Communication method and related device

Abstract

A communication method and a related device. In the method, a first communication apparatus processes a first communication parameter on the basis of a second neural network model, so as to obtain a second communication parameter, and the first communication apparatus can then process a signal on the basis of the second communication parameter, wherein, the first communication parameter is obtained on the basis of a first neural network model. In other words, the first communication apparatus can process a communication signal on the basis of a communication parameter obtained on the basis of neural network processing. In this way, while computing power of a communication apparatus can be used for neural network processing, a communication parameter can be quickly determined by means of a neutral network model, so as to reduce a communication delay. In some implementations, the second neural network model can further process the communication parameter and can perform optimization processing on the communication parameter, so as to improve the communication performance of communication performed on the basis of the second communication parameter.

Description

A communication method and related equipment

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office on February 23, 2024, with application number 202410205343.5 and application name “A Communication Method and Related Equipment”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of wireless technology, and in particular to a communication method and related equipment. Background Art

[0003] Wireless communication can be the transmission communication between two or more communication nodes without propagating through conductors or cables. The communication nodes generally include network devices and terminal devices.

[0004] Currently, in wireless communication systems, communication nodes generally possess both signal transceiver capabilities and computing capabilities. For example, network devices with computing capabilities primarily provide computing power to support signal transceiver capabilities (e.g., processing both sending and receiving signals), enabling communication between the network device and other communication nodes.

[0005] However, in communication networks, communication nodes may have excess computing power beyond just supporting the aforementioned communication tasks. Therefore, how to utilize this computing power is a pressing technical issue.

[0006] Summary of the Invention

[0007] The present application provides a communication method and related equipment, which are used to enable the computing power of a communication device to be applied to the processing of a neural network, while also enabling the rapid determination of communication parameters through a neural network model to reduce communication delays.

[0008] In a first aspect, the present application provides a communication method, which is applied to a first communication device. The first communication device can be a communication device (such as a terminal device or a network device), or the first communication device can be a component of the communication device (such as a processor, a chip, or a chip system, etc.), or the first communication device can also be a logic module or software that can implement all or part of the functions of the communication device. In this method, the first communication device processes the collected first information based on a first neural network model to obtain a first communication parameter; the first communication device processes the first communication parameter based on a second neural network model to obtain a second communication parameter; and the first communication device processes a signal based on the second communication parameter.

[0009] Based on the above technical solution, the first communication device processes the first communication parameter based on the second neural network model. After obtaining the second communication parameter, the first communication device can process the signal based on the second communication parameter. The first communication parameter is obtained based on the first neural network model. In other words, the first communication device can process the communication signal based on the communication parameter obtained by the neural network processing. In this way, the computing power of the communication device can be applied to the processing of the neural network, and the communication parameters can also be quickly determined through the neural network model to reduce communication latency.

[0010] Furthermore, in the above technical solution, the second neural network model can process the first communication parameters output by the first neural network to obtain second communication parameters for signal processing. Compared to the method of determining communication parameters through a single neural network model, in the above technical solution, the first neural network model can be a model with higher generalization, that is, the first communication parameters output by the first neural network model may be applicable to a variety of communication tasks, and the second neural network model can further process the communication parameters and optimize the communication parameters to improve the communication performance based on the second communication parameters.

[0011] In this application, the neural network model can be replaced by other terms, such as model, artificial intelligence (AI) model, AI network, AI neural network model, neural network, machine learning model or AI processing model.

[0012] It should be understood that the process in which a neural network model (e.g., the first neural network model, or the third neural network model described later) processes the collected information to obtain communication parameters is referred to as the first processing, and the first processing can be understood as one or more of prediction, inference, estimation, evaluation, decision-making, and pre-estimation. Optionally, such a neural network model can be a large wireless model with a large model parameter scale, or a pre-trained large wireless model.

[0013] It should be understood that when a neural network model (e.g., the second neural network model, or the fourth neural network model described below) processes one communication parameter to obtain another communication parameter, this processing is referred to as second processing, and the second processing can be understood as one or more of correction, modification, adjustment, optimization, updating, calibration, and fine-tuning. Optionally, this neural network model can be a correction model, a fine-tuning model, an additional layer model, etc., with a smaller scale of model parameters.

[0014] It should be noted that, in the process of the first communication device processing the signal based on the communication parameter, the first communication device may process the received signal based on the communication parameter, and / or the first communication device may process the transmitted signal based on the communication parameter.

[0015] In a possible implementation of the first aspect, before the first communication device processes the collected first information based on the first neural network model to obtain the first communication parameter, the method also includes: the first communication device communicates based on a third communication parameter, and the third communication parameter is determined based on the communication signal received and / or sent by the first communication device; wherein the third communication parameter and the fourth communication parameter are used to process the third neural network model to obtain the first neural network model; and the fourth communication parameter is a communication parameter obtained by the third neural network model processing the collected second information.

[0016] Based on the above technical solution, before the first communication device determines the first communication parameter based on the first neural network model, the first communication device can also process the third neural network model based on the third communication parameter during the communication based on the third communication parameter to obtain the first neural network model. Since the third communication parameter is determined based on the communication signal received and / or sent by the first communication device, the third communication parameter can reflect the actual communication environment. In this way, while improving the performance of the first neural network model obtained based on the third communication parameter, the first communication device can also reuse the communication signal to implement model processing, without the need to collect specific samples during the model processing process, which can reduce overhead.

[0017] It should be understood that in the process of processing one neural network model to obtain another neural network model (for example, processing the third neural network model to obtain the first neural network model, or processing the fourth neural network model described later to obtain the second neural network model, etc.), the processing is recorded as the third processing, and the third processing can be understood as one or more of: training, iteration, optimization, fine-tuning, updating, tuning, and improvement.

[0018] Optionally, the third communication parameter and the fourth communication parameter are used to process the third neural network model to obtain the first neural network model, which may include: the third communication parameter and the fourth communication parameter can be used as inputs to the third neural network model, and after the above-mentioned third processing, the first neural network model is obtained; or, the third communication parameter and the performance of the communication signal processed based on the fourth communication parameter can be used as inputs to the third neural network model, and after the above-mentioned third processing, the first neural network model is obtained.

[0019] In a possible implementation of the first aspect, before the first communication device processes the first communication parameter based on the second neural network model to obtain the second communication parameter, the method also includes: the first communication device communicates based on a fifth communication parameter, and the fifth communication parameter is determined based on the communication signal received and / or sent by the first communication device; wherein the fifth communication parameter and the first communication parameter are used to process a fourth neural network model to obtain the second neural network model.

[0020] Optionally, the fifth communication parameter and the first communication parameter are used to process the fourth neural network model to obtain the second neural network model, which may include: the fifth communication parameter and the first communication parameter can be used as inputs of the fourth neural network model, and after the above-mentioned third processing, the second neural network model is obtained; or, the fifth communication parameter and the performance of the communication signal processed based on the first communication parameter can be used as inputs of the fourth neural network model, and after the above-mentioned third processing, the second neural network model is obtained.

[0021] Based on the above technical solution, before the first communication device determines the second communication parameter based on the second neural network model, the first communication device can also update the fourth neural network model based on the fifth communication parameter and the first communication parameter during communication based on the fifth communication parameter to obtain the second neural network model. Since the fifth communication parameter is determined based on the communication signal received and / or sent by the first communication device, the fifth communication parameter can reflect the actual communication environment. In this way, while improving the performance of the second neural network model obtained based on the fifth communication parameter, the first communication device can also reuse the communication signal to achieve model update, without the need to collect specific samples during the model update process, which can reduce overhead.

[0022] Optionally, the third communication parameter and the fifth communication parameter may be the same or partially the same. In this way, the same communication parameters can be reused to process different neural network models, further reducing overhead.

[0023] Optionally, the third communication parameter and the fifth communication parameter are different from each other.

[0024] In a possible implementation of the first aspect, the method also includes: the first communication device obtains third information, and the third information is used to indicate the communication performance corresponding to the first communication parameter; the first communication device processes the first communication parameter based on the second neural network model to obtain the second communication parameter, including: the first communication device processes the third information and the first communication parameter based on the second neural network model to obtain the second communication parameter.

[0025] Based on the above technical solution, the first communication device can also obtain the communication performance of the first communication parameter obtained based on the first neural network model, and the first communication device can process the communication performance and the first communication parameter based on the second neural network model. In this way, because the basis for determining the second communication parameter by the second neural network model also includes the communication performance, the second neural network model can determine the communication parameter based on the feedback of the communication performance, thereby supporting real-time correction of the communication parameter based on the feedback of the communication performance, thereby improving the flexibility of the correction.

[0026] Optionally, the second neural network model includes a first sub-model and a second sub-model; wherein, the first sub-model is used to pre-process the third information to obtain a processing result, and the second sub-model is used to fuse the processing result and the first communication parameter to obtain the second communication parameter.

[0027] Optionally, the preprocessing may include one or more of aligning dimensions, implicitly extracting information related to wireless parameters, denoising, normalization, and standardization.

[0028] In a possible implementation of the first aspect, the first communication device processes the third information and the first communication parameter based on the second neural network model to obtain the second communication parameter, including: when at least one of the following items is satisfied, the first communication device processes the third information and the first communication parameter based on the second neural network model to obtain the second communication parameter, including:

[0029] The communication performance corresponding to the first communication parameter is lower than a threshold;

[0030] It is determined that a duration of communication based on the first communication parameter is greater than or equal to a preset duration.

[0031] Based on the above technical solution, when at least one of the above conditions is met, the first communication device can determine that the communication parameters output by the first neural network model need to be processed by the second neural network model. To this end, the first communication device can trigger the processing of the third information and the first communication parameters based on the second neural network model to obtain the second communication parameters. Through the above method, an event-based triggering method and a fixed-cycle-based triggering method are provided to enhance the flexibility of the solution implementation.

[0032] In a possible implementation of the first aspect, the method further includes: when at least one of the following is satisfied, the first communication device processes the signal based on the first communication parameter, including:

[0033] The performance corresponding to the first communication parameter is higher than or equal to a threshold;

[0034] It is determined that a duration of communication based on the first communication parameter is less than a preset duration.

