Communication method and communication device

Abstract

The embodiment of the invention provides a communication method and a communication device so as to improve the flexibility and efficiency of model alignment of both communication parties. The method comprises the following steps: a first communication device sends first information, wherein the first information indicates a model alignment mode supported by the first communication device; the first communication device receives second information, the second information indicates a target model alignment mode, the target model alignment mode is a model alignment mode supported by the second communication device, and the target alignment mode is a model alignment mode in the model alignment modes supported by the first communication device; and the first communication device performs model alignment based on the target model alignment mode.

Description

Technical Field

[0001] This application relates to the field of communication technologies, and in particular, to a communication method and a communication device. Background Art

[0002] Transmitters and receivers based on neural networks, such as encoders and decoders, can be trained by actual data driving, optimized according to scenarios, and achieve better transmit signal design and receive performance. Neural network transceivers can be used for physical layer signal processing, such as symbol modulation-demodulation, channel coding-decoding, pilot-channel estimation, etc.; they can also be used for data processing, such as channel state information compression-reconstruction.

[0003] The transmitter neural network and the receiver neural network can be jointly optimized to achieve the best performance. For example, between communication devices, the transmitters / receivers trained by one communication device can be adapted to the receivers / transmitters trained by the other communication device at the opposite end to achieve the best end-to-end performance. However, due to different capabilities and manufacturers of the two communication parties, the joint optimization efficiency between the two communication parties may be low, or joint optimization cannot be carried out. Summary of the Invention

[0004] This application provides a communication method and a communication device to improve the flexibility and efficiency of model alignment between two communication parties.

[0005] In a first aspect, a communication method is provided. This method is executed by a first communication device (the first communication device can be a terminal device or a network device), or, this method is executed by some components in the first communication device (such as a processor, a chip, or a chip system, etc.), or this method can also be implemented by a logic module or software that can implement all or part of the functions of the first communication device.

[0006] The method includes: The first communication device sends first information, and the first information indicates the model alignment mode supported by the first communication device. Or rather, the first information indicates the model alignment ability of the first communication device. The model alignment mode includes at least one of the following: model-based alignment mode, gradient-based alignment mode, and dataset-based alignment mode. The first communication device receives second information, and the second information indicates the target model alignment mode. The target model alignment mode is the model alignment mode supported by the second communication device, and the target alignment mode is one of the model alignment modes supported by the first communication device. Then, the first communication device performs model alignment based on the target model alignment mode. The first communication device and the second communication device exchange the model alignment modes they support, and then determine the target model alignment model supported by both the first communication device and the second communication device for model alignment, so that the model alignment mode can be flexibly selected according to the alignment ability of the communication device, and the model alignment efficiency and success rate can be improved.

[0007] In a possible implementation, the first communication device performs model alignment based on a target model alignment mode, including: the first communication device sends or receives target information corresponding to the target model alignment mode, and the target information is used to train the first model. Among them, the target alignment mode is a model-based alignment mode, and the target information includes a second model. The target alignment mode is a gradient-based alignment mode, and the target information includes gradients. The target alignment mode is a dataset-based alignment mode, and the target information includes a dataset. Under different model alignment modes, the data used to train the first model and the requirements for the communication device are different. The target model alignment mode is determined according to the capabilities of the first communication device and the second communication device, and training is performed based on the target model alignment mode, making model alignment more flexible.

[0008] In a possible implementation, the target alignment mode is a model-based alignment mode, and the target information includes a second model. Among them, the first model is the model of the first communication device, and the second model is the model of the second communication device; or, the first model is the model of the second communication device, and the second model is the model of the first communication device. When both communication parties support the model-based alignment mode, that is, when one of the communication parties can disclose its own model and the other party can parse and run the model sent by the other party, the target model alignment mode can be determined as the model-based alignment mode. The model-based alignment mode can perform offline model training, which can reduce the requirements for the computing power of the communication device, and the two communication parties do not need to interact information frequently, which can reduce the air interface overhead during model alignment.

[0009] In a possible implementation, the target alignment mode is a gradient-based alignment mode, and the target information includes gradients, and the gradients are obtained according to the output of the first model and the second model; the first model is a sending neural network model, and the second model is the receiving neural network model corresponding to the first model. Among them, the first model is the model of the first communication device, and the second model is the model of the second communication device; or, the first model is the model of the second communication device, and the second model is the model of the first communication device. When both communication parties support the gradient-based alignment mode, that is, when one of the communication parties can calculate the reverse gradient and the other party can parse the reverse gradient and update the model, the target model alignment mode can be determined as the gradient-based alignment mode. The model-based alignment mode can perform online model training, which can improve the model alignment efficiency. Moreover, model alignment can be completed without the two communication parties disclosing their own models, improving the flexibility of model alignment.

[0010] In a possible implementation, the target alignment mode is a dataset-based alignment mode, and the target information includes a dataset, where the dataset includes the input data and / or output data of the second model. Herein, the first model is the model of the first communication device, and the second model is the model of the second communication device; or, the first model is the model of the second communication device, and the second model is the model of the first communication device. When both communication parties support the dataset-based alignment mode, that is, when one of the communication parties can provide a dataset for training and the other party can use this dataset to train the model, it can be determined that the target model alignment mode is the dataset-based alignment mode. The dataset-based alignment mode can perform offline model training, which can reduce the requirements for the capabilities of the communication device. Moreover, model alignment can be completed without the communication parties disclosing their own models, improving the flexibility of model alignment.

[0011] In a possible implementation, performing model alignment based on the target model alignment mode further includes: the first communication device sending or receiving verification information, where the verification information is obtained according to the first model, and the verification information is used to verify whether the first model is aligned with the second model.

[0012] In a possible implementation, the method further includes: if the first model is aligned with the second model, the first communication device determines the association relationship between the first model and the second model. Thus, based on the association relationship between the first model and the second model, it is convenient to implement model selection / switching / activation / monitoring, etc. in subsequent processes, facilitating the application and management of the first model and the second model.

[0013] In a possible implementation, the first information further indicates the priority of the model alignment modes supported by the first communication device, and the target model alignment mode is the model alignment mode with the highest priority among the model alignment modes supported by the first communication device and the second communication device. Thus, when the candidate model alignment modes include multiple model alignment modes, the desired model alignment mode of the first communication device can be determined according to the priority, improving the flexibility and efficiency of model alignment.

[0014] In a possible implementation, the first information includes at least one of the following: a first bitmap, where the first bitmap indicates the model alignment modes supported by the first communication device; an index of the model alignment modes supported by the first communication device. The second information includes at least one of the following: a second bitmap, where the second bitmap indicates the target model alignment mode; an index of the target model alignment mode. Thus, the model alignment modes supported by the first communication device can be accurately indicated through the first information. The target model alignment mode determined by the second communication device can be accurately indicated through the second information, thereby improving the efficiency and success rate of model alignment.

