI entity to realize AI-related functions.

[0114] Optionally, in communication systems, AI application cases may include, but are not limited to: channel state information (CSI) feedback enhancement, beam management enhancement, positioning accuracy enhancement, network energy saving, load balancing, and mobility optimization. These will be explained below.

[0115] 1. Enhanced CSI feedback

[0116] Channel quality information (CSI) is the channel attribute of a communication link, reported by the terminal device to the network device. By reporting this information, the terminal device can select an appropriate modulation and coding scheme (MCS) to adapt to changing wireless channels. For example, the terminal device might perform channel estimation based on the received channel state information-reference signal (CSI-RS) and then feed back the CSI-RS to the network device. This feedback CSI-RS information serves as input to the network device's model, enabling AI model training. Applying AI to CSI feedback enhancement can reduce overhead, improve accuracy, and enhance predictive capabilities.

[0117] CSI-RS feedback enhancement may include at least one sub-function, such as: CSI compression, CSI prediction, and CSI-RS configuration signaling reduction. CSI compression may further include CSI compression in at least one domain: spatial, time, and frequency.

[0118] 2. Enhanced Beam Management

[0119] Enhanced beam management primarily aims to discover the strongest transmit / receive beam pairs. AI-based sparse beam prediction can improve accuracy. This can be achieved through both network-side and terminal-side AI sparse beam prediction, based on AI training and inference. Taking terminal-side AI sparse beam prediction as an example, the pre-trained AI model on the terminal device can be provided by the network or pre-stored on the terminal device. During training, the network device scans all possible beams and then provides the transmit beam pattern to the terminal device. Once training is complete, the network device only needs to scan a small subset of beams, and the terminal device then feeds back the inference results to the network device. AI-based beam management can achieve beam prediction in, for example, the temporal and / or spatial domains, reducing overhead and latency and improving beam selection accuracy.

[0120] Beam management enhancements may include at least one sub-function, such as beam scan matrix prediction and / or optimal beam prediction.

[0121] 3. Enhanced positioning accuracy

[0122] In line-of-sight (LOS) or non-line-of-sight (NLOS) scenarios, AI-based positioning can improve positioning accuracy with a smaller number of TRP antennas. Positioning enhancement can include at least one sub-function, such as: positioning enhancement based on access network devices, positioning enhancement based on positioning management function network elements, and positioning enhancement based on terminal devices.

[0123] 4. Network energy saving

[0124] Network energy conservation can be achieved through cell activation / deactivation, load reduction, coverage improvement, or other RAN setting adjustments. AI technology can be used to optimize energy-saving decisions by leveraging data collected within the RAN network. AI algorithms can predict energy efficiency and load status for the next cycle, which can be used to assist in cell activation / deactivation decisions to save energy. Based on the predicted load, the system can dynamically configure energy-saving strategies to maintain a balance between system performance and energy efficiency, and reduce energy consumption.

[0125] 5. Load balancing

[0126] Load balancing can distribute the load evenly between cells and across different areas within a cell, or transfer some traffic from congested cells, or offload users across a single cell, carrier, or access standard, thereby improving network performance. Using AI models to enhance load balancing performance—such as inputting various measurements and feedback from terminal devices and network nodes, as well as historical data—can provide a higher quality user experience and increase system capacity.

[0127] 6. Mobility Management

[0128] Mobility management is a solution that ensures service continuity during terminal device mobility by minimizing dropped calls, radio link failures (RLFs), unnecessary handovers, and ping-pong effects. AI can enhance mobility management by, for example, reducing the probability of unexpected events, predicting at least one of the terminal device's location, mobility, or performance, and traffic redirection.

[0129] Optionally, the definitions of the above technical terms are merely illustrative. For example, as technology continues to develop, the scope of the above definitions may also change, and the embodiments of this application are not limited thereto.

[0130] For example, an AI function may include multiple AI sub-functions.

[0131] Alternatively, AI application cases are also referred to as AI application scenarios or AI functions.

[0132] As described above regarding AI application examples, AI can be widely used to improve network performance in areas such as CSI feedback enhancement, beam management, positioning accuracy enhancement, energy saving, mobility enhancement, and load balancing. AI models can typically be deployed on the network side and / or the terminal device side. The training of AI models relies on the collection of training data, which can come from measurements and feedback from the terminal devices.

[0133] The following is a brief introduction to the concepts that may be involved in this application.

[0134] AI can endow machines with human-like intelligence, for example, allowing them to use computer hardware and software to simulate certain intelligent human behaviors. To achieve artificial intelligence, machine learning methods can be employed. In machine learning, machines learn (or train) a model using training data. This model represents the mapping between inputs and outputs. The learned model can be used for reasoning (or prediction), that is, it can be used to predict the output corresponding to a given input. This output can also be called the reasoning result (or prediction result).

[0135] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Unsupervised learning can also be called learning without supervision.

[0136] Supervised learning, based on collected sample values ​​and labels, uses machine learning algorithms to learn the mapping relationship between sample values ​​and labels, and then expresses this learned mapping relationship using an AI model. The process of training the machine learning model is the process of learning this mapping relationship. During training, sample values ​​are input into the model to obtain the model's predicted values, and the model parameters are optimized by calculating the error between the model's predicted values ​​and the sample labels (ideal values). After the mapping relationship is learned, it can be used to predict new sample labels. The mapping relationship learned in supervised learning can include linear or non-linear mappings. Based on the type of label, the learning task can be divided into classification tasks and regression tasks.

[0137] Unsupervised learning relies on collected sample values ​​to discover inherent patterns within the samples themselves. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping relationship from sample to sample; this is called self-supervised learning. During training, model parameters are optimized by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used for signal compression and decompression recovery applications; common algorithms include autoencoders and generative adversarial networks.

[0138] Reinforcement learning, unlike supervised learning, is a type of algorithm that learns problem-solving strategies through interaction with the environment. Unlike supervised and unsupervised learning, reinforcement learning problems do not have explicit "correct" action labels. The algorithm needs to interact with the environment to obtain reward signals from the environment, and then adjust its decision actions to obtain a larger reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmission power of each user based on the total system throughput feedback from the wireless network, aiming to achieve a higher system throughput. The goal of reinforcement learning is also to learn the mapping relationship between the environment state and a better (e.g., optimal) decision action. However, because the label of the "correct action" cannot be obtained in advance, the network cannot be optimized by calculating the error between the action and the "correct action." Reinforcement learning training is achieved through iterative interaction with the environment.

[0139] Neural networks (NNs) are a specific model in machine learning techniques. According to the general approximation theorem, neural networks can theoretically approximate any continuous function, thus enabling them to learn arbitrary mappings. Traditional communication systems rely on extensive expert knowledge to design communication modules, while deep learning communication systems based on neural networks can automatically discover hidden pattern structures from large datasets, establish mapping relationships between data, and achieve performance superior to traditional modeling methods.

[0140] The idea behind neural networks comes from the neuronal structure of the brain. For example, each neuron performs a weighted summation of its input values ​​and outputs the result through an activation function.