[0035] Based on the above technical solution, when at least one of the above conditions is met, the first communication device can determine that the communication parameters output by the first neural network model do not need to be processed by the second neural network model. To this end, the first communication device can trigger the first communication device to process the signal based on the first communication parameters without the participation of the second neural network model. Through the above method, an event-based triggering method and a fixed-cycle-based triggering method are provided to enhance the flexibility of the solution implementation.

[0036] In a possible implementation of the first aspect, after the first communication device processes the signal based on the second communication parameter, the method further includes: the first communication device obtains fourth information, and the fourth information is used to indicate the communication performance corresponding to the second communication parameter; the first communication device performs model update processing on the first neural network model and / or the second neural network model based on the fourth information.

[0037] Optionally, the model update process can be replaced by other processing of the model, such as one or more of training, iteration, optimization, fine-tuning, updating, tuning, and improvement.

[0038] Based on the above technical solution, the first communication device can also obtain the communication performance of communication based on the second communication parameter, and the first communication device can perform model update processing on the first neural network model and / or the second neural network model based on the communication performance. In this way, since the communication performance indicated by the fourth information can be used to indicate the performance of the first neural network model and / or the second neural network model, feedback based on the communication performance can be used to correct the neural network model, thereby improving the flexibility of the correction.

[0039] In a possible implementation of the first aspect, the first communication device performs model update processing on the first neural network model and / or the second neural network model based on the fourth information, including: when at least one of the following items is satisfied, the first communication device performs model update processing on the first neural network model and / or the second neural network model based on the fourth information, including:

[0040] The number of times that the performance corresponding to the second communication parameter is lower than the threshold reaches a threshold;

[0041] It is determined that a duration of communication based on the second communication parameter is greater than or equal to a preset duration.

[0042] Based on the above technical solution, when at least one of the above items is met, the first communication device can determine that the second neural network model and / or the first neural network model need to be updated. To this end, the first communication device can trigger the first neural network model and / or the second neural network model to be updated based on the fourth information. Through the above method, an event-based triggering method and a fixed-cycle-based triggering method are provided to enhance the flexibility of the solution implementation.

[0043] In a possible implementation manner of the first aspect, the collected first information and / or the collected second information includes at least one of the following: environmental information, user information, or task information.

[0044] Based on the above technical solution, the information collected by the first communication device may include at least one of the above items to improve the flexibility of the solution implementation.

[0045] The second aspect of the present application provides a communication method, which is applied to a first communication device, which may be a communication device (such as a terminal device or a network device), or the first communication device may be a partial component in the communication device (such as a processor, a chip or a chip system, etc.), or the first communication device may also be a logic module or software that can realize all or part of the functions of the communication device. In this method, the first communication device communicates based on a third communication parameter, and the third communication parameter is determined based on the communication signal received and / or sent by the first communication device; wherein the third communication parameter and the fourth communication parameter are used to process the third neural network model to obtain the first neural network model; the fourth communication parameter is the communication parameter obtained by the third neural network model processing the collected second information; the first communication device processes the collected first information based on the first neural network model to obtain the first communication parameter; the first communication device processes the signal based on the first communication parameter.

[0046] Based on the above technical solution, when the first communication device communicates based on the third communication parameter, the first communication device can process the third neural network model based on the third communication parameter to obtain the first neural network model. Since the third communication parameter is determined based on the communication signal received and / or sent by the first communication device, the third communication parameter can reflect the actual communication environment. In this way, while improving the performance of the first neural network model obtained based on the third communication parameter, the first communication device can also reuse the communication signal to realize model processing, without collecting specific samples in the model processing process, which can reduce overhead.

[0047] In a possible implementation of the second aspect, after the first communication device processes the signal based on the first communication parameter, the method further includes: the first communication device obtains fifth information, and the fifth information is used to indicate the performance corresponding to the first communication parameter; the first communication device performs model update processing on the first neural network model based on the fifth information.

[0048] Based on the above technical solution, the first communication device can also obtain the communication performance of communication based on the first communication parameter, and the first communication device can perform model update processing on the first neural network model based on the communication performance. In this way, since the communication performance indicated by the fifth information can be used to indicate the performance of the first neural network model, feedback based on the communication performance can achieve real-time correction of the neural network model, thereby improving the flexibility of correction.

[0049] In a possible implementation of the second aspect, the first communication device performs model update processing on the first neural network model based on the fifth information, including: when at least one of the following items is satisfied, the first communication device performs model update processing on the first neural network model based on the fifth information, including:

[0050] The performance corresponding to the first communication parameter is lower than a threshold;

[0051] It is determined that a duration of communication based on the first communication parameter is greater than or equal to a preset duration.

[0052] Based on the above technical solution, when at least one of the above conditions is met, the first communication device may determine that a model update process needs to be performed on the first neural network model. To this end, the first communication device may trigger a model update process on the first neural network model based on the fifth information. Through the above method, an event-based triggering method and a fixed-cycle-based triggering method are provided to enhance the flexibility of the solution implementation.

[0053] The third aspect of the present application provides a communication device, which is a first communication device, and includes a transceiver unit and a processing unit; the processing unit is used to process the collected first information based on a first neural network model to obtain a first communication parameter; the processing unit is also used to process the first communication parameter based on a second neural network model to obtain a second communication parameter; the transceiver unit is used to process the signal based on the second communication parameter.

[0054] In the third aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the first aspect and achieve corresponding technical effects. For details, please refer to the first aspect and will not be repeated here.

[0055] The fourth aspect of the present application provides a communication device, which is a first communication device, and includes a transceiver unit and a processing unit; the processing unit is used to communicate based on a third communication parameter, and the third communication parameter is determined based on the communication signal received and / or sent by the first communication device; wherein the third communication parameter and the fourth communication parameter are used to process a third neural network model to obtain a first neural network model; the fourth communication parameter is a communication parameter obtained by the third neural network model processing the collected second information; the processing unit is also used to process the collected first information based on the first neural network model to obtain the first communication parameter; the transceiver unit is used to process the signal based on the first communication parameter.

[0056] In the fourth aspect of the present application, the constituent modules of the communication device can also be used to execute the steps performed in each possible implementation method of the second aspect and achieve corresponding technical effects. For details, please refer to the second aspect and will not be repeated here.

[0057] In a fifth aspect, the present application provides a communication device, comprising at least one processor coupled to a memory; the memory is used to store programs or instructions; the at least one processor is used to execute the program or instructions so that the device implements the method described in any possible implementation method of the first or second aspect.

[0058] In a sixth aspect, the present application provides a communication device comprising at least one logic circuit and an input / output interface; the logic circuit is used to execute the method described in any possible implementation of the first or second aspect.

[0059] A seventh aspect of the present application provides a communication system, which includes the above-mentioned first communication device.

[0060] Optionally, the communication system further includes other communication devices communicating with the first communication device.

[0061] In an eighth aspect, the present application provides a computer-readable storage medium for storing one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in any possible implementation of the first or second aspect above.

[0062] In a ninth aspect, the present application provides a computer program product (or computer program). When the computer program in the computer program product is executed by the processor, the processor executes the method described in any possible implementation of the first or second aspect.

[0063] In a tenth aspect, the present application provides a chip or a chip system, which includes at least one processor for supporting a communication device to implement the method described in any possible implementation of the first or second aspect.

[0064] 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 a chip or may include a chip and other discrete components. Optionally, the chip system also includes an interface circuit that provides program instructions and / or data to the at least one processor.

[0065] Among them, the technical effects brought about by any design method in the third to tenth aspects can refer to the technical effects brought about by different design methods in the above-mentioned first or second aspects, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figures 1a to 1c are schematic diagrams of a communication system provided by this application;

[0067] Figures 2a to 2e are schematic diagrams of the AI ​​processing process involved in this application;

[0068] FIG3 is an interactive schematic diagram of the communication method provided by this application;

[0069] Figures 4a to 4f are schematic diagrams of the model processing provided by this application;

[0070] FIG5 is an interactive diagram of the communication method provided by this application;

[0071] FIG6 is a schematic diagram of the model processing provided by this application;

[0072] 7 to 11 are schematic diagrams of the communication device provided in this application. DETAILED DESCRIPTION

[0073] First, some of the terms used in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0074] (1) Terminal device: It 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.

[0075] 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 (also known as "cellular" phones, mobile phones), computers, and data cards. For example, they can be portable, pocket-sized, handheld, computer-built-in, or vehicle-mounted mobile devices that exchange voice and / or data with the radio access network. Examples include personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistants (PDAs), tablets (tablets or pads), and computers with wireless transceiver capabilities. Wireless terminal equipment can also be called system, subscriber unit, subscriber station, mobile station, mobile station (MS), remote station, access point (AP), remote terminal equipment (remote terminal), access terminal equipment (access terminal), user terminal equipment (user terminal), user agent, subscriber station (SS), customer premises equipment (CPE), terminal, user equipment (UE), mobile terminal (MT), etc.

[0076] As an example and not a limitation, in the embodiments of the present application, the terminal device may also be a wearable device. Wearable devices may also be referred to as wearable smart devices or smart wearable devices, etc., which are a general term for wearable devices that are intelligently designed and developed using wearable technology for daily wear, such as glasses, gloves, watches, clothing, and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only hardware devices, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable smart devices include those that are fully functional, large in size, and can achieve complete or partial functions without relying on smartphones, such as smart watches or smart glasses, etc., as well as those that only focus on a certain type of application function and need to be used in conjunction with other devices such as smartphones, such as various smart bracelets, smart helmets, and smart jewelry for vital sign monitoring.

[0077] The terminal may also be a drone, a robot, a terminal in device-to-device (D2D) communication, a terminal in vehicle-to-everything (V2X), a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self-driving, a wireless terminal in remote medical, a wireless terminal in smart grid, a wireless terminal in transportation safety, a wireless terminal in smart city, a wireless terminal in smart home, etc.

[0078] In addition, the terminal device may also be a terminal device in a communication system evolved after the fifth generation (5G) communication system (such as 5G Advanced or sixth generation (6G) communication system) or a terminal device in a future-evolved public land mobile network (PLMN). For example, 5G Advanced or 6G networks can further expand the form and functions of 5G communication terminals. 6G terminals include but are not limited to vehicles, cellular network terminals (with integrated satellite terminal functions), drones, and Internet of Things (IoT) devices.