[0015] The second aspect provides a communication method. This method is executed by a second communication device (the second communication device can be a terminal device or a network device), or, this method is executed by some components in the second communication device (such as a processor, a chip, or a chip system, etc.), or this method can also be implemented by a logic module or software that can implement all or part of the functions of the second communication device.

[0016] The method includes: The second communication device receives first information, and the first information indicates the model alignment mode supported by the first communication device. The second communication device sends second information, and the second information indicates the target model alignment mode. The target model alignment mode is the model alignment mode supported by the second communication device and is also the model alignment mode among the model alignment modes supported by the first communication device. The second communication device performs model alignment based on the target model alignment mode.

[0017] In a possible implementation, the model alignment mode includes at least one of the following: model-based alignment mode; gradient-based alignment mode; dataset-based alignment mode.

[0018] In a possible implementation, the second communication device performs model alignment based on the target model alignment mode, including: The second communication device sends or receives target information corresponding to the target model alignment mode, and the target information is used to train the first model.

[0019] In a possible implementation, the target alignment mode is the model-based alignment mode, and the target information includes the second model; where the first model is the model of the first communication device and the second model is the model of the second communication device; or, the first model is the model of the second communication device and the second model is the model of the first communication device.

[0020] In a possible implementation, the target alignment mode is the gradient-based alignment mode, and the target information includes the gradient, and the gradient is obtained according to the output of the first model and the second model; the first model is the sending neural network model, and the second model is the receiving neural network model corresponding to the first model; where the first model is the model of the first communication device and the second model is the model of the second communication device; or, the first model is the model of the second communication device and the second model is the model of the first communication device.

[0021] In a possible implementation, the target alignment mode is the dataset-based alignment mode, and the target information includes the dataset, and the dataset includes the input data and / or output data of the second model; where the first model is the model of the first communication device and the second model is the model of the second communication device; or, the first model is the model of the second communication device and the second model is the model of the first communication device.

[0022] In a possible implementation, the second communication device performs model alignment based on the target model alignment mode, and further includes: the second communication device sends or receives verification information, where the verification information is obtained according to the first model, and the verification information is used to verify whether the first model is aligned with the second model.

[0023] In a possible implementation, if the first model is aligned with the second model, the second communication device associates the first model and the second model.

[0024] In a possible implementation, the first information further indicates the priority of the model alignment modes supported by the first communication device, and the target model alignment mode is the model alignment mode with the highest priority among the model alignment modes supported by the first communication device and the second communication device.

[0025] In a possible implementation, the first information includes at least one of the following: a first bitmap, where the first bitmap indicates the model alignment modes supported by the first communication device; an index of the model alignment modes supported by the first communication device. The second information includes at least one of the following: a second bitmap, where the second bitmap indicates the target model alignment mode; an index of the target model alignment mode.

[0026] In a third aspect, a communication device is provided. The communication device has the functions of implementing the behaviors in the method example of the first aspect above, and the beneficial effects can be referred to the description of the first aspect and will not be elaborated here. The communication device may be the first communication device in the first aspect, or the communication device may be a device such as a chip or a chip system that can support the first communication device in the first aspect to implement the functions required by the method provided in the first aspect.

[0027] In a possible design, the communication device includes corresponding means or modules for performing the method of the first aspect. For example, the communication device includes a processing unit (sometimes also referred to as a processing module) and / or a transceiver unit (sometimes also referred to as a transceiver module). These units (modules) can perform the corresponding functions in the method example of the first aspect above. For specific details, refer to the detailed description in the method example and will not be elaborated here.

[0028] In a fourth aspect, an embodiment of the present application provides a communication device. The communication device has the functions of implementing the behaviors in the method example of the second aspect above, and the beneficial effects can be referred to the description of the second aspect and will not be elaborated here. The communication device may be the second communication device in the second aspect, or the communication device may be a device such as a chip or a chip system that can support the second communication device in the second aspect to implement the functions required by the method provided in the second aspect.

[0029] In a possible design, the communication device includes corresponding means or modules for performing the method of the second aspect. For example, the communication device includes a processing unit (sometimes also referred to as a processing module) and / or a transceiver unit (sometimes also referred to as a transceiver module). These units (modules) can perform the corresponding functions in the method examples of the second aspect above. For specific details, refer to the detailed description in the method examples and will not be elaborated here.

[0030] In a fifth aspect, an embodiment of the present application provides a communication device. The communication device can be the communication device in the third aspect or the fourth aspect in the above embodiments, or a chip or a chip system disposed in the communication device in the third aspect or the fourth aspect. The communication device includes a communication interface and a processor. Optionally, it further includes a memory. The memory is used to store computer programs or instructions or data. The processor is coupled to the memory and the communication interface. When the processor reads the computer programs or instructions or data, the communication device is caused to execute the methods performed by the terminal device or the network device in the above method embodiments.

[0031] In a sixth aspect, an embodiment of the present application provides a communication device. The communication device includes at least one processor. Optionally, it further includes a memory. The at least one processor is coupled to the memory. The at least one processor is used to execute the methods described in the first aspect or the second aspect.

[0032] In a seventh aspect, an embodiment of the present application provides a chip system. The chip system includes a processor and may further include a memory and / or a communication interface for implementing the methods described in the first aspect or the second aspect. In a possible implementation, the chip system further includes a memory for storing program instructions and / or data. The chip system can be composed of chips or can include chips and other discrete devices.

[0033] In an eighth aspect, an embodiment of the present application provides a communication system. The communication system includes a communication device for performing the method described in the first aspect and a communication device for performing the method described in the second aspect. Among them, the communication device for performing the method described in the first aspect is, for example, the first communication device described in the first aspect, and the communication device for performing the method described in the second aspect is, for example, the second communication device described in the second aspect.

[0034] In a ninth aspect, the present application provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is run, it implements the method in any one of the first aspect to the second aspect above.

[0035] In a tenth aspect, there is provided a computer program product, which includes computer program code that, when run, causes the method in any one of the first aspect to the second aspect to be executed.

[0036] Among them, for the technical effects brought by any one of the design manners in the second aspect to the tenth aspect, reference may be made to the technical effects brought by different design manners in the first aspect above, which will not be elaborated herein. Description of the Drawings

[0037] Figure 1a A schematic diagram of a communication system provided by this application;

[0038] Figure 1b A schematic diagram of another communication system provided by this application;

[0039] Figure 2 A schematic flowchart of a communication method provided by this application;

[0040] Figure 3a A schematic flowchart of a method for an interaction model alignment mode provided by this application;

[0041] Figure 3b A schematic flowchart of another method for an interaction model alignment mode provided by this application;

[0042] Figure 4 A schematic flowchart of another communication method provided by this application;

[0043] Figure 5 A schematic flowchart of another communication method provided by this application;

[0044] Figure 6 A schematic flowchart of another communication method provided by this application;

[0045] Figure 7 A schematic flowchart of another communication method provided by this application;

[0046] Figure 8 A schematic diagram of a communication device provided by this application;

[0047] Figure 9 A schematic diagram of another communication device provided by this application;

[0048] Figure 10 A schematic diagram of another communication device provided by this application;

[0049] Figure 11 A schematic diagram of another communication device provided by this application;

[0050] Figure 12Another schematic diagram of the communication device provided by this application. Detailed implementation manners

[0051] First, some terms in the embodiments of this application are explained to facilitate the understanding of those skilled in the art.