[0141] Figure 2a shows a schematic diagram of a neuron structure. Assume the input to the neuron is x = [x0, x1, ..., x...]. n The weights corresponding to each input are w = [w0, w1, ..., w] n ], where n is a positive integer, w i and x i It can be any possible type, such as a decimal, an integer (e.g., 0, a positive integer, or a negative integer), or a complex number. i As x i The weights are used to assign weights to x. i Weighting is applied. The bias for the weighted sum of the input values ​​is, for example, b. Activation functions can take many forms. Suppose the activation function of a neuron is: y = f(z) = max(0, z), then the output of that neuron is: For example, if the activation function of a neuron is y = f(z) = z, then the output of that neuron is: Here, b can be any possible type, such as a decimal, an integer (e.g., 0, a positive integer, or a negative integer), or a complex number. The activation functions of different neurons in a neural network can be the same or different.

[0142] Furthermore, neural networks generally consist of multiple layers, each of which may include one or more neurons. Increasing the depth and / or width of a neural network can improve its expressive power, providing more powerful information extraction and abstract modeling capabilities for complex systems. The depth of a neural network can refer to the number of layers it includes, and the number of neurons in each layer can be called the width of that layer. In one implementation, a neural network includes an input layer and an output layer. The input layer processes the received input information through neurons and passes the processing result to the output layer, which then obtains the output of the neural network. In another implementation, a neural network includes an input layer, hidden layers, and an output layer. The input layer processes the received input information through neurons and passes the processing result to the hidden layer. The hidden layer calculates the received processing result and passes the calculation result to the output layer or the next adjacent hidden layer, ultimately obtaining the output of the neural network. A neural network may include one hidden layer or multiple sequentially connected hidden layers, without limitation.

[0143] Neural networks, for example, are deep neural networks (DNNs). Depending on how the network is constructed, DNNs can include feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

[0144] Figure 2b is a schematic diagram of an FNN network. A characteristic of FNN networks is that neurons in adjacent layers are completely connected pairwise. This characteristic makes FNNs typically require a large amount of storage space, leading to high computational complexity.

[0145] CNNs are neural networks specifically designed to process data with a grid-like structure. For example, time-series data (e.g., discrete sampling along a time axis) and image data (e.g., two-dimensional discrete sampling) can both be considered grid-like data. CNNs do not use all the input information at once for computation; instead, they use a fixed-size window to extract a portion of the information for convolution operations, which significantly reduces the computational cost of model parameters. Furthermore, depending on the type of information extracted by the window (e.g., people and objects in an image represent different types of information), each window can use different convolution kernels, allowing CNNs to better extract features from the input data.

[0146] Recurrent Neural Networks (RNNs) are a type of neural network that utilizes feedback time-series information. The input to an RNN includes the current input value and its own output value from the previous time step. RNNs are suitable for acquiring temporally correlated sequence features, and are applicable to applications such as speech recognition and channel coding / decoding.

[0147] In the model training process described above, a loss function can be defined. The loss function describes the difference between the model's output value and the ideal target value. The loss function can be expressed in various forms, and there are no restrictions on its specific form. The model training process can be viewed as follows: by adjusting some or all of the model's parameters, the value of the loss function is made to be less than a threshold or to meet the target requirement.

[0148] A model can also be called an AI model, a rule, or other names. An AI model can be considered a specific method for implementing AI functions. An AI model represents the mapping relationship or function between the model's input and output. AI functions can include one or more of the following: data collection, model training (or model learning), model information dissemination, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model validation, or inference result publication, etc. AI functions can also be called AI (related) operations or AI-related functions.

[0149] The implementation process of the neural network will be described below with reference to the accompanying drawings.

[0150] Taking a fully connected neural network as an example, a fully connected neural network is also called a multilayer perceptron (MLP).

[0151] As shown in Figure 2c, an MLP consists of an input layer (left side), an output layer (right side), and multiple hidden layers (middle). Each layer of an MLP contains several nodes, called neurons. Neurons in adjacent layers are connected pairwise.

[0152] Optionally, considering neurons in two adjacent layers, the output h of the next layer's neurons is the weighted sum of all neurons x in the previous layer connected to it, processed by an activation function, and can be expressed as: h = f(wx + b).

[0153] Where w is the weight matrix, b is the bias vector, and f is the activation function.

[0154] Alternatively, the output of the neural network can be recursively expressed as: y = f z (w z f z-1 (…)+b z ).

[0155] Where z is the index of the neural network layer, z is greater than or equal to 1 and z is less than or equal to Z, where Z is the total number of layers in the neural network.

[0156] In other words, a neural network can be understood as a mapping from an input data set to an output data set. Neural networks are typically initialized randomly; the process of obtaining this mapping from random values ​​w and b using existing data is called training the neural network.

[0157] Optionally, the training process may involve evaluating the output of the neural network using a loss function.

[0158] As shown in Figure 2d, the error can be backpropagated, and the neural network parameters (including w and b) can be iteratively optimized using gradient descent until the output of the loss function reaches its minimum value, which is the "better point (e.g., the optimal point)" in Figure 2d. Optionally, the neural network parameters corresponding to the "better point (e.g., the optimal point)" in Figure 2d can be used as the neural network parameters in the trained AI model information.

[0159] Alternatively, the gradient descent process can be represented as:

[0160] Where θ represents the parameters to be optimized (including w and b), L is the loss function, and η is the learning rate, controlling the step size of gradient descent. This represents the differentiation operation. This indicates taking the derivative of θ with respect to L.

[0161] Alternatively, the backpropagation process may utilize the chain rule for partial derivatives.

[0162] As shown in Figure 2e, the gradient of the parameters in the previous layer can be recursively calculated from the gradient of the parameters in the next layer, and can be expressed as:

[0163] Among them, w ij Let s be the weight of the connection between node j and node i. i The weighted sum of the inputs at node i.

[0164] The technical solutions provided in this application can be applied to wireless communication systems (such as the systems shown in Figure 1a, 1b, or 1c). In wireless communication systems, communication nodes generally possess signal transmission and reception capabilities as well as computing capabilities. To address the vision of future intelligent and inclusive accessibility, intelligence will further evolve at the wireless network architecture level, taking AI / machine learning (ML) as an example. In future networks, AI / ML will be further deeply integrated with wireless networks to achieve network-native intelligence, which also includes the intelligence of terminals.

[0165] For example, neural network-based AI / ML can be applied to wireless communication systems. Through data-driven training, accurate modeling and prediction of wireless data can be achieved. For instance, AI / ML can be used for various wireless tasks, such as time-frequency domain channel estimation, prediction, and beam prediction related to wireless channels. AI / ML models for specific tasks require data collection, model training, selection, switching, and retraining based on applicable conditions.