[0079] In an embodiment of the present application, the terminal device may also obtain artificial intelligence (AI) services provided by the network device. Optionally, the terminal device may also have AI processing capabilities.

[0080] (2) Network equipment: It can be a device in a wireless network. For example, the network equipment can be a RAN node (or device) that connects a terminal device to a wireless network, which can also be called a base station. Currently, some examples of RAN equipment are: base station, evolved NodeB (eNodeB), gNB (gNodeB) in a 5G communication system, transmission reception point (TRP), evolved Node B (eNB), radio network controller (RNC), Node B (NB), home base station (e.g., home evolved Node B, or home Node B, HNB), base band unit (BBU), or wireless fidelity (Wi-Fi) access point (AP). In addition, in a network structure, the network equipment can include a centralized unit (CU) node, a distributed unit (DU) node, or a RAN device including a CU node and a DU node.

[0081] Alternatively, a RAN node can be a macro base station, micro base station, indoor base station, relay node, donor node, or a wireless controller in a cloud radio access network (CRAN) scenario. A RAN node can also be a server, wearable device, vehicle, or vehicle-mounted device. For example, the access network device in vehicle-to-everything (V2X) technology can be a roadside unit (RSU).

[0082] In another possible scenario, multiple RAN nodes collaborate to assist the terminal in achieving wireless access, and different RAN nodes respectively implement part of the functions of the base station. For example, the RAN node can be a CU, DU, CU-control plane (CP), CU-user plane (UP), or radio unit (RU). The CU and DU can be set separately, or they can be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or radio frequency unit, such as a remote radio unit (RRU), an active antenna unit (AAU), a radio head (RH), or a remote radio head (RRH).

[0083] In different systems, CU (or CU-CP and CU-UP), DU or RU may also have different names, but those skilled in the art can understand their meanings. For example, in an open access network (open RAN, O-RAN or ORAN) system, CU may also be called O-CU (open CU), DU may also be called O-DU, CU-CP may also be called O-CU-CP, CU-UP may also be called O-CU-UP, and RU may also be called O-RU. For the convenience of description, this application takes CU, CU-CP, CU-UP, DU and RU as examples for description. Any unit of CU (or CU-CP, CU-UP), DU and RU in this application can be implemented by a software module, a hardware module, or a combination of a software module and a hardware module.

[0084] The communication between the access network device and the terminal device follows a certain protocol layer structure. The 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: a radio resource control (RRC) layer, a packet data convergence protocol (PDCP) layer, a radio link control (RLC) layer, a media access control (MAC) layer, or a physical (PHY) layer. The user plane protocol layer may include at least one of the following: a service data adaptation protocol (SDAP) layer, a PDCP layer, an RLC layer, a MAC layer, or a physical layer.

[0085] For the correspondence between network elements in the ORAN system and their achievable protocol layer functions, please refer to Table 1 below.

[0086] Table 1

[0087] The network device may be any other device that provides wireless communication functionality to the terminal device. The embodiments of this application do not limit the specific technology and device form used by the network device. For ease of description, the embodiments of this application do not limit this.

[0088] The network equipment may also include core network equipment, which may include, for example, a mobility management entity (MME), a home subscriber server (HSS), a serving gateway (S-GW), a policy and charging rules function (PCRF), and a public data network gateway (PDN gateway, P-GW) in a fourth generation (4G) network; and network elements such as an access and mobility management function (AMF), a user plane function (UPF), or a session management function (SMF) in a 5G network. In addition, the core network equipment may also include other core network equipment in a 5G network and a next generation network of a 5G network.

[0089] In an embodiment of the present application, the above-mentioned network device may also have a network node with AI capabilities, which can provide AI services for terminals or other network devices. For example, it can be an AI node on the network side (access network or core network), a computing power node, a RAN node with AI capabilities, a core network element with AI capabilities, etc.

[0090] In the embodiments of the present application, the apparatus for implementing the function of the network device may be the network device, or may be a device capable of supporting the network device in implementing the function, such as a chip system, which may be installed in the network device. In the technical solutions provided in the embodiments of the present application, the technical solutions provided in the embodiments of the present application are described by taking the network device as an example.

[0091] (3) Configuration and pre-configuration: In this application, configuration and pre-configuration are used simultaneously. Configuration refers to the network device / server sending some parameter configuration information or parameter values ​​to the terminal through messages or signaling, so that the terminal can determine the communication parameters or resources during transmission based on these values ​​or information. Pre-configuration is similar to configuration, and can be parameter information or parameter values ​​pre-negotiated between the network device / server and the terminal device, or parameter information or parameter values ​​used by the base station / network device or terminal device as specified in the standard protocol, or parameter information or parameter values ​​pre-stored in the base station / server or terminal device. This application does not limit this.

[0092] Furthermore, these values ​​and parameters can be changed or updated.

[0093] (4) The terms "system" and "network" in the embodiments of the present application can be used interchangeably. "Multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of A, B and C" includes A, B, C, AB, AC, BC or ABC. In addition, unless otherwise specified, the ordinal numbers such as "first" and "second" mentioned in the embodiments of the present application are used to distinguish multiple objects, and are not used to limit the order, timing, priority or importance of multiple objects.

[0094] (5) “Sending” and “receiving” in the embodiments of the present application indicate the direction of signal transmission. For example, “sending information to XX” can be understood as the destination of the information being XX, which can include direct sending through the air interface, as well as indirect sending through the air interface by other units or modules. “Receiving information from YY” can be understood as the source of the information being YY, which can include direct receiving from YY through the air interface, as well as indirect receiving from YY through the air interface from other units or modules. “Sending” can also be understood as the “output” of the chip interface, and “receiving” can also be understood as the “input” of the chip interface.

[0095] In other words, sending and receiving can be performed between devices, for example, between a network device and a terminal device, or can be performed within a device, for example, sending or receiving between components, modules, chips, software modules or hardware modules within the device through a bus, wiring or interface.

[0096] It is understandable that information may be processed between the source and destination of information transmission, such as coding, modulation, etc., but the destination can understand the valid information from the source. Similar expressions in this application can be understood similarly and will not be repeated.

[0097] (6) In the embodiments of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information (such as the indication information described below) is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, directly indicating the information to be indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated may also be indirectly indicated by indicating other information, wherein the other information is associated with the information to be indicated; or only a part of the information to be indicated may be indicated, while the other part of the information to be indicated is known or agreed in advance. For example, the indication of specific information may be achieved by means of the arrangement order of each information agreed in advance (such as predefined by the protocol), thereby reducing the indication overhead to a certain extent. The present application does not limit the specific method of indication. It is understandable that for the sender of the indication information, the indication information can be used to indicate the information to be indicated, and for the receiver of the indication information, the indication information can be used to determine the information to be indicated.

[0098] In this application, unless otherwise specified, the same or similar parts between the various embodiments can refer to each other. In the various embodiments of this application, and the various methods / designs / implementations in each embodiment, if there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments and the various methods / designs / implementations in each embodiment are consistent and can be referenced to each other. The technical features in different embodiments and the various methods / designs / implementations in each embodiment can be combined to form new embodiments, methods, or implementations according to their inherent logical relationships. The following description of the implementation methods of this application does not constitute a limitation on the scope of protection of this application.

[0099] The present application can be applied to a long term evolution (LTE) system, a new radio (NR) system, or a communication system evolved after 5G (such as 6G, etc.). The communication system includes at least one network device and / or at least one terminal device.

[0100] Please refer to Figure 1a, which is a schematic diagram of a communication system in this application. Figure 1a exemplarily illustrates a network device and six terminal devices, namely terminal device 1, terminal device 2, terminal device 3, terminal device 4, terminal device 5, and terminal device 6. In the example shown in Figure 1a, terminal device 1 is a smart teacup, terminal device 2 is a smart air conditioner, terminal device 3 is a smart gas pump, terminal device 4 is a vehicle, terminal device 5 is a mobile phone, and terminal device 6 is a printer.

[0101] As shown in Figure 1a, the sending entity of AI configuration information can be a network device. The receiving entity of AI configuration information can be terminal devices 1-6. In this case, the network device and terminal devices 1-6 form a communication system. In this communication system, terminal devices 1-6 can send data to the network device, and the network device needs to receive data sent by terminal devices 1-6. At the same time, the network device can send configuration information to terminal devices 1-6.

[0102] For example, in Figure 1a, terminal devices 4 and 6 can also form a communication system. Terminal device 5 acts as a network device, i.e., the sending entity of AI configuration information; terminal devices 4 and 6 act as terminal devices, i.e., the receiving entities of AI configuration information. For example, in a connected vehicle system, terminal device 5 sends AI configuration information to terminal devices 4 and 6, respectively, and receives data from terminal devices 4 and 6. Correspondingly, terminal devices 4 and 6 receive AI configuration information from terminal device 5 and send data to terminal device 5.

[0103] Taking the communication system shown in Figure 1a as an example, in addition to executing communication-related services, different devices (including between network devices, between network devices and terminal devices, and / or between terminal devices) may also execute AI-related services.

[0104] As shown in Figure 1b, taking the 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.

[0105] As shown in Figure 1c, taking the terminal devices including a TV and a mobile phone as an example, communication-related services and AI-related services can also be performed between the TV and the mobile phone.

[0106] The technical solution provided in this application can be applied to a wireless communication system (e.g., the system shown in FIG. 1a , FIG. 1b , or FIG. 1c ). For example, an AI network element can be introduced into the communication system provided in this application to implement some or all AI-related operations. The AI ​​network element can also be referred to as an AI node, AI device, AI entity, AI module, AI model, or AI unit, etc. The AI ​​network element can be a network element built into the communication system. For example, the AI ​​network element can be an AI module built into: an access network device, a core network device, a cloud server, or an operation, administration, and maintenance (OAM) system to implement AI-related functions. The OAM can serve as a network manager for a core network device and / or as a network manager for an access network device. Alternatively, the AI ​​network element can also be an independently configured network element in the communication system. Optionally, the terminal or a chip built into the terminal can also include an AI entity to implement AI-related functions.

[0107] The following is a brief introduction to the AI ​​that may be involved in this application.