[0052] (1) Terminal device: It can be a wireless terminal device that can receive scheduling and indication information from a network device. The wireless terminal device can be a device that provides voice and / or data connectivity to users, or a handheld device with wireless connection capabilities, or other processing devices connected to a wireless modem.

[0053] The terminal device can communicate with one or more core networks or the Internet via a radio access network (RAN). The terminal device can be a mobile terminal device, such as a mobile phone (or a "cellular" phone, mobile phone), a computer, and a data card. For example, it can be a portable, pocket-sized, handheld, computer-integrated, or vehicle-mounted mobile device that exchanges voice and / or data with the wireless access network. For example, personal communication service (PCS) phones, cordless phones, session initiation protocol (SIP) phones, wireless local loop (WLL) stations, personal digital assistant (PDA), tablet (Pad), computers with wireless transceiver functions, etc. The wireless terminal device can also be referred to as a system, subscriber unit, subscriber station, mobile station, mobile station (MS), remote station, access point (AP), remote terminal device, access terminal device, user terminal device, user agent, subscriber station (SS), customer premises equipment (CPE), terminal, user equipment (UE), mobile terminal (MT), etc.

[0054] By way of example and not limitation, in the embodiments of the present application, the terminal device may also be a wearable device. Wearable devices, also known as wearable intelligent devices or smart wearable devices, etc., are the general term for devices developed by applying wearable technology to the intelligent design of daily wear, such as glasses, gloves, watches, clothing, and shoes. Wearable devices are portable devices that are worn directly on the body or integrated into the user's clothes or accessories. Wearable devices are not only a hardware device, but also achieve powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable intelligent devices include those with complete functions and large sizes that can achieve complete or partial functions without relying on a smartphone, such as smart watches or smart glasses, etc., and those that only focus on a certain type of application function and need to cooperate with other devices such as smartphones, such as various smart bracelets for vital sign monitoring, smart helmets, and smart jewelry.

[0055] The terminal may also be a drone, a robot, a terminal in device-to-device (D2D) communication, a terminal in vehicle-to-everything (V2X) communication, 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.

[0056] 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 the sixth-generation (6G) communication system, etc.) or a terminal device in a future evolved public land mobile network (PLMN). Exemplarily, the 6G network can further expand the form and function of 5G communication terminals, and 6G terminals include but are not limited to vehicles, cellular network terminals (integrating satellite terminal functions), drones, and Internet of Things (IoT) devices.

[0057] In the embodiments of the present application, the above terminal device may also be a device with an AI model, and can process the data to be sent or the received signal based on the AI model.

[0058] (2) Network device: It can be a device in a wireless network. For example, the network device can be a RAN node (or device) that connects a terminal device to a wireless network, and can also be called a base station. Currently, some examples of RAN devices 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, etc. Additionally, in a network structure, the network device can include a centralized unit (CU) node, or a distributed unit (DU) node, or a RAN device including a CU node and a DU node.

[0059] Optionally, the RAN node can also be a macro base station, a micro base station or an indoor station, a relay node or a donor node, or a radio controller in a cloud radio access network (CRAN) scenario. The RAN node can also be a server, a wearable device, a vehicle or an in-vehicle device, etc. For example, the access network device in vehicle to everything (V2X) technology can be a road side unit (RSU).

[0060] In another possible scenario, multiple RAN nodes cooperate to assist a terminal in achieving wireless access, and different RAN nodes respectively implement some functions of a base station. For example, the RAN nodes can be a central unit (CU), a distributed unit (DU), a CU-control plane (CP), a CU-user plane (UP), or a radio unit (RU), etc. The CU and the DU can be separately set, or can also be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or a radio frequency unit, such as included in a remote radio unit (RRU), an active antenna unit (AAU), or a remote radio head (RRH).

[0061] In different systems, the 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 RAN (O-RAN or ORAN) system, the CU can also be called an O-CU (open CU), the DU can also be called an O-DU, the CU-CP can also be called an O-CU-CP, the CU-UP can also be called an O-CU-UP, and the RU can also be called an O-RU. For the convenience of description, in this application, the CU, CU-CP, CU-UP, DU, and RU are used as examples for description. Any one of the 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.

[0062] 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: radio resource control (RRC) layer, packet data convergence protocol (PDCP) layer, radio link control (RLC) layer, media access control (MAC) layer, or physical (PHY) layer, etc. The user plane protocol layer may include at least one of the following: service data adaptation protocol (SDAP) layer, PDCP layer, RLC layer, MAC layer, or physical layer, etc.

[0063] For the correspondence between the network elements in the ORAN system and the protocol layer functions they can implement, refer to Table 1 below.

[0064] Table 1

[0065] ORAN Network Element Protocol Layer Functions of 3GPP O-CU-CP RRC+PCDP - Control Plane (PDCP-C) O-CU-UP SDAP+PCDP - User Plane (PDCP-U) O-DU RLC+MAC+PHY-high O-RU PHY-low

[0066] The network device may be other devices that provide wireless communication functions for the terminal device. The specific technologies and device forms adopted by the network device are not limited in the embodiments of the present application. For ease of description, the embodiments of the present application do not limit.

[0067] The network device may further include a core network device. For example, the core network device includes 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; 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 device may further include other core network devices in a 5G network and the next-generation network of the 5G network.

[0068] In an embodiment of this application, the foregoing network device may further be a network node with an AI model, and may process data to be sent or a received signal based on the AI model.

[0069] In an embodiment of this application, the apparatus for implementing the functions of the network device may be the network device or an apparatus capable of supporting the network device to implement the functions, such as a chip system. The apparatus may be installed in the network device. In the technical solution provided in the embodiment of this application, the apparatus for implementing the functions of the network device is the network device as an example to describe the technical solution provided in the embodiment of this application.

[0070] (3) The AI model, that is, the AI algorithm (or AI operator), is a general term for mathematical algorithms constructed based on the principles of artificial intelligence and is also the basis for using AI to solve specific problems. According to different specific methods and / or technologies for implementing artificial intelligence, the AI model may specifically be referred to as a machine learning model, a deep learning model, or a reinforcement learning model. Machine learning is a method for implementing artificial intelligence. The goal of this method is to design and analyze some algorithms (that is, models) that allow a computer to automatically "learn". The designed algorithms are called machine learning models. A machine learning model is a class of algorithms that automatically analyze and obtain rules from data and use the rules to predict unknown data.