[0166] As an example, Figure 2f illustrates how AI models can be deployed in an overlay configuration within a wireless communication system. For instance, the AI ​​model itself does not participate in signal processing but only designs and optimizes communication parameters. For example, the overlay model can output a constellation diagram, which the modulation and demodulation modules can use for modulation and demodulation. The overlay model can also output a Polar code encoding sequence and / or a base map of LDPC codes, which the channel coding and decoding modules can use for channel coding and decoding. Furthermore, the overlay model can output filter coefficients, which the transmitter can use for signal filtering, and so on.

[0167] As an example, as shown in Figure 2g, the current 3GPP discussion provides a functional framework for using AI / ML to improve air interface performance, including data collection, model training, management, inference, and model storage. Optionally, these functions may interact with each other. For example, the data collection module can provide data to the model training, management, and inference modules; the model training and management modules can provide models to the model storage module; and the model storage module can provide models for inference.

[0168] However, the above process only provides a framework of functional description. As for communication devices, there is currently no solution to manage the communication parameters determined by the AI ​​model.

[0169] To address the aforementioned problems, this application provides a communication method and related apparatus, which will be described in detail below with reference to the accompanying drawings.

[0170] Please refer to Figure 3, which is a schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.

[0171] Optionally, in the following text, Figure 3 illustrates the method using a first communication device and other communication devices (such as a second communication device) as examples of the execution subjects of this interaction illustration, but this application does not limit the execution subjects of this interaction illustration. For example, the communication device can be a communication device (such as a terminal device or a network device), or a chip, baseband chip, modem chip, SoC chip (such as an SoC chip containing a modem core), SIP chip, communication module, chip system, processor, logic module, or software in the communication device.

[0172] As an example, the first communication device can be a terminal device and the second communication device can be a network device.

[0173] As another example, the first communication device can be a network device, and the second communication device can be a terminal device.

[0174] As another example, both the first and second communication devices are network devices.

[0175] Optionally, the aforementioned network equipment may be access network equipment or ORAN equipment (including at least one of O-CU, O-DU, and O-RU).

[0176] As another example, both the first and second communication devices are terminal devices, meaning that the scheme shown in Figure 3 can be applied to side link communication scenarios.

[0177] S301. The first communication device sends first information, and correspondingly, the second communication device receives the first information. The first information indicates that the first communication device supports communication via N types of communication parameters, where N is a positive integer; and the N types of communication parameters are determined based on an AI method.

[0178] In one possible implementation, the first information includes fifth indication information; wherein the fifth indication information is used to indicate that the first communication device has the ability to communicate using communication parameters determined by AI. In other words, the first information sent by the first communication device may include the aforementioned fifth indication information, enabling the recipient of the first information (e.g., a second communication device) to clearly understand, based on the fifth indication information, that the first communication device has the ability to communicate using communication parameters determined by AI, and subsequently activate or deactivate this ability using the second information.

[0179] Optionally, the first information also includes sixth indication information, which indicates the N types of communication parameters. In this way, the recipient of the first information can determine, based on the sixth indication information, that the first communication device supports communication via the N types of communication parameters determined by AI, and can subsequently activate or deactivate some or all of the N types of communication parameters through the second information.

[0180] Optionally, the N-type communication parameters can be pre-configured or pre-defined, meaning the first information may not include the sixth indication information to reduce overhead.

[0181] S302. The second communication device sends second information, and correspondingly, the first communication device receives the second information. The second information is used to indicate K types of communication parameters, where K is a positive integer less than or equal to N; and the K types of communication parameters are included in the N types of communication parameters.

[0182] Optionally, the communication parameters involved in this application may include parameters involved in the processing of communication signals (e.g., generation, transmission, parsing, etc.). For example, the communication parameters may include, but are not limited to, one or more of the following: constellation diagrams (e.g., downlink constellation points, uplink constellation points, etc., which can be used for modulation and / or demodulation), polar code sequences (which can be used for polar code encoding and decoding), low-density parity check (LDPC) code base diagrams (which can be used for LDPC code encoding and decoding), filter parameters (which can be used for filtering), digital predistortion (DPD) parameters, modulation parameters, pilot parameters, waveform parameters, precoding parameters, power control parameters, or scheduling parameters.

[0183] Optionally, determining communication parameters through AI can be understood as determining communication parameters in an intelligent manner. This intelligent manner includes, but is not limited to, technologies enabled by AI, neural networks, machine learning, reinforcement learning, deep learning, or large language model (LLM). Accordingly, the AI ​​method involved in this application can be replaced by technologies enabled by intelligence, neural networks, machine learning, reinforcement learning, deep learning, or LLM. Similarly, the non-AI methods mentioned below can be replaced by non-intelligent, non-neural network, non-machine learning, non-reinforcement learning, non-deep learning, or non-LLM technologies, or the non-AI methods can also be replaced by traditional or conventional methods.

[0184] Optionally, in the above scheme, K equals 0. In other words, the sender of the second information (e.g., the second communication device) determines, based on certain information (e.g., the fifth information mentioned below, including but not limited to one or more of scene information, environmental information, and system information), that the first communication device is not suitable for communication based on communication parameters determined by AI. In this case, the second information indicates that the communication parameters are determined by a non-AI method, or the second information indicates that the communication parameters are not determined by AI, so that the first communication device can fall back to the non-AI method for communication based on the indication, and that the indication / scheduling / configuration of the second information can be adapted to the inference configuration information.

[0185] Based on the scheme shown in Figure 3, after the first communication device sends first information in step S301 indicating that it supports communication via N types of communication parameters, in step S302, the second information received by the first communication device is used to indicate K types of communication parameters among the N types of communication parameters. In other words, the sender of the second information can indicate K types of communication parameters based on the N types of communication parameters supported by the first communication device. Therefore, after determining the K types of communication parameters based on AI, the first communication device can communicate based on these K types of communication parameters to optimize the communication parameters through AI. This optimization method can quickly determine communication parameters to reduce communication latency, and also enables the communication device to become intelligent through the communication parameters determined by AI, thereby improving communication performance.

[0186] Furthermore, the recipient of the first information (e.g., a second communication device) can determine through the first information that the first communication device supports communication using N types of communication parameters determined by AI, and the K types of communication parameters indicated by the second information are some or all of the N types of communication parameters. In this way, the second information can indicate / schedule / configure communication parameters that are compatible with the capabilities of the first communication device, avoiding situations where the second information indicates communication parameters that the first communication device does not support, leading to indication / schedule / configuration failures, thereby improving communication efficiency.

[0187] Optionally, the second information can indicate K-type communication parameters in various ways, which will be described below with some implementation examples.

[0188] Example 1: The second information indicates K-type communication parameters through a single-level parameter.

[0189] In one possible implementation of Example 1, the second information includes K pieces of information. The i-th piece of information includes a first indication and a second indication, where i ranges from 1 to K. The first indication is used to indicate the i-th type of communication parameter among the K types of communication parameters, and the second indication is used to indicate that the i-th type of communication parameter is determined by AI (or, the second indication is used to activate the i-th type of communication parameter, or, the second indication is used to activate the i-th type of communication parameter determined by AI). Therefore, the second information can include K pieces of information, each of which is used to configure and activate one type of parameter among the K types of communication parameters determined by AI. That is, the first communication device can determine the K types of communication parameters through the indication of the second information (i.e., a single-level indication), enabling rapid scheduling / indication / configuration of the K types of communication parameters to reduce communication latency.