[0108] AI can imbue machines with human intelligence. For example, it can use computer hardware and software to simulate certain intelligent human behaviors. Machine learning methods can be used to achieve artificial intelligence. In machine learning, a machine uses training data to learn (or train) a model. This model represents the mapping from input to output. The learned model can be used for inference (or prediction), meaning that the model can be used to predict the output corresponding to a given input. This output can also be called an inference result (or prediction result).

[0109] Machine learning can include supervised learning, unsupervised learning, and reinforcement learning. Among them, unsupervised learning can also be called unsupervised learning.

[0110] Supervised learning uses machine learning algorithms to learn the mapping relationship between sample values ​​and sample labels based on collected sample values ​​and sample labels, and then expresses this learned mapping relationship using an AI model. The process of training a 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. The model parameters are optimized by calculating the error between the model's predicted values ​​and the sample labels (ideal values). Once the mapping relationship is learned, the learned mapping can be used to predict new sample labels. The mapping relationship learned by supervised learning can include linear mappings or nonlinear mappings. Based on the type of label, the learning task can be divided into classification tasks and regression tasks.

[0111] Unsupervised learning uses algorithms to discover inherent patterns in collected sample values. One type of unsupervised learning algorithm uses the samples themselves as supervisory signals, meaning the model learns the mapping from one sample to another. This is called self-supervised learning. During training, the model parameters are optimized by calculating the error between the model's predictions and the samples themselves. Self-supervised learning can be used in signal compression and decompression recovery applications. Common algorithms include autoencoders and generative adversarial networks.

[0112] 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 lack explicit label data for "correct" actions. Instead, the algorithm must interact with the environment to obtain reward signals from the environment, and then adjust its decision-making actions to maximize the reward signal value. For example, in downlink power control, the reinforcement learning model adjusts the downlink transmit power of each user based on the overall system throughput fed back by the wireless network, hoping to achieve higher system throughput. The goal of reinforcement learning is also to learn the mapping between environmental states and optimal (e.g., optimal) decision-making actions. However, because the labels for "correct actions" cannot be obtained in advance, network optimization cannot be achieved by calculating the error between actions and "correct actions." Reinforcement learning training is achieved through iterative interaction with the environment.

[0113] A neural network (NN) is a specific model in machine learning technology. According to the universal approximation theorem, NNs can theoretically approximate any continuous function, enabling them to learn arbitrary mappings. Traditional communication systems require extensive expert knowledge to design communication modules. However, deep learning communication systems based on neural networks can automatically discover implicit patterns in massive data sets and establish mapping relationships between data, achieving performance superior to traditional modeling methods.

[0114] The idea of ​​a neural network is derived from the neuronal structure of the brain. For example, each neuron performs a weighted sum operation on its input values ​​and outputs the result through an activation function.

[0115] As shown in Figure 2a, it is a schematic diagram of the neuron structure. Assume that the input of the neuron is x = [x0, x1, ..., x n ], and the weights corresponding to each input are w=[w,w1,…,w n ], where n is a positive integer, w i and x i It can be a decimal, an integer (such as 0, a positive integer or a negative integer, etc.), or a complex number. i As x i The weight of x iWeighted. The bias for weighted summation of input values ​​according to the weights is, for example, b. The activation function can take many forms. Assuming that the activation function of a neuron is: y = f(z) = max(0,z), then the output of the neuron is: For another example, if the activation function of a neuron is: y = f(z) = z, then the output of the neuron is: b can be a decimal, an integer (eg, 0, a positive integer, or a negative integer), or a complex number, etc. The activation functions of different neurons in a neural network can be the same or different.

[0116] Furthermore, neural networks generally include 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 comprises, and the number of neurons in each layer can be referred to as the width of that layer. In one implementation, a neural network includes an input layer and an output layer. The input layer processes the input information received by the neural network through neurons, passing the processing results to the output layer, which then obtains the output of the neural network. In another implementation, a neural network includes an input layer, a hidden layer, and an output layer. The input layer processes the input information received by the neural network through neurons, passing the processing results to an intermediate hidden layer. The hidden layer performs calculations on the received processing results to obtain a calculation result, which is then passed to the output layer or the next adjacent hidden layer, which ultimately obtains the output of the neural network. A neural network can include one hidden layer or multiple hidden layers connected in sequence, without limitation.

[0117] The neural network is, for example, a deep neural network (DNN). Depending on how the network is constructed, DNNs can include feedforward neural networks (FNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

[0118] Figure 2b is a schematic diagram of an FNN network. A characteristic of FNN networks is that neurons in adjacent layers are fully connected. This characteristic typically requires a large amount of storage space and results in high computational complexity.

[0119] CNN is a type of neural network specifically designed to process data with a grid-like structure. For example, time series data (e.g., discrete sampling on the time axis) and image data (e.g., discrete sampling on the two-dimensional axis) can both be considered grid-like data. CNNs do not utilize all input information at once for computation. Instead, they use a fixed-size window to intercept a portion of the information for convolution operations, significantly reducing the computational complexity of model parameters. Furthermore, depending on the type of information intercepted by the window (e.g., people and objects in an image represent different types of information), each window can use a different convolution kernel, enabling CNNs to better extract features from the input data.

[0120] RNNs are a type of DNN that utilizes feedback time series information. RNN inputs include the current input value and its own output value at the previous moment. RNNs are suitable for capturing temporally correlated sequence features and are particularly well-suited for applications such as speech recognition and channel coding.

[0121] During the machine learning model training process, a loss function can be defined. This function describes the gap or discrepancy between the model's output and the ideal target value. Loss functions can be expressed in various forms, and there are no restrictions on their specific form. The model training process can be viewed as adjusting some or all of the model's parameters to keep the loss function below a threshold or meet the target.

[0122] A model may also be referred to as an AI model, rule, or other name. An AI model can be considered a specific method for implementing an AI function. An AI model represents a mapping relationship or function between the input and output of a model. AI functions may include one or more of the following: data collection, model training (or model learning), model information release, model inference (or model reasoning, inference, or prediction, etc.), model monitoring or model verification, or inference result release, etc. AI functions may also be referred to as AI (related) operations, or AI-related functions.

[0123] The following is an illustrative description of the implementation process of a fully connected neural network, also known as a multilayer perceptron (MLP), with reference to the accompanying drawings.

[0124] As shown in Figure 2c, an MLP consists of an input layer (left), an output layer (right), and multiple hidden layers (center). Each layer of the MLP contains several nodes, called neurons. Neurons in adjacent layers are connected to each other.

[0125] Optionally, considering neurons in two adjacent layers, the output h of a neuron in the next layer is the weighted sum of all neurons x connected to it in the previous layer and passes through an activation function, which can be expressed as: h=f(wx+b).

[0126] Among them, w is the weight matrix, b is the bias vector, and f is the activation function.

[0127] Alternatively, the output of the neural network can be recursively expressed as: y = f n (w n f n-1 (…)+b n ).

[0128] Where n is the index of the neural network layer, n is greater than or equal to 1, and n is less than or equal to N, where N is the total number of neural network layers.

[0129] 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, and the process of obtaining this mapping from random w and b using existing data is called neural network training.

[0130] Optionally, a specific training method is to use a loss function to evaluate the output results of the neural network.

[0131] 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 loss function reaches a minimum, which is the "better point (e.g., optimal point)" in Figure 2d. It is understood that the neural network parameters corresponding to the "better point (e.g., optimal point)" in Figure 2d can be used as the neural network parameters in the trained AI model information.

[0132] Alternatively, the gradient descent process can be expressed as:

[0133] Among them, θ is the parameter to be optimized (including w and b), L is the loss function, and η is the learning rate, which controls the step size of gradient descent. represents the derivative operation, represents the derivative of θ with respect to L.

[0134] Optionally, the backpropagation process utilizes the chain rule for partial derivatives.

[0135] As shown in Figure 2e, the gradient of the previous layer parameters can be recursively calculated from the gradient of the next layer parameters, which can be expressed as:

[0136] Among them, w ijis the weight of node j connecting to node i, s i is the weighted sum of the inputs to node i.

[0137] The technical solutions provided in this application can be applied to wireless communication systems (e.g., the systems shown in Figures 1a, 1b, or 1c). In wireless communication systems, communication nodes generally have both signal transceiver capabilities and computing capabilities. For example, network devices with computing capabilities primarily provide computing power to support signal transceiver capabilities (e.g., performing signal transmission and reception processing) to enable communication between the network device and other communication nodes.

[0138] However, in communication networks, communication nodes may have excess computing power beyond just supporting the aforementioned communication tasks. Therefore, how to utilize this computing power is a pressing technical issue.

[0139] In order to solve the above problems, the present application provides a communication method and related equipment, which will be described in detail below with reference to the accompanying drawings.

[0140] Please refer to FIG3 , which is a schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.

[0141] It should be noted that, in the following, FIG3 and FIG5 illustrate the method by taking the first communication device and other communication devices as the execution subjects of the interaction diagram as examples, but the present application does not limit the execution subjects of the interaction diagram. For example, the communication device can be a communication device (such as a terminal device or a network device), or a chip, a baseband chip, a modem chip, a system on chip (SoC) chip including a modem core, a system in package (SIP) chip, a communication module, a chip system, a processor, a logic module or software in the communication device.

[0142] S301. The first communication device processes the collected first information based on the first neural network model to obtain a first communication parameter.

[0143] S302. The first communication device processes the first communication parameter based on the second neural network model to obtain a second communication parameter.

[0144] S303. The first communication device processes the signal based on the second communication parameter.

[0145] In this application, the neural network model can be replaced by other terms, such as model, AI model, AI network, AI neural network model, neural network, machine learning model or AI processing model, etc.

[0146] It should be understood that when a neural network model (e.g., the first neural network model, or the third neural network model described below) processes collected information to obtain communication parameters, this processing is referred to as a first processing, and the first processing may include one or more of prediction, inference, estimation, evaluation, decision-making, and pre-estimation. Optionally, such a neural network model may be one or more of a large wireless model with a large model parameter scale, a pre-trained large wireless model, or the like.

[0147] It should be understood that when a neural network model (e.g., the second neural network model, or the fourth neural network model described below) processes one communication parameter to obtain another communication parameter, this processing is referred to as second processing, and the second processing can be understood as one or more of correction, modification, adjustment, optimization, update, calibration, and fine-tuning. Optionally, this neural network model can be one or more of a correction model, a fine-tuning model, an additional layer model, etc., with a smaller scale of model parameters.