[0071] The typical structure of current deep learning models is a deep neural network. A neural network is a mathematical model or computational model that mimics the structure and function of a biological neural network (the central nervous system of an animal, especially the brain). A neural network performs calculations through a large number of neuron connections. A neural network can include multiple neural network layers with different functions, and each layer includes parameters and calculation rules. Depending on the different calculation formulas or functions, different layers in a neural network have different names. For example, a layer that performs convolutional calculations is called a convolutional layer, and the convolutional layer is often used for feature extraction of input signals. A neural network can also be composed of a combination of multiple sub-neural networks. Neural networks with different structures can be applicable to different scenarios (e.g., classification, recognition) or provide different effects when used in the same scenario. The differences in the structure of a neural network specifically include one or more of the following: the number of network layers in the neural network is different, the order of each network layer is different, the weights, parameters, or calculation formulas in each network layer are different. There are already many different neural networks with high accuracy for application scenarios such as recognition or classification in the industry. Some neural networks can be trained with a specific dataset and used alone to complete a task or combined with other neural networks (or other functional modules) to complete a task.

[0072] (4) The terms "system" and "network" in the embodiments of the present application can be used interchangeably. "Multiple" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, or B exists alone, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one (item)" or its similar expression refers to any combination of these items, including any combination of single items (items) or multiple items (items). For example, "at least one of A, B, and C" includes A, B, C, AB, AC, BC, or ABC. Also, 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 do not limit the order, time sequence, priority, or importance of multiple objects.

[0073] (5) In the embodiments of the present application, "send" and "receive" represent the direction of signal transmission. For example, "send information to XX" can be understood as the destination of the information is XX, which can include direct transmission through the air interface or indirect transmission through other units or modules through the air interface. "Receive information from YY" can be understood as the source of the information is YY, which can include direct reception from YY through the air interface or indirect reception from YY through other units or modules through the air interface. "Send" can also be understood as the "output" of the chip interface, and "receive" can also be understood as the "input" of the chip interface.

[0074] In other words, the sending and receiving can be carried out between devices, for example, between a network device and a terminal device, or can be carried out within a device, for example, sending or receiving between components, modules, chips, software modules or hardware modules within a device through a bus, trace or interface.

[0075] It can be understood that the information may be subjected to necessary processing, such as encoding, modulation, etc. between the source end and the destination end of the information sending, but the destination end can understand the valid information from the source end. Similar expressions in this application can be understood similarly and will not be elaborated here.

[0076] (6) In the embodiments of this application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain piece of information (such as the indication information described below) is called the information to be indicated. Then, in the specific implementation process, there are many ways to indicate the information to be indicated. For example, but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated, etc. It is also possible to indirectly indicate the information to be indicated by indicating other information, where there is an association relationship between the other information and the information to be indicated; it is also possible to only indicate a part of the information to be indicated, while the other parts of the information to be indicated are known or pre-agreed. For example, the arrangement order of each piece of information pre-agreed (such as protocol pre-definition) can be used to realize the indication of specific information, thereby reducing the indication overhead to a certain extent. This application does not limit the specific manner of indication. It can be understood 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.

[0077] In this application, unless otherwise specified, the same or similar parts between various embodiments can be referred to each other. In various embodiments of this application, as well as in each method / design / implementation manner in each embodiment, if there is no special explanation and logical conflict, the terms and / or descriptions between different embodiments, as well as between each method / design / implementation manner in each embodiment, are consistent and can be referred to each other. The technical features in different embodiments, as well as in each method / design / implementation manner in each embodiment, can be combined to form new embodiments, methods, or implementation manners according to their internal logical relationships. The embodiments of this application described below do not constitute a limitation on the protection scope of this application.

[0078] This 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.). Among them, the communication system includes at least one network device and / or at least one terminal device.

[0079] Please refer to Figure 1a , which is a schematic diagram of a communication system provided by this application. Figure 1a Exemplarily, a network device and two terminal devices are shown. Figure 1a Taking the communication system as a cellular communication system and the network device as a base station as an example. The network device can communicate with each terminal device through wireless signals. The network device can send downlink signals to the terminal device, and the terminal device can send uplink signals to the network device. The terminal devices can also receive and send wireless signals through a sidelink (SL).

[0080] Please refer to Figure 1b , which is a schematic diagram of another communication system provided by this application. Figure 1b Exemplarily, a network device and one terminal device are shown. Figure 1b Taking the communication system as a wireless local area network (WLAN) system and the network device as a wireless access point (AP) as an example. The network device can communicate with each terminal device through wireless signals. The network device can send downlink signals to the terminal device, and the terminal device can send uplink signals to the network device.

[0081] In a wireless communication system (such as Figure 1a or Figure 1b the communication system shown), as a communication node that is a signal sender, the original data to be sent can go through multiple processing processes, including channel coding, modulation, etc.; correspondingly, as a communication node that is a signal receiver, the received signal can go through other processing processes corresponding to the multiple processing processes, including channel decoding, demodulation, etc., to recover the original data (or obtain an estimate of the original data). These processing processes can improve the reliability of data transmission.

[0082] After more than half a century of development, artificial intelligent (AI) technology has now fully entered the industrialization development stage. AI technology has penetrated into all fields and industries, including the wireless communication field. Specifically, in a wireless communication system, an AI model can be introduced into the transmitter and receiver of a device (network device or terminal device). For example, the transmitter of the device can perform processing such as channel coding and modulation on the data to be sent based on the AI model, and the receiver of the device can perform processing such as channel decoding and demodulation on the received signal based on the AI model, thereby improving system flexibility, spectrum efficiency, and system stability.

[0083] To improve the end-to-end performance of both communicating parties, the AI models in the transmitters and receivers of both communicating parties can be jointly optimized, that is, the transmitter / receiver trained by one party adapts to the receiver / transmitter trained by the other party. However, the devices in a communication system often come from different manufacturers, and the manufacturers may not be willing to disclose the models they have trained. Moreover, the capabilities of different devices vary, and these factors will all lead to low efficiency or inability to perform joint optimization between the two communicating parties.

[0084] In view of this, the present application provides the following embodiments to enable flexible model alignment between devices and improve the model optimization efficiency of both communicating parties.

[0085] In the embodiments of the present application, the communication system includes a first communication device and a second communication device. The first communication device sends the model alignment capability of the first communication device to the second communication device, and the second communication device determines the target model alignment mode for both parties to perform model alignment based on the alignment capability of the first communication device and its own alignment capability. Then, based on the target model alignment mode, information interaction is performed to align the model in the first communication device with the model in the second communication device.

[0086] In the present application, model alignment can also be referred to as model adaptation. Model alignment refers to training and adjusting the model of one or both of the two communicating parties based on an end-to-end loss function so that the models of the two communicating parties meet the end-to-end performance requirements. Alternatively, model alignment can also refer to using the model of one party as a reference to train the model of the other party to make their performances similar. The model in the present application refers to an AI model, which can also be referred to as a neural network model, a machine learning model, a deep learning model, a reinforcement learning model, etc. Specifically, the model can be, for example, a convolutional neural network (CNN), a recurrent neural network (RNN), a recursive neural network (RNN), a transformer, etc.