[0190] Optionally, in Implementation Example 1, the second information further includes M pieces of information, the j-th piece of information among the M pieces of information including a third indication information and a fourth indication information, where j takes the value from 1 to M, and M is a positive integer; wherein, the third indication information is used to indicate the j-th type of communication parameter among the M types of communication parameters, and the second indication information is used to indicate that the j-th type of communication parameter is determined by a non-AI method.

[0191] Optionally, the value of M is less than or equal to NK, that is, the value of N is greater than or equal to the sum of M and K.

[0192] Therefore, the second information may also include M pieces of information, each of which is used to configure and activate one of the M types of communication parameters determined by a non-AI method. That is, the first communication device can realize the use of K types of communication parameters and M types of communication parameters through the indication of the second information, and can quickly realize the scheduling / indication / configuration of these communication parameters to reduce communication latency.

[0193] As an example, Figure 4a illustrates a single-level parameter activation / deactivation signaling structure. In Figure 4a, taking 5 (N=5) types of communication parameters as an example, the second communication device can list the configurable parameter flags supported by the first communication device in the signaling (e.g., RRC / downlink control information (DCI) / uplink control information (UCI) / UAI, etc.), and activate or deactivate the communication parameters by setting the corresponding flag to 1 or 0. For example, the activated communication parameters are the aforementioned type K communication parameters, and the deactivated parameters are the aforementioned type M communication parameters.

[0194] For example, in Figure 4a, when the 5 (N=5) flags are set to "00001" (e.g., the second information indicator "00001"), K=1 and M=4, that is, the activated K-type communication parameters are power control parameters, and the first communication device can then communicate based on the power control parameters determined by AI. In addition, the deactivated M-type communication parameters include modulation parameters, pilot parameters, waveform parameters and precoding parameters, and the first communication device can then communicate based on the modulation parameters, pilot parameters, waveform parameters and precoding parameters determined by non-AI methods.

[0195] For example, in Figure 4a, when the 5 (N=5) flags are set to "10101" (e.g., the second information indicator "10101"), K=3 and M=2. That is, the activated K-type communication parameters are modulation parameters, power control parameters, and waveform parameters. The first communication device can then communicate based on the modulation parameters, power control parameters, and waveform parameters determined by AI. In addition, the deactivated M-type communication parameters include pilot parameters and precoding parameters. The first communication device can then communicate based on the pilot parameters and precoding parameters determined by non-AI methods.

[0196] For example, in Figure 4a, when the 5 (N=5) flags are set to "00000" (e.g., the second information indicator "00000"), K=0 and M=5, meaning there are no active K-type communication parameters. Furthermore, the deactivated M-type communication parameters include modulation parameters, pilot parameters, waveform parameters, precoding parameters, and power control parameters. Subsequently, the first communication device can communicate based on the modulation parameters, pilot parameters, waveform parameters, precoding parameters, and power control parameters determined in a non-AI manner.

[0197] For example, in Figure 4a, when the 5 (N=5) flags are set to "11111" (e.g., the second information indicator "11111"), K=5 and M=0, meaning there are no deactivated M-type communication parameters. Furthermore, the activated K-type communication parameters include modulation parameters, pilot parameters, waveform parameters, precoding parameters, and power control parameters. Subsequently, the first communication device can communicate based on the modulation parameters, pilot parameters, waveform parameters, precoding parameters, and power control parameters determined by AI.

[0198] Optionally, the values ​​of each flag bit can also represent activation / deactivation through other values, which are not limited here. For example, a value of 0 can represent activation and a value of 1 can represent deactivation.

[0199] Example 2: The second information indicates the K-type communication parameters through two levels of parameters.

[0200] In one possible implementation of Example 2, the method further includes: the first communication device receiving third information for configuring the K-type communication parameters; wherein the second information is used to indicate the activation of the K-type communication parameters.

[0201] Therefore, the first communication device can also receive third information for configuring K-type communication parameters, and the second information is used to indicate the activation of the K-type communication parameters (i.e., two-level indication). That is, the first communication device determines the K-type communication parameters through the configuration of the third information and the indication of the second information (i.e., two-level indication), thereby enabling flexible scheduling of various types of communication parameters.

[0202] In addition, the second information can indicate the activation or deactivation of one or more types of communication parameters, which can reduce the overhead of activation or deactivation indication.

[0203] As an example, Figures 4b and 4c show a schematic diagram of a two-level parameter activation / deactivation signaling structure.

[0204] As shown in Figure 4b, the second communication device can define the K-type communication parameters through the third information, and activate the K-type communication parameters through the second information. For example, the third information can be an RRC message or other messages / signaling / information defined by the future network, and the second information can be DCI / UCI / UAI or other messages / signaling / information defined by the future network.

[0205] As shown in Figure 4c, taking the third information as an example, there are two editable parameters (i.e., editable parameter 0 and editable parameter 1 in the figure). The second information can indicate the activation or deactivation of these two editable parameters through a flag. For example, a value of 1 indicates activation and a value of 0 indicates deactivation, or a value of 1 indicates deactivation and a value of 0 indicates activation. For specific implementation details, please refer to Figure 4a and related descriptions above.

[0206] As can be seen from the above examples, by using a two-level parameter activation / deactivation method, transmission overhead can be reduced and flexible scheduling / configuration of activated communication parameters can be achieved.

[0207] In one possible implementation, the method shown in Figure 3 further includes: the first communication device receiving or sending fourth information, which indicates the value of the K-type communication parameter. Thus, the first communication device can also receive or send the fourth information, enabling the recipient of the fourth information to determine the value of the K-type communication parameter, so that the recipient can process the communication signal based on the value of the K-type communication parameter.

[0208] Optionally, the sender of the fourth information can determine the value of the K-type communication parameters using AI. For example, the value of the K-type communication parameters can be determined using an AI model.

[0209] In one possible implementation, the method shown in Figure 3 further includes: the first communication device receiving or sending fifth information, the fifth information being used to determine an AI model, and the AI ​​model being used to determine the values ​​of the K types of communication parameters. Thus, the first communication device can also receive or send the fifth information, enabling the recipient of the fifth information to determine the AI ​​model through the fifth information, so that the recipient can determine the values ​​of the K types of communication parameters based on the AI ​​model.

[0210] Optionally, the fifth information includes at least one of the following: scene information corresponding to the K-type communication parameters, model information of the AI ​​model used to determine the parameter values ​​of the K-type communication parameters, system information corresponding to the K-type communication parameters, resource information used to transmit the parameter values ​​of the K-type communication parameters, or parameter type information of the K-type communication parameters.

[0211] For example, the fifth piece of information is used to determine the AI ​​model. This can be understood as the inference configuration of the AI ​​model being determined through the fifth piece of information. Accordingly, the fifth piece of information can be called inference configuration information or AI configuration information, etc.