[0148] It should be noted that in S303, when the first communication device processes the signal based on the communication parameter, the first communication device may process the received signal based on the communication parameter, and / or the first communication device may process the transmitted signal based on the communication parameter.

[0149] Exemplarily, as shown in FIG4a, it is a schematic diagram of the implementation of the first neural network model and the second neural network model in the method shown in FIG3.

[0150] In FIG4 a , the input of the first neural network model may include first information, and the output of the first neural network model may include a first communication parameter.

[0151] In FIG4 a , the input of the second neural network model may include a first communication parameter, and the output of the second neural network model may include a second communication parameter.

[0152] It should be noted that the communication parameters involved in the embodiments of this application (e.g., one or more of the first to fifth communication parameters) may include parameters used in the communication process. For example: the encoding and / or decoding code rate, the sequence used for scrambling and / or descrambling, the modulation and / or demodulation order, channel parameters, communication rate, bit error rate, etc.

[0153] Optionally, in the process of obtaining another communication parameter by processing a communication parameter through a neural network model (for example, obtaining a second communication parameter by processing a first communication parameter through a second neural network model), the parameter type of the communication parameter and the second communication parameter are the same. For example, taking the example of a parameter whose parameter type is encoding code rate, the second communication parameter also includes a parameter whose parameter type is encoding code rate. For another example, taking the example of a parameter whose parameter type is modulation order, the second communication parameter also includes a parameter whose parameter type is modulation order.

[0154] Optionally, the parameter value of the one communication parameter may be the same as or different from the parameter value of the other communication parameter.

[0155] Based on the technical solution shown in Figure 3, the first communication device processes the first communication parameter based on the second neural network model in S302. After obtaining the second communication parameter, the first communication device can process the signal based on the second communication parameter in S303. The first communication parameter is obtained based on the first neural network model. In other words, the first communication device can process the communication signal based on the communication parameter obtained by the neural network processing. In this way, the computing power of the communication device can be applied to the processing of the neural network, and the communication parameters can also be quickly determined through the neural network model to reduce communication latency.

[0156] Furthermore, in the above technical solution, the second neural network model can process the first communication parameters output by the first neural network to obtain second communication parameters for signal processing. Compared to the method of determining communication parameters through a single neural network model, in the above technical solution, the first neural network model can be a model with higher generalization, that is, the first communication parameters output by the first neural network model may be applicable to a variety of communication tasks, and the second neural network model can further process the communication parameters and optimize the communication parameters to improve the communication performance based on the second communication parameters.

[0157] In one possible implementation, the information collected by the first communication device (e.g., the first information collected in S301 and / or the second information collected by the first communication device described below) includes at least one of the following: environmental information, user information, or task information. Thus, the information collected by the first communication device may include at least one of the above items, thereby increasing the flexibility of solution implementation.

[0158] As an implementation example, with respect to the environmental information included in the information collected by the first communication device, the environmental information may include information about the communication environment in which the first communication device is located. For example, the environmental information may include one or more of the following: information indicating whether the environment is indoors, information indicating whether the environment is outdoor, environmental perception information (e.g., one or more of temperature information, humidity information, etc.), and geographic environment information.

[0159] As an implementation example, the user information included in the information collected by the first communication device may include information of the user corresponding to the first communication device. For example, the user information may include one or more of the following: identity information, service subscription information, mobile speed information, device hardware information, etc.

[0160] As an implementation example, the task information included in the information collected by the first communication device may include information about the task performed by the first communication device. The task may include a communication task (e.g., one or more of a data transmission task and a signal acquisition task), an AI task (e.g., one or more of a channel information prediction task and a path loss prediction task), etc. For example, the task information may include one or more of the following: a task identifier, task-related parameters, etc.

[0161] In one possible implementation, before the first communication device processes the collected first information based on the first neural network model to obtain the first communication parameter, the method also includes: the first communication device communicates based on a third communication parameter, and the third communication parameter is determined based on the communication signal received and / or sent by the first communication device; wherein the third communication parameter and the fourth communication parameter are used to process the third neural network model to obtain the first neural network model; and the fourth communication parameter is the communication parameter obtained by the third neural network model processing the collected second information.

[0162] Specifically, before the first communication device determines the first communication parameter based on the first neural network model, the first communication device may also process the third neural network model based on the third communication parameter during communication based on the third communication parameter to obtain the first neural network model. Since the third communication parameter is determined based on the communication signal received and / or sent by the first communication device, the third communication parameter can reflect the actual communication environment. In this way, while improving the performance of the first neural network model obtained based on the third communication parameter, the first communication device can also reuse the communication signal to implement model processing, without the need to collect specific samples in the model processing process, which can reduce overhead.

[0163] It should be understood that in the process of processing one neural network model to obtain another neural network model (for example, processing the third neural network model to obtain the first neural network model, or processing the fourth neural network model described later to obtain the second neural network model, etc.), the processing is recorded as the third processing, and the third processing can be understood as one or more of: training, iteration, optimization, fine-tuning, updating, tuning, and improvement.

[0164] Optionally, the third communication parameter and the fourth communication parameter are used to process the third neural network model to obtain the first neural network model, which may include: the third communication parameter and the fourth communication parameter can be used as inputs to the third neural network model, and after the above-mentioned third processing, the first neural network model is obtained; or, the third communication parameter and the performance of the communication signal processed based on the fourth communication parameter can be used as inputs to the third neural network model, and after the above-mentioned third processing, the first neural network model is obtained.

[0165] Exemplarily, as shown in FIG4 b , it is a schematic diagram of the third neural network model in the above process.

[0166] In FIG4b , the first communication device may process a received or transmitted communication signal based on a conventional communication module to obtain a third communication parameter. Furthermore, the third communication parameter may be used for signal processing, that is, the first communication device may receive or transmit a signal based on the third communication parameter.

[0167] In FIG4b , for the third neural network model, one of the processing steps includes: the input of the third neural network model may include the second information, and the output of the third neural network model may include the fourth communication parameter. The processing step may be similar to the first processing step described above. In addition, for the third neural network model, another processing step includes: the third communication parameter and the fourth communication parameter may be used as inputs to the third neural network model (or, the performance of the communication signal processed based on the fourth communication parameter is used as the model input data (or training data) of the third neural network model, and the third communication parameter is used as the corresponding label). Through the third processing step, the first neural network model is obtained.

[0168] Optionally, the processing involved in the traditional communication module involved in the embodiments of the present application may include one or more of the following: coding, rate matching, scrambling, modulation, layer mapping, precoding, resource element (RE) mapping, digital beamforming (BF), inverse fast Fourier transformation (IFFT) / adding a cyclic prefix (CP), decoding, rate matching, descrambling, demodulation, inverse discrete Fourier transformation (IDFT), channel equalization (or channel estimation), RE mapping, digital BF, fast Fourier transform (FFT) / de-CP, digital to analog (DA) conversion, analog BF, analog to digital (AD) conversion, or analog BF, scheduling, retransmission, etc.

[0169] In addition, the communication parameters involved in the embodiments of the present application (e.g., one or more of the first to fifth communication parameters) may include parameters used in the communication process. For example, the encoding and / or decoding code rate, the sequence used for scrambling and / or descrambling, the modulation and / or demodulation order, channel parameters, communication rate, bit error rate, etc.

[0170] In one possible implementation, before the first communication device processes the first communication parameter based on the second neural network model to obtain the second communication parameter, the method also includes: the first communication device communicates based on a fifth communication parameter, and the fifth communication parameter is determined based on the communication signal received and / or sent by the first communication device; wherein the fifth communication parameter and the first communication parameter are used to process a fourth neural network model to obtain the second neural network model.

[0171] Specifically, before the first communication device determines the second communication parameter based on the second neural network model, the first communication device may also update the fourth neural network model based on the fifth communication parameter during communication based on the fifth communication parameter to obtain the second neural network model. Since the fifth communication parameter is determined based on the communication signal received and / or sent by the first communication device, the fifth communication parameter can reflect the actual communication environment. In this way, while improving the performance of the second neural network model obtained based on the fifth communication parameter, the first communication device can also reuse the communication signal to achieve model update, without the need to collect specific samples during the model update process, which can reduce overhead.

[0172] Optionally, the fifth communication parameter and the first communication parameter are used to process the fourth neural network model to obtain the second neural network model, which may include: the fifth communication parameter and the first communication parameter can be used as inputs of the fourth neural network model, and after the above-mentioned third processing, the second neural network model is obtained; or, the fifth communication parameter and the performance of the communication signal processed based on the first communication parameter can be used as inputs of the fourth neural network model, and after the above-mentioned third processing, the second neural network model is obtained.

[0173] Optionally, the third communication parameter and the fifth communication parameter may be the same or partially the same. In this way, the same communication parameters can be reused to process different neural network models, further reducing overhead.

[0174] Optionally, the third communication parameter and the fifth communication parameter are different from each other.

[0175] Exemplarily, as shown in FIG4c , it is a schematic diagram of the fourth neural network model in the above process.

[0176] In Figure 4c, the first communication device can process the received or sent communication signal based on the traditional communication module to obtain the fifth communication parameter. In addition, the fifth communication parameter can be used for signal processing, that is, the first communication device can receive or send a signal based on the fifth communication parameter.

[0177] In FIG4c , the input of the first neural network model may include first information, and the output of the first neural network model may include a first communication parameter. The processing may be similar to the first processing described above.

[0178] In Figure 4c, in the fourth neural network model, the first communication parameter and the fifth communication parameter can be used as inputs of the fourth neural network model (or, the performance of the communication signal processed based on the first communication parameter is used as model input data (or training data) of the fourth neural network model, and the fifth communication parameter is used as the corresponding label), and the second neural network model is obtained through the third processing process.

[0179] In one possible implementation, the method also includes: the first communication device obtains third information, and the third information is used to indicate the communication performance corresponding to the first communication parameter obtained by processing the first neural network model; the first communication device processes the first communication parameter based on the second neural network model to obtain the second communication parameter, including: the first communication device processes the third information and the first communication parameter based on the second neural network model to obtain the second communication parameter.