[0087] As Figure 2 shown, Figure 2 is a schematic flowchart of a communication method provided by the present application. In this embodiment, the first communication device can be a network device and the second communication device is a terminal device, or the first communication device can be a terminal device and the second communication device is a network device, or both the first communication device and the second communication device are terminal devices. This embodiment includes the following steps:

[0088] S201: The first communication device sends a first piece of information, and the first piece of information indicates the model alignment mode supported by the first communication device. Correspondingly, the second communication device receives the first piece of information.

[0089] Among them, the model alignment mode includes, for example, at least one of a model-based alignment mode, a gradient-based alignment mode, and a dataset-based alignment mode, etc. The first information indicates the model alignment mode supported by the first communication device, that is, the first information can indicate the alignment capability of the first communication device. Correspondingly, the model alignment mode supported by the first communication device may include at least one of a model-based alignment mode, a gradient-based alignment mode, and a dataset-based alignment mode, etc.

[0090] In the model-based alignment mode, the communication device can send its own model to other communication devices, or the communication device can receive a model from other communication devices and perform model alignment based on the received model.

[0091] In the gradient-based alignment mode, the communication device can calculate the reverse gradient, or can decode the reverse gradient from other communication devices and train the model based on the gradient.

[0092] In the dataset-based alignment mode, the communication device can provide sample data for the model of other communication devices to train, or can use the sample data from other communication devices for model training to align the models of both communication parties.

[0093] The first information can be carried in a radio resource control (RRC) message, sidelink control information (SCI), medium access control control element (MAC CE), downlink control information (DCI), or uplink control information (UCI), etc.

[0094] In a possible implementation, the first information includes, for example, a first bitmap. One bit in the first bitmap can correspond to a model alignment mode, and the value in the bit indicates whether the first communication device supports the corresponding model alignment mode. For example, when the value in the bit is 1, it indicates that the first communication device supports the corresponding model alignment mode; when the value in the bit is 0, it indicates that the first communication device does not support the corresponding model alignment mode. Alternatively, when the value in the bit is 0, it indicates that the first communication device supports the corresponding model alignment mode; when the value in the bit is 1, it indicates that the first communication device does not support the corresponding model alignment mode. Exemplarily, taking the first bitmap as 3 bits and the value of the bit being 1 indicating that the first communication device supports the corresponding model alignment mode as an example, the first bit in the first bitmap corresponds to the model-based alignment mode, the second bit corresponds to the gradient-based alignment mode, and the third bit corresponds to the dataset-based alignment mode. If the value of the first bitmap is 101, it indicates that the first communication device supports the model-based alignment mode and the dataset-based alignment mode, and does not support the gradient-based alignment mode.

[0095] In another possible implementation, each model alignment mode has a corresponding index, and the first information includes, for example, the indexes of the supported model alignment modes. Exemplarily, the index of the model-based alignment mode is 01, the index of the gradient-based alignment mode is 10, and the index of the dataset-based alignment mode is 11. If the first information includes the indexes 10 and 11, it indicates that the first communication device supports the gradient-based alignment mode and the dataset-based alignment mode. It can be understood that the indexes of the model alignment modes here are only examples and should not be construed as a limitation to this application.

[0096] In yet another possible implementation, the size of the first information can be 1 bit. For example, when the value of the first information is 1, it indicates that the first communication device supports all model alignment modes; when the value of the first information is 0, it indicates that the first communication device supports some model alignment modes. Alternatively, for example, when the value of the first information is 0, it indicates that the first communication device supports all model alignment modes; when the value of the first information is 1, it indicates that the first communication device supports some model alignment modes. Thus, the signaling overhead of the first information can be reduced.

[0097] Optionally, the first information further includes, for example, priority information corresponding to the model alignment modes supported by the first communication device, to indicate that when there are multiple candidate model alignment modes (model alignment modes supported by both the first communication device and the second communication device), the second communication device determines the model alignment mode with the highest priority as the target model alignment mode.

[0098] Optionally, when the first communication device supports the model-based alignment mode, the first information may further include, for example, the model description format of the model. The model description format includes, for example, data structures, as well as descriptions of tensors, network layers, and connection relationships. Only when the first communication device and the second communication device can process the same model description format can the model be accurately parsed and model alignment be performed.

[0099] Optionally, when the first communication device supports the gradient-based alignment mode, the first information may further include the computing power of the first communication device. The computing power is, for example, the computing resources of the first communication device, which may be the currently available computing resources of the first communication device, the total computing resources of the first communication device, or the computing resources currently used by the first communication device. The computing resources include, for example, at least one of XPU (central processing unit (CPU), graphics processing unit (GPU), neural processing unit (NPU), or tensor processing unit (TPU), etc.) resources, memory resources, or bandwidth resources. Since online model alignment is performed between the first communication device and the second communication device in the gradient-based alignment mode, the computing power requirements for the communication device are relatively high. Therefore, including the computing power of the first communication device in the first information can assist the second communication device in determining whether the first communication device has the ability to perform model alignment based on gradients.

[0100] S202: The second communication device sends second information to the first communication device, and the second information indicates the target model alignment mode. Correspondingly, the first communication device receives the second information.

[0101] The second communication device determines the target model alignment mode based on the first information and the capabilities of the second communication device itself, and sends second information indicating the target model alignment mode to the first communication device. The target model alignment mode is the model alignment mode supported by the second communication device and is also the model alignment mode among the model alignment modes supported by the first communication device. That is, the target model alignment mode is the alignment mode supported by both the first communication device and the second communication device.

[0102] In a possible implementation, the second information may include a second bitmap. One bit in the second bitmap may correspond to one model alignment mode, and the value in the bit indicates whether the first communication device supports the corresponding model alignment mode. For example, if the value of the bit is 0, it indicates that the corresponding model alignment mode is the target model alignment. Or, if the value of the bit is 1, it indicates that the corresponding model alignment mode is the target alignment mode.

[0103] In another possible implementation, each model alignment mode has a corresponding index, and the first information includes, for example, the index of the target model alignment mode.

[0104] Optionally, when there are multiple model alignment modes supported by both the first communication device and the second communication device, that is, when there are multiple candidate model alignment modes, if the first information includes the priority information of the model alignment mode, the second communication device determines the model alignment mode with the highest priority among the candidate model alignment modes as the target model alignment mode.

[0105] Optionally, when both the first communication device and the second communication device support the model-based alignment mode, and the first information includes the model description format of the model, if the model description format is the model description format supported by the second communication device, the second communication device may determine the model-based alignment mode as the target model alignment mode, or determine the model-based alignment mode as a candidate model alignment mode. If the model description format is not the model description format supported by the second communication device, the model-based alignment mode is not used as the target model alignment mode.

[0106] Optionally, when both the first communication device and the second communication device support the gradient-based alignment mode, and the first information includes the computing power of the first communication device, the second communication device may determine whether the computing power of the first communication device supports aligning the model in the gradient-based alignment mode according to the complexity of the model to be aligned (such as the number of parameters in the model, quantization accuracy, etc.). If the computing power of the first communication device supports aligning the model in the gradient-based alignment mode, the second communication device may determine the gradient-based alignment mode as the target model alignment mode, or determine the gradient-based alignment mode as a candidate model alignment mode. If the computing power of the first communication device does not support aligning the model in the gradient-based alignment mode, the gradient-based alignment mode is not used as the target model alignment mode.