[0212] In one possible implementation, the method shown in Figure 3 further includes: the first communication device receiving sixth information, which instructs that P-type communication parameters be determined using a non-AI method. These P-type communication parameters are included in the K-type communication parameters, where P is a positive integer less than or equal to K. Thus, the sixth information received by the first communication device instructs that P-type communication parameters within the K-type communication parameters be determined using a non-AI method. That is, the sender of the sixth information (e.g., the second communication device) can use the sixth information to activate some or all of the K-type communication parameters, thereby achieving flexible scheduling of the communication parameter determination method through activation / deactivation signaling.

[0213] Optionally, the sixth information is determined based on the performance information of communication using K types of communication parameters determined by AI. The method shown in Figure 3 further includes: the first communication device sending a seventh information, which is used to determine the performance information. Thus, during the communication process using K types of communication parameters determined by AI, the performance information of this process can be used to determine whether to activate or deactivate some or all of the K types of communication parameters. Correspondingly, the first communication device can send a seventh information indicating the performance information, enabling the recipient of the seventh information to flexibly schedule the determination method of communication parameters based on the performance information through activation / deactivation signaling.

[0214] As can be seen from the above implementation process, the solution involved in this application can be applied to a communication device that supports the determination of communication parameters based on AI, and the communication device can interact with another communication device to activate the communication parameters determined based on AI. Further examples will be provided below, where the communication parameters determined based on AI are used as an example of those determined through an overlay model.

[0215] As shown in Figure 4d, the process of using the overlay model includes the following steps.

[0216] Step 1. Capability Interaction.

[0217] As an example, as shown in Figure 4e, taking the communication device deploying the overlay model (i.e., the first communication device) as the terminal device and the network device on the other side as an example, the capability interaction process may involve the following steps. For example, the network device queries the User Equipment Capability Enquiry to see if the terminal device supports optimizing communication parameters through the overlay model, and which communication parameters support partial or complete optimization through the overlay model. For example, the network device adds fields as shown in Table 2 below to the UECapability Enquiry.

[0218] Table 2

[0219] In addition, terminal devices can report capability information in the User Equipment Capability Information (UECapabilityInformation). For example, add fields to UECapabilityInformation as shown in Table 3 below.

[0220] Table 3

[0221] Step 2. Inference Configuration. For example, the side where the model is located (terminal device or network device) obtains inference configuration information, including scene information (such as indoor / outdoor, idle / busy time, etc.), model information (such as model ID / model performance / model complexity), system information (such as antenna configuration), communication information (such as the location of communication resources for inference result distribution / reporting, such as the location in UAI / DCI / UCI), and parameter information to be configured (such as communication parameters to be configured for overlay model optimization), etc.

[0222] Step 3. Model Inference. For example, the model deployment side selects a model based on the inference configuration information, completes model inference, obtains communication parameters, and optionally sends the indication of these parameters to the other side.

[0223] Step 4. Parameter Activation. For example, the activation indication information carried by the counterparty in DCI or UAI / UCI indicates to the model deployment side to activate or deactivate the communication parameters optimized by the overlay model (i.e., use standard parameters).

[0224] Step 5. Performance Monitoring. Monitor the system performance using the optimized communication parameters with the Overlay model on either side, so that the model deployment side can obtain the monitoring results.

[0225] Step 6. Model Update. The model deployment side determines that model update conditions (such as performance degradation, counter / timer expiration) are met and initiates a model update. Optionally, the model deployment side may revert relevant communication parameters to standard parameters (e.g., parameters not determined by the overlay model).

[0226] To facilitate understanding of the above solution, more examples will be used to describe it below.

[0227] Please refer to the example shown in Figure 4f, which uses the first communication device deploying the Overlay model as the terminal device and the second communication device as the network device. In this example, the model deployed on the terminal device is an uplink filter coefficient optimization model.

[0228] Step 1. Capability Interaction. For example, in capability interaction, the terminal device reports its ability to support configurable uplink filter coefficients.

[0229] Step 2. Inference Configuration. Optionally, the network device sends inference configuration information to the terminal device, which includes at least one of scenario information, environment information, and system information.

[0230] Step 3. Model Inference. For example, based on the inference configuration information, the terminal device selects a suitable model, performs model inference, and obtains the optimized uplink filter coefficients.

[0231] Step 4. Optionally, report the uplink filter system configurability indication (or the uplink filter model / model parameter configurability indication) to the network device.

[0232] Step 5. Uplink AI filter configuration activation. For example, the network device instructs the terminal device to activate the uplink filter coefficients obtained based on AI in a single-stage or two-stage manner to perform AI-based uplink filter transmission.

[0233] Steps 6 and 7. After the transmission based on the uplink AI filter, the terminal device or network device monitors the performance of the model to obtain performance monitoring results.

[0234] Step 8. Model Update. When the performance monitoring results obtained by the terminal device in Step 7 indicate a deterioration in performance and / or the number of inference attempts reaches a preset value A, the model update is initiated. For example, the terminal device will revert the uplink filter coefficients to non-AI optimized coefficients.

[0235] Optionally, the model updates involved in this application may include model switching, model fine-tuning, or model training.

[0236] Optionally, as shown in Figure 4g, the uplink filter coefficient optimization model can also be deployed on the network side. The process can be referred to the process shown in Figure 4f. The difference is that after the model completes inference on the network device and obtains the uplink filter coefficients, the network device can send the uplink filter coefficients output by the overlay model to the terminal device and then activate the coefficients.

[0237] Please refer to the example shown in Figure 4h, which uses a network device as the first communication device deploying the Overlay model and a terminal device as the second communication device. In this example, the model deployed on the terminal device is a downlink constellation graph optimization model.

[0238] Step 1. Capability Interaction. For example, in capability interaction, the terminal device reports capabilities that support downlink constellation mapping.

[0239] Step 2. Inference Configuration. For example, the network device obtains inference configuration information, including the downlink channel signal-to-noise ratio (SNR) information reported by the terminal device, the bit distribution information statistically obtained by the network device, and one or more other scenario information, environmental information, and system information.

[0240] Step 3. Model Inference. For example, based on the inference configuration information, the network device selects an appropriate model, performs model inference, obtains an optimized downlink constellation diagram, and then instructs the terminal device on the constellation diagram.

[0241] Steps 4 and 5. Downlink constellation point configuration indication and activation. For example, network devices may use a single-level or two-level approach to instruct terminal devices to activate the AI-based downlink constellation map and perform AI-based downlink constellation map transmission.

[0242] Steps 6 and 7. After transmission based on downlink constellation points, the terminal device or network device monitors the performance of the model to obtain performance monitoring results.

[0243] Step 8. Model Update. When the network device receives performance monitoring results in Step 7 indicating a deterioration in performance and / or the number of inference attempts reaches a preset value A, a model update is initiated. For example, the network device may revert downlink constellation points to non-AI optimized coefficients.