[0180] Specifically, the first communication device can also obtain communication performance based on the first communication parameter obtained by the first neural network model, and the first communication device can process the communication performance and the first communication parameter based on the second neural network model. In this way, because the basis for determining the second communication parameter by the second neural network model also includes communication performance, the second neural network model can determine the communication parameter based on feedback from the communication performance, thereby supporting real-time correction of the communication parameter based on the feedback from the communication performance, thereby improving the flexibility of the correction.

[0181] It should be noted that, for the first communication device, the first communication device can obtain the communication performance corresponding to the communication parameters (such as the third information mentioned above, the fourth information and the fifth information described later, etc.) in a variety of ways.

[0182] For example, after the first communication device receives a signal based on the communication parameters, the first communication device may locally receive signal quality information of the received signal, where the signal quality information may be used to indicate communication performance corresponding to the communication parameters. For example, the signal quality information may include one or more of a bit error rate, a block error rate, a reference signal received power (RSRP), and a signal to interference plus noise ratio (SINR).

[0183] For another example, after the first communication device sends a signal based on the communication parameters, the first communication device may receive signal quality information fed back by the signal receiver, where the signal quality information may be used to indicate the communication performance corresponding to the communication parameters. For example, the signal quality information may include one or more of hybrid automatic repeat request-acknowledgement (HARQ-ACK) information, bit error rate, block error rate, RSRP, and SINR.

[0184] Exemplarily, as shown in FIG4 d , it is a schematic diagram of the second neural network model in the above process.

[0185] In FIG4d , for the first neural network model, one of the processing processes is as follows: the input of the first neural network model may include first information, and the output of the first neural network model may include first communication parameters. Thereafter, the first communication device may process the received or transmitted communication signal based on the first communication parameters output by the first neural network model. Furthermore, the first communication device may obtain third information indicating the performance of the received or transmitted communication signal. Optionally, in this process, the first communication parameters output by the first neural network model are used for signal processing. For this purpose, this process may be referred to as a process of using the first neural network model.

[0186] In FIG4d , for the second neural network model, the input of the second neural network model may include the first communication parameter and the third information, and the output of the second neural network model may include the second communication parameter. Subsequently, the first communication device may process the signal based on the second communication parameter. Optionally, in this process, the first communication parameter output by the first neural network model is subjected to the second processing by the second neural network model to obtain the second communication parameter for signal processing. For this reason, this process can be referred to as a correction (or real-time correction) process of the first neural network model.

[0187] Optionally, the second neural network model includes a first sub-model and a second sub-model; wherein, the first sub-model is used to pre-process the third information to obtain a processing result, and the second sub-model is used to fuse the processing result and the first communication parameter to obtain the second communication parameter.

[0188] Optionally, the preprocessing may include one or more of aligning dimensions, implicitly extracting information related to wireless parameters, denoising, normalization, and standardization.

[0189] For example, as shown in FIG4e , when the second neural network model includes a first sub-model and a second sub-model, FIG4d can be represented as the implementation process shown in FIG4e , that is, the implementation of each model in FIG4e can refer to the process shown in FIG4d . Unlike the implementation process shown in FIG4d , the process executed by the second neural network model is processed by the first sub-model and the second sub-model respectively.

[0190] In one possible implementation, in the process of processing based on the third information, when at least one of the following conditions is met, the first communication device processes the third information and the first communication parameter based on the second neural network model to obtain the second communication parameter, including:

[0191] The communication performance corresponding to the first communication parameter is lower than a threshold;

[0192] It is determined that a duration of communication based on the first communication parameter is greater than or equal to a preset duration.

[0193] Specifically, when at least one of the above conditions is met, the first communication device may determine that the communication parameters output by the first neural network model need to be processed by the second neural network model. To this end, the first communication device may trigger the processing of the third information and the first communication parameters based on the second neural network model to obtain the second communication parameters. Through the above method, an event-based triggering method and a fixed-cycle-based triggering method are provided to enhance the flexibility of the solution implementation.

[0194] In a possible implementation, during the process based on the first communication parameter, the method further includes: when at least one of the following conditions is satisfied, the first communication device processes the signal based on the first communication parameter, including:

[0195] The performance corresponding to the first communication parameter is higher than or equal to a threshold;

[0196] It is determined that a duration of communication based on the first communication parameter is less than a preset duration.

[0197] Specifically, when at least one of the above conditions is met, the first communication device may determine that the communication parameters output by the first neural network model do not need to be processed by the second neural network model. To this end, the first communication device may trigger the first communication device to process the signal based on the first communication parameter without the participation of the second neural network model. Through the above method, an event-based triggering method and a fixed-cycle-based triggering method are provided to enhance the flexibility of the solution implementation.

[0198] In a possible implementation of the method shown in FIG3 , after the first communication device processes the signal based on the second communication parameter in S303, the method further includes: the first communication device obtains fourth information, the fourth information being used to indicate the communication performance corresponding to the second communication parameter; the first communication device performs model update processing on the first neural network model and / or the second neural network model based on the fourth information. Specifically, the first communication device can also obtain the communication performance of communicating based on the second communication parameter, and the first communication device can perform model update processing on the first neural network model and / or the second neural network model based on the communication performance. In this way, since the communication performance indicated by the fourth information can be used to indicate the performance of the first neural network model and / or the second neural network model, feedback based on the communication performance can be used to correct the neural network model, thereby improving the flexibility of correction.

[0199] Optionally, the model update process can be replaced by other processing of the model, such as one or more of training, iteration, optimization, fine-tuning, updating, tuning, and improvement.

[0200] Optionally, in the above process, when at least one of the following items is satisfied, the first communication device performs model update processing on the first neural network model and / or the second neural network model based on the fourth information, including:

[0201] The number of times that the performance corresponding to the second communication parameter is lower than the threshold reaches a threshold;

[0202] It is determined that a duration of communication based on the second communication parameter is greater than or equal to a preset duration.

[0203] Specifically, when at least one of the above conditions is met, the first communication device may determine that the second neural network model and / or the first neural network model needs to be updated. To this end, the first communication device may trigger the update of the first neural network model and / or the second neural network model based on the fourth information. Through the above method, an event-based triggering method and a fixed-cycle-based triggering method are provided to enhance the flexibility of the solution implementation.

[0204] Exemplarily, as shown in FIG4f , it is a schematic diagram of the model updating process in the above process.

[0205] In FIG4f , for the first neural network model and the second neural network model, one processing process is as follows: the input of the first neural network model may include first information, and the output of the first neural network model may include first communication parameters; thereafter, the input of the second neural network model may include the first communication parameters, and the output of the second neural network model may include second communication parameters. Thereafter, the first communication device may process the received or transmitted communication signal based on the second communication parameters.

[0206] In FIG4f , for the first neural network model, another processing process is as follows: the first communication device obtains fourth information indicating the communication performance of the second communication parameter, and the fourth information can be used as the model input data of the first neural network model, and the model update process is implemented through the third processing process. Optionally, in the model update process, in addition to using the fourth information as the model input data (or training data) of the first neural network model, other communication parameters (the other communication parameters can be determined based on the communication signal received by the first communication device, such as the third communication parameter, the fifth communication parameter, etc. described above) can also be used as corresponding labels to implement the model update process through the third processing process.

[0207] In FIG4f , for the second neural network model, another processing process is as follows: the first communication device obtains fourth information indicating the communication performance of the second communication parameter, and the fourth information can be used as the model input data of the second neural network model, and the model update process is implemented through the third processing process. Optionally, in the model update process, in addition to using the fourth information as the model input data (or training data) of the second neural network model, other communication parameters (the other communication parameters can be determined based on the communication signal received by the first communication device, such as the third communication parameter, the fifth communication parameter, etc. described above) can also be used as corresponding labels to implement the model update process through the third processing process.

[0208] Please refer to FIG5 , which is another schematic diagram of an implementation of the communication method provided in this application. The method includes the following steps.

[0209] S501. The first communication device communicates based on a third communication parameter, and the third communication parameter is determined based on the communication signal received and / or sent by the first communication device; wherein the third communication parameter and the fourth communication parameter are used to process the third neural network model to obtain the first neural network model; the fourth communication parameter is the communication parameter obtained by the third neural network model when processing the collected second information.

[0210] S502. The first communication device processes the collected first information based on the first neural network model to obtain a first communication parameter.

[0211] S503. The first communication device processes the signal based on the first communication parameter.

[0212] Based on the technical solution shown in Figure 5, when the first communication device communicates based on the third communication parameter, the first communication device can process the third neural network model based on the third communication parameter to obtain the first neural network model. Since the third communication parameter is determined based on the communication signal received and / or sent by the first communication device, the third communication parameter can reflect the actual communication environment. In this way, while improving the performance of the first neural network model obtained based on the third communication parameter, the first communication device can also reuse the communication signal to realize model processing, without collecting specific samples in the model processing process, which can reduce overhead.

[0213] It should be noted that the implementation process of the third neural network model in the above process can refer to the implementation example shown in Figure 4b above.

[0214] In a possible implementation of the method shown in Figure 5, after the first communication device processes the signal based on the first communication parameter in S502, the method also includes: the first communication device obtains fifth information, and the fifth information is used to indicate the performance corresponding to the first communication parameter; the first communication device performs model update processing on the first neural network model based on the fifth information.

[0215] Optionally, during the model update processing, in addition to using the fifth information as the model input data (or training data) of the first neural network model, other communication parameters (the other communication parameters can be determined based on the communication signal received by the first communication device, such as the third communication parameter, fifth communication parameter, etc. described above) can be used as corresponding labels to implement the model update process through the third processing process.

[0216] Specifically, the first communication device can also obtain the communication performance of communication based on the first communication parameter, and the first communication device can perform model update processing on the first neural network model based on the communication performance. In this way, since the communication performance indicated by the fifth information can be used to indicate the performance of the first neural network model, feedback based on the communication performance can be used to correct the neural network model, thereby improving the flexibility of the correction.

[0217] Exemplarily, as shown in FIG6 , it is a schematic diagram of the model update processing in the above process.

[0218] In FIG6 , for the first neural network model, one processing process is as follows: the input of the first neural network model may include first information, and the output of the first neural network model may include first communication parameters. Thereafter, the first communication device may process received or transmitted communication signals based on the first communication parameters.