[0107] Optionally, the second information may further include the parameter configuration of model training. For example, it may include at least one of quantization accuracy, dataset size, number of training rounds, information exchange period, etc. Among them, the information exchange period is, for example, the period of feedback gradient when the target model alignment mode is the gradient-based model alignment mode.

[0108] S203: The first communication device and the second communication device perform model alignment based on the target model alignment mode.

[0109] The first communication device and the second communication device interact with the target information corresponding to the target model alignment mode based on the target model alignment mode, and train the first model based on the target information to achieve the alignment of the first model and the second model.

[0110] In a possible implementation, the first model is the model of the first communication device, and the second model is the model of the second communication device. That is, the first communication device trains the first model, and the second communication device provides the target information corresponding to the target model alignment mode, and the target information is used for the training of the first model to align the first model and the second model of the second communication device. In this case, as Figure 3a shown, the first information may be used to request the second communication device to perform model alignment. The first information is, for example, the information in the model alignment request, or the first information is the model alignment request. Correspondingly, the second information is the information in the model alignment response corresponding to the model alignment request, or the second information is the model alignment response.

[0111] In another possible implementation, the first model is the model of the second communication device, and the second model is the model of the first communication device. That is, the second communication device trains the first model, and the first communication device provides the target information corresponding to the target model alignment mode, and the target information is used for the training of the first model to align the second model and the first model of the first communication device. In this case, as Figure 3b shown, the first information is used to send the model alignment capability of the first communication device. The first information is carried, for example, in a broadcast message, or the first information may also be carried in a unicast message. The second information is used to request the first communication device to perform model alignment. The second information is, for example, the information in the model alignment request, or the second information is the model alignment request. Optionally, after receiving the second information, the first communication device may also send a model alignment response to the second communication device to confirm the model alignment based on the target model alignment mode.

[0112] The first model may be a transmitting model, and the second model may be a receiving model. Or, the first model may be a receiving model, and the second model may be a transmitting model. Or, both the first model and the second model are transmitting models. Or, both the first model and the second model are receiving models.

[0113] The transmitting model refers to the AI model in the transmitter for processing such as information compression, channel coding, or modulation. The receiving model refers to the AI model in the receiver for processing such as information decompression, channel decoding, or demodulation. Since both the first communication device and the second communication device include a transmitter and a receiver, the first model may be the model in the first communication device or the model in the second communication device, and the first model may be a transmitting model or a receiving model.

[0114] The first model and the second model are related models. The second model is used to perform inverse processing on the data (signal) output by the first model, or the first model is used to perform inverse processing on the data (signal) output by the second model. Or the first model and the second model are models for implementing the same function. For example, when the first model is an AI model for information compression, the second model can be an AI model for information compression, or the second model can be an AI model for information decompression. Also for example, when the first model is an AI model for channel coding, the second model can be an AI model for channel decoding, or the second model can be an AI model for channel decoding. Also for example, when the first model is an AI model for demodulation, the second model can be an AI model for modulation, or the second model can be an AI model for demodulation, etc., and no further examples are given here.

[0115] Under different model alignment modes, and whether the first model is a sending model or a receiving model, the process of model alignment between the first communication device and the second communication device is different.

[0116] Case 1: The first model is a sending model, and the target model alignment mode is the model-based alignment mode.

[0117] Case 2: The first model is a sending model, and the target model alignment mode is the dataset-based alignment mode.

[0118] Case 3: The first model is a sending model, and the target model alignment mode is the gradient-based alignment mode.

[0119] Case 4: The first model is a receiving model, and the target model alignment mode is the model-based alignment mode.

[0120] Case 5: The first model is a receiving model, and the target model alignment mode is the dataset-based alignment mode.

[0121] When the first model is a receiving model, the target model alignment mode does not include the gradient-based alignment mode. This is because the end-to-end performance verification of the model is implemented on the receiving model side, that is, the loss function and gradients, etc., are implemented on the receiving model side, so there is no need for the sending model side to provide gradients.

[0122] The processes of model alignment between the first communication device and the second communication device in the above Cases 1 to 5 are described below.

[0123] For Case 1 above, as Figure 4 shown, Figure 41 is a flow chart of another communication method provided by the present application. This embodiment is implemented by communication device A and communication device B. The first model is a model in communication device A, and the second model is a model in communication device B. It should be noted that communication device A can be Figure 2 The first communication device in, and the communication device B is Figure 2 Alternatively, the communication device A may be Figure 2 The second communication device in, and the communication device B is Figure 2 The first communication device in this embodiment comprises the following steps:

[0124] S401: Communication device A sends a model alignment request to communication device B. Correspondingly, communication device B receives the model alignment request.

[0125] S402: Communication device B sends a model alignment response to communication device A. Correspondingly, communication device A receives the model alignment response.

[0126] In a possible implementation, the communication device A and the communication device B may be Figure 3a The interactive process shown determines that the target model alignment mode is a model-based alignment mode. For example, the model alignment request sent by communication device A to communication device B includes first information, indicating the model alignment mode supported by communication device A. The model alignment response sent by communication device B to communication device A includes second information, indicating the target model alignment mode determined by communication device B, and the target model alignment mode is a model-based alignment mode.

[0127] In another possible implementation, the communication device A and the communication device B may be Figure 3b The interactive process shown determines that the target model alignment mode is a model-based alignment mode. For example, before S401, communication device B sends first information indicating the model alignment capability of communication device B to communication device A. Communication device A determines that the target model alignment mode is a model-based alignment mode based on the communication device B and the model alignment capability, as well as the model alignment capability of communication device A itself. Then communication device A sends a model alignment request to communication device B, and the model alignment request includes second information, that is, the model alignment request indicates the target model alignment mode. Then communication device B sends a model alignment response to communication device A to confirm the model alignment request of communication device A.

[0128] S403: Communication device B sends the second model to communication device A. Correspondingly, communication device A receives the second model.

[0129] In this embodiment, the target information corresponding to the target model alignment mode is the second model.

[0130] Since the capabilities of different communication devices are different, some communication devices may not support all structural types of AI models. If the model structure type of the model sent by communication device B to communication device A is a model structure type not supported by communication device A, communication device A cannot train based on this model. Optionally, in order to avoid wasting air interface resources by transmitting invalid models and improve model alignment efficiency, before S403, communication device A can also send third information to communication device B. The third information indicates one or more model structure types supported by communication device A (hereinafter referred to as candidate model structure types). The candidate model structure types include, for example, at least one of CNN, RNN, and transformer, etc. Communication device B then determines the second model according to the candidate model structure types indicated by communication device A in the third information, that is, the model structure type of the second model is the structure type among the candidate model structure types indicated by communication device A.