[0244] Referring to Figure 5, this application embodiment provides a communication device 500. This communication device 500 can implement the functions of the first communication device (or second communication device) in the above method embodiments, and therefore can also achieve the beneficial effects of the above method embodiments. In this application embodiment, the communication device 500 can be the first communication device (or the second communication device), or it can be an integrated circuit or component inside the first communication device (or the second communication device), such as a chip, baseband chip, modem chip, SoC chip (e.g., an SoC chip containing a modem core), SIP chip, communication module, chip system, processor, etc.

[0245] Optionally, the transceiver unit 502 may include a transmitting unit and a receiving unit, which are used to perform transmitting and receiving respectively.

[0246] In one possible implementation, when the device 500 is used to execute the method performed by the first communication device in the preceding embodiments, the device 500 includes a processing unit 501 and a transceiver unit 502; the processing unit 501 is used to determine first information; the transceiver unit 502 is used to send the first information, which indicates that the first communication device supports communication through N types of communication parameters, where N is a positive integer; wherein the N types of communication parameters are determined based on AI; the transceiver unit 502 is also used to receive second information, which indicates K types of communication parameters, where K is a positive integer less than or equal to N; wherein the K types of communication parameters are included in the N types of communication parameters.

[0247] In one possible implementation, when the device 500 is used to execute the method performed by the second communication device in the preceding embodiments, the device 500 includes a transceiver unit 502; the transceiver unit 502 is used to receive first information, the first information being used to indicate that the first communication device supports communication through N types of communication parameters, where N is a positive integer; wherein, the N types of communication parameters are determined based on an AI method; the processing unit 501 is used to determine second information; the transceiver unit 502 is also used to send second information, the second information being used to indicate K types of communication parameters, where K is a positive integer less than or equal to N; wherein, the K types of communication parameters are included in the N types of communication parameters.

[0248] In one possible design, when the communication device 500 is a terminal device or a communication module within a terminal, the functionality of the processing unit 501 can be implemented by one or more processors. Specifically, the processor may include a modem chip, a SoC chip (such as a SoC chip containing a modem core), or a SIP chip. The functionality of the transceiver unit 502 can be implemented by transceiver circuitry.

[0249] In one possible design, when the communication device 500 is a circuit or chip in a terminal responsible for communication functions, such as a modem chip, a SoC chip, or a SoC chip or SIP chip containing a modem core, the function of the processing unit 501 can be implemented by a circuit system in the aforementioned chip that includes one or more processors or processor cores. The function of the transceiver unit 502 can be implemented by the interface circuitry or data transceiver circuitry on the aforementioned chip.

[0250] Optionally, the information execution process of the unit of the above-mentioned communication device 500 can be specifically referred to in the description of the method embodiment shown above in this application, and will not be repeated here.

[0251] Please refer to Figure 6, which is another schematic structural diagram of the communication device 600 provided in this application. The communication device 600 includes a logic circuit 601 and an input / output interface 602. The communication device 600 can be a chip or an integrated circuit.

[0252] In Figure 5, the transceiver unit 502 can be a communication interface, which can be the input / output interface 602 in Figure 6, and the input / output interface 602 can include an input interface and an output interface. Alternatively, the communication interface can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.

[0253] In one possible implementation, when the device 600 is used to execute the method performed by the first communication device in the preceding embodiments, the logic circuit 601 is used to determine first information; the input / output interface 602 is used to send the first information, which indicates that the first communication device supports communication through N types of communication parameters, where N is a positive integer; wherein the N types of communication parameters are determined based on AI; the input / output interface 602 is also used to receive second information, which indicates K types of communication parameters, where K is a positive integer less than or equal to N; wherein the K types of communication parameters are included in the N types of communication parameters.

[0254] In one possible implementation, when the device 600 is used to execute the method performed by the second communication device in the preceding embodiments, the input / output interface 602 is used to receive first information, which indicates that the first communication device supports communication through N types of communication parameters, where N is a positive integer; wherein, the N types of communication parameters are determined based on AI; the logic circuit 601 is used to determine second information; the input / output interface 602 is also used to send second information, which indicates K types of communication parameters, where K is a positive integer less than or equal to N; wherein, the K types of communication parameters are included in the N types of communication parameters.

[0255] The logic circuit 601 and the input / output interface 602 can also perform other steps performed by the first or second communication device in any embodiment and achieve corresponding beneficial effects, which will not be elaborated here.

[0256] In one possible implementation, the processing unit 501 shown in FIG5 can be the logic circuit 601 in FIG6.

[0257] Optionally, the logic circuit 601 can be a processing device, the functions of which can be partially or entirely implemented in software.

[0258] Optionally, the processing apparatus may include a memory and a processor, wherein the memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform the corresponding processing and / or steps in any of the method embodiments.

[0259] Optionally, the processing device may consist of only a processor. A memory for storing computer programs is located outside the processing device, and the processor is connected to the memory via circuitry / wires to read and execute the computer programs stored in the memory. The memory and processor may be integrated together or physically independent of each other.

[0260] Optionally, the processing device may be one or more chips, or one or more integrated circuits. For example, the processing device may be one or more field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), system-on-chips (SoCs), central processing units (CPUs), network processors (NPs), digital signal processors (DSPs), microcontroller units (MCUs), programmable logic devices (PLDs), or other integrated chips, or any combination of the above chips or processors.

[0261] Please refer to Figure 7, which shows the communication device 700 involved in the above embodiments provided in the embodiments of this application. Specifically, the communication device 700 can be the communication device as a terminal device in the above embodiments. The example shown in Figure 7 is that the terminal device is implemented through the terminal device (or the components in the terminal device).

[0262] The present invention is a possible logical structure diagram of the communication device 700, which may include, but is not limited to, at least one processor 701 and a communication port 702.

[0263] In Figure 5, the transceiver unit 502 can be a communication interface, which can be the communication port 702 in Figure 7. The communication port 702 can include an input interface and an output interface. Alternatively, the communication port 702 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.

[0264] Further optionally, the device may also include at least one of a memory 703 and a bus 704. In the embodiments of this application, the at least one processor 701 is used to control the operation of the communication device 700.

[0265] Furthermore, the processor 701 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, etc. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0266] Optionally, the communication device 700 shown in FIG7 can be used to implement the steps implemented by the terminal device in the aforementioned method embodiments and to achieve the corresponding technical effects of the terminal device. The specific implementation of the communication device shown in FIG7 can be referred to the description in the aforementioned method embodiments, and will not be repeated here.

[0267] Please refer to Figure 8, which is a structural schematic diagram of the communication device 800 involved in the above embodiments provided in the embodiments of this application. Specifically, the communication device 800 can be a communication device as a network device in the above embodiments. The example shown in Figure 8 is that the network device is implemented through a network device (or a component in the network device). The structure of the communication device can be referred to the structure shown in Figure 8.