[0219] In Figure 6, for the first neural network model, another processing process is: the first communication device obtains fifth information for indicating the communication performance of the first communication parameter, and the fifth information can be used as model input data (or training data) of the first neural network model, through the third processing process to realize the model update process.

[0220] Optionally, the first communication device performs model update processing on the first neural network model based on the fifth information, including: when at least one of the following items is satisfied, the first communication device performs model update processing on the first neural network model based on the fifth information, including:

[0221] The performance corresponding to the first communication parameter is lower than a threshold;

[0222] It is determined that a duration of communication based on the first communication parameter is greater than or equal to a preset duration.

[0223] Specifically, when at least one of the above conditions is met, the first communication device may determine that a model update process needs to be performed on the first neural network model. To this end, the first communication device may trigger a model update process on the first neural network model based on the fifth information. Through the above method, an event-based triggering method and a fixed-cycle-based triggering method are provided to enhance the flexibility of the solution implementation.

[0224] Referring to FIG. 7 , an embodiment of the present application provides a communication device 700. The communication device 700 can implement the functions of the first communication device in the above-described method embodiment, thereby also achieving the beneficial effects of the above-described method embodiment. In the embodiment of the present application, the communication device 700 can be the first communication device, or it can be an integrated circuit or component within the first communication device, such as a chip, a baseband chip, a modem chip, a SoC chip including a modem core, a system-in-package (SIP) chip, a communication module, a chip system, a processor, etc.

[0225] It should be noted that the transceiver unit 702 may include a sending unit and a receiving unit, which are respectively used to perform sending and receiving.

[0226] In one possible implementation, when the device 700 is used to execute the method executed by the first communication device in Figure 3 and related embodiments, the device 700 includes a processing unit 701 and a transceiver unit 702; the processing unit 701 is used to process the collected first information based on the first neural network model to obtain a first communication parameter; the processing unit 701 is also used to process the first communication parameter based on the second neural network model to obtain a second communication parameter; the transceiver unit 702 is used to process the signal based on the second communication parameter.

[0227] In one possible implementation, when the device 700 is used to execute the method executed by the first communication device in Figure 3 and related embodiments, the device 700 includes a processing unit 701 and a transceiver unit 702; the processing unit 701 is used to communicate based on a third communication parameter, and the third communication parameter is determined based on the communication signal received and / or sent by the first communication device; wherein the third communication parameter and the fourth communication parameter are used to process the third neural network model to obtain the first neural network model; the fourth communication parameter is the communication parameter obtained by the third neural network model processing the collected second information; the processing unit 701 is also used to process the collected first information based on the first neural network model to obtain the first communication parameter; the transceiver unit 702 is used to process the signal based on the first communication parameter.

[0228] In one possible design, when the communication device 700 is a terminal device or a communication module in a terminal, the functions of the processing unit 701 can be implemented by one or more processors. Specifically, the processor can include a modem chip, or a SoC chip or SIP chip containing a modem core. The functions of the transceiver unit 702 can be implemented by a transceiver circuit.

[0229] In one possible design, when the communication device 700 is a circuit or chip responsible for communication functions in a terminal, such as a modem chip or a SoC chip or SIP chip containing a modem core, the functions of the processing unit 701 can be implemented by a circuit system including one or more processors or processor cores in the above chip. The functions of the transceiver unit 702 can be implemented by an interface circuit or data transceiver circuit on the above chip.

[0230] It should be noted that, for details on the information execution process of the units of the above-mentioned communication device 700, please refer to the description in the method embodiment shown above in this application, and no further details will be given here.

[0231] Please refer to Fig. 8, which is another schematic structural diagram of a communication device 800 provided in this application. The communication device 800 includes a logic circuit 801 and an input / output interface 802. The communication device 800 may be a chip or an integrated circuit.

[0232] The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the input / output interface 802 in FIG8 , which may include an input interface and an output interface. Alternatively, the communication interface may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0233] In one possible implementation, when the device 800 is used to execute the method executed by the first communication device in Figure 3 and related embodiments, the logic circuit 801 is used to process the collected first information based on the first neural network model to obtain a first communication parameter; the logic circuit 801 is also used to process the first communication parameter based on the second neural network model to obtain a second communication parameter; the input and output interface 802 is used to process the signal based on the second communication parameter.

[0234] In one possible implementation, when the device 800 is used to execute the method executed by the first communication device in Figure 3 and related embodiments, the logic circuit 801 is used to communicate based on a third communication parameter, and the third communication parameter is determined based on the communication signal received and / or sent by the first communication device; wherein the third communication parameter and the fourth communication parameter are used to process the third neural network model to obtain the first neural network model; the fourth communication parameter is the communication parameter obtained by the third neural network model processing the collected second information; the logic circuit 801 is also used to process the collected first information based on the first neural network model to obtain the first communication parameter; the input and output interface 802 is used to process the signal based on the first communication parameter.

[0235] The logic circuit 801 and the input / output interface 802 may also execute other steps executed by the first communication device in any embodiment and achieve corresponding beneficial effects, which will not be described in detail here.

[0236] In a possible implementation, the processing unit 701 shown in FIG. 7 may be the logic circuit 801 in FIG. 8 .

[0237] Optionally, the logic circuit 801 may be a processing device, and the functions of the processing device may be partially or entirely implemented by software. The functions of the processing device may be partially or entirely implemented by software.

[0238] Optionally, the processing device 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 corresponding processing and / or steps in any one of the method embodiments.

[0239] Alternatively, the processing device may include only a processor. A memory for storing the computer program is located outside the processing device, and the processor is connected to the memory via circuits / wires to read and execute the computer program stored in the memory. The memory and processor may be integrated or physically separate.

[0240] 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 processor units (CPUs), network processors (NPs), digital signal processors (DSPs), microcontroller units (MCUs), programmable logic controllers (PLDs), or other integrated chips, or any combination of the above chips or processors.

[0241] Please refer to Figure 9, which shows a communication device 900 involved in the above-mentioned embodiments provided in an embodiment of the present application. The communication device 900 can specifically be a communication device serving as a terminal device in the above-mentioned embodiments. The example shown in Figure 9 is that the terminal device is implemented through the terminal device (or a component in the terminal device).

[0242] Here, a possible logical structure diagram of the communication device 900 is shown. The communication device 900 may include but is not limited to at least one processor 901 and a communication port 902 .

[0243] The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the communication port 902 in FIG9 , which may include an input interface and an output interface. Alternatively, the communication port 902 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0244] Further optionally, the device may also include at least one of a memory 903 and a bus 904. In an embodiment of the present application, the at least one processor 901 is used to control and process the actions of the communication device 900.

[0245] In addition, the processor 901 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 device, a transistor logic device, a hardware component, 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, and so on. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0246] It should be noted that the communication device 900 shown in Figure 9 can be specifically used to implement the steps implemented by the terminal device in the aforementioned method embodiment and achieve the corresponding technical effects of the terminal device. The specific implementation methods of the communication device shown in Figure 9 can refer to the description in the aforementioned method embodiment and will not be repeated here.

[0247] Please refer to Figure 10, which is a structural diagram of the communication device 1000 involved in the above-mentioned embodiments provided in an embodiment of the present application. The communication device 1000 can specifically be a communication device as a network device in the above-mentioned embodiments. The example shown in Figure 10 is that the network device is implemented through the network device (or a component in the network device), wherein the structure of the communication device can refer to the structure shown in Figure 10.

[0248] The communication device 1000 includes at least one processor 1011 and at least one network interface 1014. Further optionally, the communication device also includes at least one memory 1012, at least one transceiver 1013 and one or more antennas 1015. The processor 1011, the memory 1012, the transceiver 1013 and the network interface 1014 are connected, for example, via a bus. In an embodiment of the present application, the connection may include various interfaces, transmission lines or buses, etc., which are not limited in this embodiment. The antenna 1015 is connected to the transceiver 1013. The network interface 1014 is used to enable the communication device to communicate with other communication devices through a communication link. For example, the network interface 1014 may include a network interface between the communication device and the core network device, such as an S1 interface, and the network interface may include a network interface between the communication device and other communication devices (such as other network devices or core network devices), such as an X2 or Xn interface.

[0249] The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the network interface 1014 in FIG10 , which may include an input interface and an output interface. Alternatively, the network interface 1014 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0250] Processor 1011 is primarily used to process communication protocols and communication data, control the entire communication device, execute software programs, and process software program data, for example, to support the communication device in performing the actions described in the embodiments. The communication device may include a baseband processor and a central processing unit. The baseband processor is primarily used to process communication protocols and communication data, while the central processing unit is primarily used to control the entire terminal device, execute software programs, and process software program data. Processor 1011 in Figure 10 may integrate the functions of both a baseband processor and a central processing unit. Those skilled in the art will appreciate that the baseband processor and the central processing unit may also be independent processors interconnected via a bus or other technology. Those skilled in the art will appreciate that a terminal device may include multiple baseband processors to accommodate different network standards, multiple central processing units to enhance its processing capabilities, and various components of the terminal device may be connected via various buses. The baseband processor may also be referred to as a baseband processing circuit or a baseband processing chip. The central processing unit may also be referred to as a central processing circuit or a central processing chip. The functionality for processing communication protocols and communication data may be built into the processor or stored in memory as a software program, which is executed by the processor to implement the baseband processing functionality.

[0251] The memory is primarily used to store software programs and data. Memory 1012 can exist independently and be connected to processor 1011. Alternatively, memory 1012 and processor 1011 can be integrated together, for example, within a single chip. Memory 1012 can store program code for executing the technical solutions of the embodiments of the present application, and execution is controlled by processor 1011. The various computer program codes executed can also be considered drivers for processor 1011.

[0252] Figure 10 shows only one memory and one processor. In an actual terminal device, there may be multiple processors and multiple memories. The memory may also be referred to as a storage medium or a storage device. The memory may be a storage element on the same chip as the processor, i.e., an on-chip storage element, or an independent storage element, which is not limited in the present embodiment.