[0131] In another implementation, the model structure types supported by communication device A can also be included in the first information, thereby reducing signaling overhead.

[0132] S404: Communication device A trains the first model based on the second model.

[0133] Communication device A trains the first model based on the second model to align the first model and the second model. Communication device A can train the first model based on local training sample data and the second model.

[0134] When the first model is the sending model and the second model is the receiving model, communication device A inputs the local training sample data as input data into the first model to obtain the first output data of the first model, and then inputs the first output data into the second model to obtain the second output data output by the second model. Communication device A calculates the first loss value based on the input data and the second output data. The first loss value is the end-to-end loss. Communication device A calculates the gradient based on the first loss value, and then optimizes the first model based on the gradient.

[0135] When the first model is a sending model and the second model is also a sending model, communication device A inputs the local training sample data as input data into the first model to obtain the first output data of the first model, and inputs the local training sample data as input data into the second model to obtain the third output data of the second model. Communication device A calculates a second loss value based on the first output data and the third output data. The communication device calculates a gradient based on the second loss value, and then optimizes the first model based on the gradient. Thus, the performance of the first model and the second model is made close or consistent. Alternatively, communication device A inputs the local training sample data into the second model to obtain the third output data of the second model, and then communication device A uses the third output data as training samples to train the first model so that the performance of the first model and the second model is close or consistent.

[0136] Optionally, after training the first model for a preset number of rounds or when the first model converges, communication device A may request communication device B to verify whether the first model is aligned with the second model. After S404, it may further include:

[0137] S405: Communication device A sends verification information of the first model to communication device B. Correspondingly, communication device B receives the verification information.

[0138] In a possible implementation, the verification information may include model performance information. The model performance information may include, for example, accuracy, precision, or recall, etc. When the accuracy / precision / recall, etc. is greater than the model performance threshold, it can be considered that the first model is aligned with the second model, otherwise it is considered that the first model and the second model are still not aligned. Alternatively, the verification information may include the input data and output data of the first model, and communication device B verifies whether the end-to-end loss of the first model and the second model is less than the loss threshold. If it is less than the loss threshold, it can be considered that the first model is aligned with the second model, otherwise it is considered that the first model and the second model are still not aligned.

[0139] S406: Communication device B sends a verification result to communication device A. Correspondingly, communication device A receives the verification result.

[0140] After determining the verification result based on the verification information, communication device B sends the verification result to communication device A. The verification result indicates that the first model is aligned with the second model, or the first model and the second model are not aligned. Thus, when the verification result indicates that the first model and the second model are not aligned, communication device A continues to train the first model so that the first model is aligned with the second model.

[0141] When the first model is aligned with the second model, optionally, S407 may also be executed.

[0142] S407: Communication device A and communication device B associate the first model and the second model.

[0143] After the first model and the second model are aligned, the communication device A and the communication device B can interact to associate the first model and the second model.

[0144] For example, the communication device A can configure a first identifier for the first model and a second identifier for the second model. The communication device A associates the first identifier and the second identifier to associate the first model and the second model, and sends the second identifier to the communication device B. The communication device B uses the second identifier as the identifier of the second model. Among them, the first identifier and the second identifier can be the same or different. The first identifier of the first model can be configured by the communication device A before the first model and the second model are aligned, or can be configured by the communication device A after the first model and the second model are aligned. The communication device A can determine the first identifier of the first model according to the reception time of the model alignment response. Of course, the communication device A can also configure an identifier for the first model according to other principles. For example, a sequence can be randomly generated as the identifier of the first model, or the order number of the aligned models can be used as the identifier of the first model, which is not limited here.

[0145] Also for example, the communication device A can use the session identifier of the model alignment request or the reception time of the model alignment response as a third identifier to associate the first model and the second model. The communication device A associates the third identifier with the first model and sends the third identifier to the communication device B. The communication device B associates the third identifier with the second model.

[0146] Also for example, the communication device B can configure a first identifier for the first model and a second identifier for the second model. The communication device B associates the first identifier and the second identifier to associate the first model and the second model, and sends the first identifier to the communication device A. The communication device A uses the first identifier as the identifier of the first model. Among them, the first identifier and the second identifier can be the same or different. The second identifier of the second model can be configured by the communication device B before the first model and the second model are aligned, or can be configured by the communication device B after the first model and the second model are aligned. The communication device B, for example, uses the time when the model alignment request is received as the identifier of the second model. Of course, the communication device B can also assign an identifier to the second model according to other principles. For example, a sequence can be randomly generated as the identifier of the second model, or the order number of the aligned models can be used as the identifier of the second model, which is not limited here.

[0147] The first identifier is associated with the second identifier, or the third identifier is associated with the first model and the second model. This association relationship can be used for model selection / switching / activation / monitoring, etc. in subsequent processes. For example, when the first model is used to process data to be sent subsequently, communication device A determines the second identifier or the third identifier of the second model associated with the first model according to the association relationship. Communication device A can indicate the second identifier or the third identifier to communication device B, so as to instruct communication device B to process the received signal according to the second model corresponding to the second identifier or the third identifier, thereby ensuring end-to-end performance. Or, when communication device B uses the second model to process data to be sent, communication device B can indicate the second identifier or the third identifier to communication device A, so as to instruct communication device A to determine the first model according to the second identifier or the third identifier and the association relationship, and use the first model to process the signal from communication device B.

[0148] In this embodiment, when both communication parties support the model-based alignment mode, the model-based alignment mode can be determined for alignment. In the model-based alignment mode, communication device A can perform offline training on the first model. Communication device A can calculate gradients based on the second model, etc. During the training process, there is no need to frequently interact with communication device B, which can improve the training efficiency and reduce the air interface resources occupied during the model alignment process.

[0149] For the above-mentioned second case, as Figure 5 shown, Figure 5 is a schematic flowchart of another communication method provided by this application. This embodiment is implemented by communication device A and communication device B. The first model is the model in communication device A, and the second model is the model in communication device B. It should be noted that communication device A can be the Figure 2 first communication device in, and communication device B is the Figure 2 second communication device in. Or, communication device A can be the Figure 2 second communication device in, and communication device B is the Figure 2 first communication device in. This embodiment includes the following steps:

[0150] S501: Communication device A sends a model alignment request to communication device B. Correspondingly, communication device B receives the model alignment request.

[0151] S502: Communication device B sends a model alignment response to communication device A. Correspondingly, communication device A receives the model alignment response.

[0152] S503: Communication device B sends a data set to communication device A. Correspondingly, communication device A receives the data set.

[0153] In this embodiment, the target information corresponding to the target model alignment mode is a data set.

[0154] When both the first model and the second model are sending models, the dataset may include the input data and output data of the second model.

[0155] When the first model is a sending model and the second model is a receiving model, before S503, communication device A may send initial sample data to communication device B. Communication device B obtains a dataset according to the initial sample data and a third model. The third model is the sending model corresponding to the second model in communication device B. The dataset includes the output data of the third model.

[0156] S504: Communication device A trains the first model based on the dataset.