[0268] The communication device 800 includes at least one processor 811 and at least one network interface 814. Further optionally, the communication device also includes at least one memory 812, at least one transceiver 813, and one or more antennas 815. The processor 811, memory 812, transceiver 813, and network interface 814 are connected, for example, via a bus. In this embodiment, the connection may include various interfaces, transmission lines, or buses, etc., and this embodiment is not limited thereto. The antenna 815 is connected to the transceiver 813. The network interface 814 enables the communication device to communicate with other communication devices through a communication link. For example, the network interface 814 may include a network interface between the communication device and core network equipment, such as an S1 interface, or a network interface between the communication device and other communication devices (e.g., other network devices or core network equipment), such as an X2 or Xn interface.

[0269] In Figure 5, the transceiver unit 502 can be a communication interface, which can be the network interface 814 in Figure 8. The network interface 814 can include an input interface and an output interface. Alternatively, the network interface 814 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.

[0270] The processor 811 is primarily used to process communication protocols and communication data, control the entire communication device, execute software programs, and process data from these programs, for example, to support the actions described in the embodiments of the communication device. The communication device may include a baseband processor and a central processing unit (CPU). The baseband processor is primarily used to process communication protocols and communication data, while the CPU is primarily used to control the entire terminal device, execute software programs, and process data from these programs. The processor 811 in Figure 8 can integrate the functions of both a baseband processor and a CPU. Those skilled in the art will understand that the baseband processor and CPU can also be independent processors interconnected via technologies such as buses. Those skilled in the art will understand that a terminal device may include multiple baseband processors to adapt to different network standards, and multiple CPUs to enhance its processing capabilities. The various components of the terminal device can be connected via various buses. The baseband processor can also be described as a baseband processing circuit or a baseband processing chip. The CPU can also be described as a central processing circuit or a central processing chip. The function of processing communication protocols and communication data can be built into the processor or stored in memory as a software program, which is then executed by the processor to implement the baseband processing function.

[0271] The memory is primarily used to store software programs and data. The memory 812 can exist independently or be connected to the processor 811. Optionally, the memory 812 can be integrated with the processor 811, for example, integrated within a single chip. The memory 812 can store program code that executes the technical solutions of the embodiments of this application, and its execution is controlled by the processor 811. The various types of computer program code being executed can also be considered as drivers for the processor 811.

[0272] Figure 8 shows only one memory and one processor. In actual terminal devices, there may be multiple processors and multiple memories. Memory can also be called storage medium or storage device, etc. Memory can be a storage element on the same chip as the processor, i.e., an on-chip storage element, or it can be a separate storage element; this application does not limit this.

[0273] Transceiver 813 can be used to support the reception or transmission of radio frequency (RF) signals between a communication device and a terminal. Transceiver 813 can be connected to antenna 815. Transceiver 813 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 815 can receive RF signals. The receiver Rx of transceiver 813 receives the RF signals from the antennas, converts the RF signals into digital baseband signals or digital intermediate frequency (IF) signals, and provides the digital baseband signals or IF signals to processor 811 so that processor 811 can perform further processing on the digital baseband signals or IF signals, such as demodulation and decoding. Furthermore, the transmitter Tx in transceiver 813 is also used to receive modulated digital baseband signals or IF signals from processor 811, convert the modulated digital baseband signals or IF signals into RF signals, and transmit the RF signals through one or more antennas 815. Specifically, the receiver Rx can selectively perform one or more stages of downmixing and analog-to-digital conversion on the radio frequency signal to obtain a digital baseband signal or a digital intermediate frequency (IF) signal. The order of these downmixing and IF conversion processes is adjustable. The transmitter Tx can selectively perform one or more stages of upmixing and digital-to-analog conversion on the modulated digital baseband signal or digital IF signal to obtain a radio frequency signal. The order of these upmixing and IF conversion processes is also adjustable. The digital baseband signal and the digital IF signal can be collectively referred to as digital signals.

[0274] The transceiver 813 can also be called a transceiver unit, transceiver, transceiver device, etc. Optionally, the device in the transceiver unit that performs the receiving function can be regarded as the receiving unit, and the device in the transceiver unit that performs the transmitting function can be regarded as the transmitting unit. That is, the transceiver unit includes a receiving unit and a transmitting unit. The receiving unit can also be called a receiver, input port, receiving circuit, etc., and the transmitting unit can be called a transmitter, transmitter, or transmitting circuit, etc.

[0275] Optionally, the communication device 800 shown in FIG8 can be used to implement the steps implemented by the network device in the aforementioned method embodiments and achieve the corresponding technical effects of the network device. The specific implementation of the communication device 800 shown in FIG8 can be referred to the description in the aforementioned method embodiments, and will not be repeated here.

[0276] Please refer to Figure 9, which is a schematic diagram of the structure of the communication device involved in the above embodiments provided in the embodiments of this application.

[0277] Optionally, the communication device 900 includes, for example, modules, units, elements, circuits, or interfaces, etc., appropriately configured together to execute the technical solutions provided in this application. The communication device 900 may be the terminal device or network device described above, or a component (e.g., a chip) within these devices, used to implement the methods described in the following method embodiments. The communication device 900 includes one or more processors 901. The processor 901 may be a general-purpose processor or a dedicated processor, etc. For example, it may be a baseband processor or a central processing unit. The baseband processor can be used to process communication protocols and communication data, and the central processing unit can be used to control the communication device (e.g., RAN node, terminal, or chip, etc.), execute software programs, and process data from the software programs.

[0278] Alternatively, in one design, processor 901 may include program 903 (sometimes also referred to as code or instructions), which can be executed on processor 901 to cause communication device 900 to perform the methods described in the embodiments below. In yet another possible design, communication device 900 includes circuitry (not shown in FIG9).

[0279] Optionally, the communication device 900 may include one or more memories 902 storing a program 904 (sometimes referred to as code or instructions), which can be run on the processor 901 to cause the communication device 900 to perform the methods described in the above method embodiments.

[0280] Optionally, the processor 901 and / or memory 902 may include AI modules 907 and 908, which are used to implement AI-related functions. The AI ​​modules can be implemented through software, hardware, or a combination of both. For example, the AI ​​module may include a radio intelligence control (RIC) module. For instance, the AI ​​module may be a near real-time RIC or a non-real-time RIC.

[0281] Optionally, the processor 901 and / or memory 902 may also store data. The processor and memory may be configured separately or integrated together.

[0282] Optionally, the communication device 900 may further include a transceiver 905 and / or an antenna 906. The processor 901, sometimes referred to as a processing unit, controls the communication device (e.g., a RAN node or terminal). The transceiver 905, sometimes referred to as a transceiver unit, transceiver, transceiver circuit, or transceiver, is used to implement the transmission and reception functions of the communication device via the antenna 906.

[0283] In Figure 5, the processing unit 501 can be a processor 901. The transceiver unit 502 shown in Figure 5 can be a communication interface, which can be the transceiver 905 in Figure 9. The transceiver 905 can include an input interface and an output interface. Alternatively, the transceiver 905 can also be a transceiver circuit, which can include an input interface circuit and an output interface circuit.