[0253] The transceiver 1013 can be used to support the reception or transmission of radio frequency signals between the communication device and the terminal. The transceiver 1013 can be connected to the antenna 1015. The transceiver 1013 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 1015 can receive radio frequency signals. The receiver Rx of the transceiver 1013 is used to receive the radio frequency signal from the antenna, convert the radio frequency signal into a digital baseband signal or a digital intermediate frequency signal, and provide the digital baseband signal or digital intermediate frequency signal to the processor 1011 so that the processor 1011 can further process the digital baseband signal or digital intermediate frequency signal, such as demodulation and decoding. In addition, the transmitter Tx in the transceiver 1013 is also used to receive a modulated digital baseband signal or digital intermediate frequency signal from the processor 1011, convert the modulated digital baseband signal or digital intermediate frequency signal into a radio frequency signal, and transmit the radio frequency signal through one or more antennas 1015. Specifically, the receiver Rx can selectively perform one or more stages of down-mixing and analog-to-digital conversion on the RF signal to obtain a digital baseband signal or a digital intermediate frequency signal. The order of the down-mixing and analog-to-digital conversion processes is adjustable. The transmitter Tx can selectively perform one or more stages of up-mixing and digital-to-analog conversion on the modulated digital baseband signal or digital intermediate frequency signal to obtain a RF signal. The order of the up-mixing and digital-to-analog conversion processes is adjustable. The digital baseband signal and the digital intermediate frequency signal may be collectively referred to as digital signals.

[0254] The transceiver 1013 may also be referred to as a transceiver unit, a transceiver, a transceiver device, etc. Optionally, a device in the transceiver unit that implements a receiving function may be referred to as a receiving unit, and a device in the transceiver unit that implements a transmitting function may be referred to as a transmitting unit. That is, the transceiver unit includes a receiving unit and a transmitting unit. The receiving unit may also be referred to as a receiver, an input port, a receiving circuit, etc., and the transmitting unit may be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

[0255] It should be noted that the communication device 1000 shown in Figure 10 can be specifically used to implement the steps implemented by the network device in the aforementioned method embodiment, and to achieve the corresponding technical effects of the network device. The specific implementation methods of the communication device 1000 shown in Figure 10 can refer to the description in the aforementioned method embodiment, and will not be repeated here one by one.

[0256] Please refer to FIG11 , which is a schematic structural diagram of the communication device involved in the above-mentioned embodiment provided in an embodiment of the present application.

[0257] It can be understood that the communication device 110 includes, for example, modules, units, elements, circuits, or interfaces, which are appropriately configured together to implement the technical solutions provided in this application. The communication device 110 can be the terminal device or network device described above, or a component (such as a chip) in these devices, used to implement the method described in the following method embodiment. The communication device 110 includes one or more processors 111. The processor 111 can be a general-purpose processor or a dedicated processor. For example, it can 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 (such as a RAN node, terminal, or chip, etc.), execute software programs, and process data of software programs.

[0258] Optionally, in one design, the processor 111 may include a program 113 (sometimes also referred to as code or instructions), which may be executed on the processor 111 to cause the communication device 110 to perform the methods described in the following embodiments. In yet another possible design, the communication device 110 includes circuitry (not shown in FIG11 ).

[0259] Optionally, the communication device 110 may include one or more memories 112 on which a program 114 (sometimes also referred to as code or instructions) is stored. The program 114 can be run on the processor 111, so that the communication device 110 executes the method described in the above method embodiment.

[0260] Optionally, the processor 111 and / or the memory 112 may include AI modules 117 and 118, which are used to implement AI-related functions. The AI ​​module may be implemented through software, hardware, or a combination of software and hardware. For example, the AI ​​module may include a wireless intelligent control (RIC) module. For example, the AI ​​module may be a near real-time RIC or a non-real-time RIC.

[0261] Optionally, data may be stored in the processor 111 and / or the memory 112. The processor and the memory may be provided separately or integrated together.

[0262] Optionally, the communication device 110 may further include a transceiver 115 and / or an antenna 116. The processor 111 may also be referred to as a processing unit, and controls the communication device (e.g., a RAN node or terminal). The transceiver 115 may also be referred to as a transceiver unit, a transceiver, a transceiver circuit, or a transceiver, and is configured to implement the transceiver functions of the communication device through the antenna 116.

[0263] The processing unit 701 shown in FIG7 may be the processor 111. The transceiver unit 702 shown in FIG7 may be a communication interface, which may be the transceiver 115 shown in FIG11 . The transceiver 115 may include an input interface and an output interface. Alternatively, the transceiver 115 may be a transceiver circuit, which may include an input interface circuit and an output interface circuit.

[0264] An embodiment of the present application further provides a computer-readable storage medium, which is used to store one or more computer-executable instructions. When the computer-executable instructions are executed by a processor, the processor executes the method described in the possible implementation methods of the first communication device or the second communication device in the aforementioned embodiment.

[0265] An embodiment of the present application also provides a computer program product (or computer program). When the computer program product is executed by the processor, the processor executes the method of the possible implementation mode of the above-mentioned first communication device.

[0266] An embodiment of the present application also provides a chip system, which includes at least one processor for supporting a communication device to implement the functions involved in the possible implementation methods of the above-mentioned communication device. Optionally, the chip system also includes an interface circuit, which provides program instructions and / or data to the at least one processor. In one possible design, the chip system may also include a memory, which is used to store the necessary program instructions and data for the communication device. The chip system can be composed of chips, or it can include chips and other discrete devices, wherein the communication device can specifically be the first communication device in the aforementioned method embodiment.

[0267] An embodiment of the present application further provides a communication system, wherein the network system architecture includes the first communication device in any of the above embodiments.

[0268] Optionally, the communication system further includes other communication devices communicating with the first communication device.

[0269] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. Whether a function is performed 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 to be beyond the scope of this application.

[0270] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0271] In addition, the functional units in the various embodiments of the present application can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. If the integrated unit is implemented in the form of 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 the present application is essentially or the contributing part or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

Claims

1. A communication method, characterized in that: include: Processing the collected first information based on the first neural network model to obtain a first communication parameter; Processing the first communication parameter based on a second neural network model to obtain a second communication parameter; The signal is processed based on the second communication parameter.

2. The method according to claim 1, characterized in that Before processing the collected first information based on the first neural network model to obtain the first communication parameter, the method further includes: communicating based on a third communication parameter, the third communication parameter being determined based on a communication signal received and / or sent by the first communication device; Among them, the third communication parameter and the fourth communication parameter are used to process the third neural network model to obtain the first neural network model; the fourth communication parameter is the communication parameter obtained by the third neural network model processing the collected second information.

3. The method according to claim 1 or 2, characterized in that Before processing the first communication parameter based on the second neural network model to obtain the second communication parameter, the method further includes: communicating based on a fifth communication parameter, the fifth communication parameter being determined based on a communication signal received and / or sent by the first communication device; The fifth communication parameter and the first communication parameter are used to process the fourth neural network model to obtain the second neural network model.

4. The method according to any one of claims 1 to 3, characterized in that The method further comprises: Acquire third information, where the third information is used to indicate communication performance corresponding to the first communication parameter; The processing of the first communication parameter based on the second neural network model to obtain the second communication parameter includes: The third information and the first communication parameter are processed based on the second neural network model to obtain the second communication parameter.

5. The method according to claim 4, characterized in that The second neural network model includes a first sub-model and a second sub-model; wherein, the first sub-model is used to pre-process the first information to obtain a processing result, and the second sub-model is used to fuse the processing result and the first communication parameter to obtain the second communication parameter.

6. The method according to claim 4 or 5, characterized in that The processing of the third information and the first communication parameter based on the second neural network model to obtain the second communication parameter includes: When at least one of the following is satisfied, processing the third information and the first communication parameter based on the second neural network model to obtain the second communication parameter includes: The communication performance corresponding to the first communication parameter is lower than a threshold; Determine whether a duration of communication based on the first communication parameter is greater than or equal to a preset duration.

7. The method according to any one of claims 4 to 6, characterized in that The method further comprises: When at least one of the following is satisfied, processing the signal based on the first communication parameter includes: The performance corresponding to the first communication parameter is higher than or equal to a threshold; It is determined that a duration of communication based on the first communication parameter is less than a preset duration.

8. The method according to any one of claims 1 to 7, characterized in that After processing the signal based on the second communication parameter, the method further includes: Acquire fourth information, where the fourth information is used to indicate communication performance corresponding to the second communication parameter; Perform model update processing on the first neural network model and / or the second neural network model based on the fourth information.

9. The method according to claim 8, characterized in that The performing model updating processing on the first neural network model and / or the second neural network model based on the fourth information includes: When at least one of the following conditions is met, performing model update processing on the first neural network model and / or the second neural network model based on the fourth information includes: The number of times that the performance corresponding to the second communication parameter is lower than the threshold reaches a threshold; Determine whether a duration of communication based on the second communication parameter is greater than or equal to a preset duration.

10. The method according to any one of claims 1 to 9, characterized in that The collected first information includes at least one of the following: Environmental information, user information, or task information.

11. A communication method, characterized in that: include: Communicating based on a third communication parameter, the third communication parameter being determined based on a communication signal received and / or sent by the first communication device; wherein the third communication parameter and the fourth communication parameter are used to process the third neural network model to obtain the first neural network model; and the fourth communication parameter is a communication parameter obtained by the third neural network model processing the collected second information; Processing the collected first information based on the first neural network model to obtain a first communication parameter; A signal is processed based on the first communication parameter.

12. The method according to claim 11, characterized in that After processing the signal based on the first communication parameter, the method further includes: Acquire fifth information, where the fifth information is used to indicate performance corresponding to the first communication parameter; Perform model update processing on the first neural network model based on the fifth information.

13. The method according to claim 12, characterized in that The performing model updating processing on the first neural network model based on the fifth information includes: When at least one of the following conditions is met, performing model update processing on the first neural network model based on the fifth information includes: The performance corresponding to the first communication parameter is lower than a threshold; Determine whether a duration of communication based on the first communication parameter is greater than or equal to a preset duration.

14. A communication device, characterized in that: Comprising means for performing the method according to any one of claims 1 to 13.

15. A communication device, characterized in that: The method comprises at least one processor configured to execute the method according to any one of claims 1 to 13.

16. A readable storage medium, characterized in that The storage medium stores a computer program or instruction. When the computer program or instruction is executed by the communication device, the method according to any one of claims 1 to 13 is implemented.

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