[0284] This application also provides a computer-readable storage medium for storing one or more computer-executable instructions. When the computer-executable instructions are executed by a computer, the computer performs the method described in the possible implementations of the first or second communication device in the foregoing embodiments.

[0285] This application also provides a computer program product (or computer program) that, when executed by a computer, allows the computer to execute the method described above for the possible implementation of the first or second communication device.

[0286] This application also provides a chip system including at least one processor for supporting a communication device in implementing the functions involved in the possible implementations of the communication device described above. Optionally, the chip system further includes an interface circuit that provides program instructions and / or data to the at least one processor. In one possible design, the chip system may further include a memory for storing the program instructions and data necessary for the communication device. The chip system may be composed of chips or may include chips and other discrete devices, wherein the communication device may specifically be the first communication device or the second communication device in the aforementioned method embodiments.

[0287] This application also provides a communication system, which includes the first communication device in any of the above embodiments.

[0288] Optionally, the communication system may also include a second communication device.

[0289] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms. Whether a function is implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0290] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0291] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A communication method characterized by comprising: The method comprises: sending first information, the first information being used for indicating that the first communication device supports communication through N-type communication parameters, N being a positive integer; wherein the N-type communication parameters are determined based on an AI manner; receiving second information, the second information being used for indicating K-type communication parameters, K being a positive integer less than or equal to N; wherein the K-type communication parameters are contained in the N-type communication parameters.

2. The method of claim 1, wherein, The second information comprises K pieces of information, the i-th piece of information in the K pieces of information comprising first indication information and second indication information, i taking values from 1 to K; wherein the first indication information is used for indicating the i-th type of communication parameter in the K-type communication parameters, and the second indication information is used for indicating that the i-th type of communication parameter is determined through an AI manner.

3. The method of claim 2, wherein, The second information further comprises M pieces of information, the j-th piece of information in the M pieces of information comprising third indication information and fourth indication information, j taking values from 1 to M, M being a positive integer; wherein the third indication information is used for indicating the j-th type of communication parameter in M-type communication parameters, and the second indication information is used for indicating that the j-th type of communication parameter is determined through a non-AI manner.

4. The method of claim 1, wherein, The method further comprises: receiving third information, the third information being used for configuring the K-type communication parameters; wherein the second information is used for indicating that the K-type communication parameters are activated.

5. The method according to any one of claims 1 to 4, characterized in that, The first information comprises fifth indication information, the fifth indication information being used for indicating that the first communication device has the capability of communicating through communication parameters determined based on an AI manner.

6. The method of claim 5, wherein, The first information further comprises sixth indication information, the sixth indication information being used for indicating the N-type communication parameters.

7. The method according to any one of claims 1 to 6, characterized in that, The method further comprises: receiving or sending fourth information, the fourth information being used for indicating the values of the K-type communication parameters.

8. The method according to any one of claims 1 to 7, characterized in that, The method further comprises: receiving or sending fifth information, the fifth information being used for determining an AI model, the AI model being used for determining the values of the K-type communication parameters.

9. The method of claim 8, wherein, The fifth information comprises at least one of the following: scene information corresponding to the K-type communication parameters, model information of an AI model used for determining the parameter values of the K-type communication parameters, system information corresponding to the K-type communication parameters, resource information used for transmitting the parameter values of the K-type communication parameters, or parameter type information of the K-type communication parameters.

10. The method according to any one of claims 1 to 9, characterized in that, The method further comprises: receiving sixth information, the sixth information being used for indicating that P-type communication parameters are determined through a non-AI manner, the P-type communication parameters being contained in the K-type communication parameters, P being a positive integer less than or equal to K.

11. The method of claim 10, wherein, The sixth information is determined based on performance information of communication through K-type communication parameters determined through an AI manner, and the method further comprises: sending seventh information, the seventh information being used for determining the performance information.

12. A communication method characterized by comprising: The method comprises: receiving first information, the first information being used for indicating that the first communication device supports communication through N-type communication parameters, N being a positive integer; wherein the N-type communication parameters are determined based on an AI manner; sending second information, the second information being used for indicating K-type communication parameters, K being a positive integer less than or equal to N; wherein the K-type communication parameters are contained in the N-type communication parameters.

13. The method of claim 12, wherein, The second information includes K pieces of information, and the i-th piece of information among the K pieces of information includes first indication information and second indication information, where i takes the value from 1 to K; Wherein, the first indication information is used to indicate the i-th type of communication parameter in the K-type communication parameters, and the second indication information is used to indicate that the i-th type of communication parameter is determined by AI.

14. The method of claim 13, wherein, The second information also includes M pieces of information, wherein the j-th piece of information includes a third indication and a fourth indication, where j takes a value from 1 to M, and M is a positive integer; The third indication information is used to indicate the j-th type of communication parameter in the M-type communication parameters, and the second indication information is used to indicate that the j-th type of communication parameter is determined by a non-AI method.

15. The method of claim 12, wherein, The method further includes: Send a third message, the third message being used to configure the K-type communication parameters; wherein, the second message is used to indicate the activation of the K-type communication parameters.

16. The method according to any one of claims 12 to 15, characterized in that, The first information includes a fifth indication information, which indicates that the first communication device has the ability to communicate using communication parameters determined by AI.

17. The method of claim 16, wherein, The first information also includes a sixth indication information, which is used to indicate the N types of communication parameters.

18. The method according to any one of claims 12 to 17, characterized in that, The method further includes: Receive or send a fourth message, which is used to indicate the value of the K-type communication parameter.

19. The method according to any one of claims 12 to 18, characterized in that, The method further includes: Receive or send fifth information, the fifth information being used to determine the AI ​​model, and the AI ​​model being used to determine the values ​​of the K types of communication parameters.

20. The method of claim 19, wherein, The fifth piece of information includes at least one of the following: The scenario information corresponding to the K-type communication parameters, the model information of the AI ​​model used to determine the parameter values ​​of the K-type communication parameters, the system information corresponding to the K-type communication parameters, the resource information used to transmit the parameter values ​​of the K-type communication parameters, or the parameter type information of the K-type communication parameters.

21. The method according to any one of claims 12 to 20, characterized in that, The method further includes: Send a sixth message, which is used to indicate that the P-type communication parameters are determined by a non-AI method. The P-type communication parameters are included in the K-type communication parameters, where P is a positive integer less than or equal to K.

22. The method of claim 21, wherein, The sixth piece of information is determined based on communication performance information derived from K types of communication parameters determined through AI. The method further includes: Receive the seventh information, which is used to determine the performance information.

23. A communications device, characterized by Includes a module for performing the method as described in any one of claims 1 to 22.

24. A communications device, characterized by It includes at least one processor, said at least one processor being used to perform the method as described in any one of claims 1 to 22.

25. The communication apparatus according to claim 24, wherein, The communication device is a chip or chip system.

26. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program or instructions that, when executed, implement the method as described in any one of claims 1 to 22.

27. A computer program product, characterised in that, It includes a computer program or instructions that, when executed by a computer, implement the method as described in any one of claims 1 to 22.

Images