[0157] Communication device A trains the first model based on the data in the dataset to align the first model and the second model.

[0158] Communication device A inputs the input data in the dataset into the first model to obtain a fourth output data, calculates the loss value between the fourth output data and the output data in the dataset, and optimizes the first model according to the loss value.

[0159] S505: Communication device A sends verification information of the first model to communication device B.

[0160] In this embodiment, the verification information may include, for example, the input data and output data of the first model. The input data of the first model is, for example, the sample data in the verification set.

[0161] Communication device B uses the output data in the verification information as the input data of the second model to obtain a fifth output data output by the second model. Communication device B calculates the loss value between the fifth output data and the input data in the verification information, and determines whether the first model and the second model are aligned according to the loss value. This loss value is an end-to-end loss value. When this loss value is less than the end-to-end loss threshold, it can be determined that the second model is aligned with the first model, otherwise they are not aligned.

[0162] S506: Communication device B sends a verification result to communication device A. Correspondingly, communication device A receives the verification result.

[0163] S507: Communication device A and communication device B associate the first model and the second model.

[0164] In this embodiment, when both communication parties support the alignment mode based on the dataset, the model alignment can be performed based on the alignment mode of the dataset. Based on the alignment mode of the model, communication device A can perform offline training on the first model, which can reduce the requirements for the capabilities of communication device A. And during the model alignment process, the model alignment can be completed without disclosing the models of the communication devices.

[0165] For the above-mentioned third scenario, as Figure 6 shown, Figure 6 is a schematic flowchart of another communication method provided by this application. This embodiment is implemented by communication device A and communication device B. The first model is the model in communication device A, and the second model is the model in communication device B. It should be noted that communication device A can be the Figure 2 first communication device in, and communication device B is the Figure 2 second communication device in. Or, communication device A can be the Figure 2 second communication device in, and communication device B is the Figure 2 first communication device in. This embodiment includes the following steps:

[0166] S601: Communication device A sends a model alignment request to communication device B. Correspondingly, communication device B receives the model alignment request.

[0167] S602: Communication device B sends a model alignment response to communication device A. Correspondingly, communication device A receives the model alignment response.

[0168] S603: Communication device A sends the output data of the first model to communication device B. Correspondingly, communication device B receives the output data.

[0169] Communication device A inputs the training sample data into the first model to obtain the output data of the first model. Communication device A can send the output data of the first model in this round of training to communication device B when performing one round of training. Thus, communication device B calculates the reverse gradient (sometimes simply referred to as gradient in this application) based on the output data of the first model in this round of training.

[0170] S604: Communication device B sends the gradient to communication device A. Correspondingly, communication device A receives the gradient.

[0171] In this embodiment, the target information corresponding to the target model alignment mode is the gradient.

[0172] S605: Communication device A trains the first model based on the gradient.

[0173] Communication device A trains the first model based on the gradient to align the first model and the second model. The gradient refers to the rate of change of the loss function at a certain point, and its direction points to the direction where the function value changes the most. Update the parameters in the first model based on the gradient, for example, use the gradient descent algorithm to update the parameters in the first model to minimize the value of the loss function.

[0174] S606: Communication device A sends the verification information of the first model to communication device B.

[0175] In this embodiment, the verification information includes, for example, the input data and output data of the first model. The input data of the first model is, for example, the sample data in the verification set. When the sample data in the verification set is included in communication device B, the verification information may not include the input data of the first model.

[0176] Communication device B uses the output data in the verification information as the input data of the second model to obtain the fifth output data output by the second model. Communication device B calculates the loss value between the fifth output data and the input data in the verification information, and determines whether the first model and the second model are aligned according to this loss value. This loss value is an end-to-end loss value. When this loss value is less than the end-to-end loss threshold, it can be determined that the second model is aligned with the first model; otherwise, they are not aligned.

[0177] S607: Communication device B sends the verification result to communication device A. Correspondingly, communication device A receives this verification result.

[0178] S608: Communication device A and communication device B associate the first model and the second model.

[0179] In this embodiment, when both communication parties support the gradient-based alignment mode, the model alignment can be performed based on the gradient-based alignment mode. Moreover, the model alignment process can be completed without disclosing the models of the communication devices.

[0180] In the embodiments of the present application,

[0181] For the above-mentioned situation 4 and situation 5, as Figure 7 shown, Figure 7 is a schematic flowchart of another communication method provided by the present application. This embodiment is implemented by communication device A and communication device B. The first model is the model in communication device A, and the second model is the model in communication device B. It should be noted that communication device A may be Figure 2 the first communication device in Figure 2 , and communication device B is Figure 2 the second communication device in Figure 2 . Or, communication device A may be Figure 2 the second communication device in Figure 2 , and communication device B is Figure 2 the first communication device in Figure 2 . This embodiment includes the following steps:

[0182] S701: Communication device A sends a model alignment request to communication device B. Correspondingly, communication device B receives the model alignment request.

[0183] S702: Communication device B sends a model alignment response to communication device A. Correspondingly, communication device A receives the model alignment response.

[0184] S703: Communication device B sends target information corresponding to the target model alignment mode to communication device A. Correspondingly, communication device A receives the target information. The target information includes a second model or a dataset.

[0185] S704: Communication device A trains the first model based on the second model / dataset.

[0186] When the target model alignment mode is a model-based alignment mode, the target information includes a second model. Communication device A trains the first model based on the second model to align the first model and the second model. Communication device A can train the first model based on local training sample data and the second model.

[0187] When the first model is a receiving model and the second model is a sending model, communication device A takes the local training sample data as input data and inputs it into the second model to obtain the output data of the second model, and then inputs the output data of the second model into the first model to obtain the output data of the first model. Communication device A calculates a first loss value based on the input data of the second model and the output data of the first model. This first loss value is an end-to-end loss. Communication device A calculates a gradient based on this first loss value, and then optimizes the first model based on this gradient.

[0188] When the first model is a receiving model and the second model is also a receiving model, communication device A takes the local training sample data as input data and inputs it into the second model to obtain the output data of the second model, and takes the local training sample data as input data and inputs it into the first model to obtain the output data of the first model. Communication device A calculates a loss value based on the output data of the second model and the output data of the first model. The communication device calculates a gradient based on this loss value, and then optimizes the first model based on this gradient. Thus, the performance of the first model and the second model is made close or consistent.

[0189] When the target model alignment mode is a dataset-based alignment mode, the target information includes a dataset. The dataset may include the input data and output data of the second model.

[0190] After the first model is trained for a preset number of rounds, or after the first model converges, communication device A can verify whether the first model is aligned with the second model. Optionally, when the target model alignment mode is a dataset-based alignment mode, communication device A can request communication device B to send verification information. This verification information may include the input data and output data of the second model based on the sample data in the verification set, so that communication device A can verify whether the first model is aligned with the second model based on the verification information. Optionally, after the verification passes, that is, after it is confirmed that the first model is aligned with the second model, communication device A can execute S705.

[0191] S705: The communication device A sends the verification result to the communication device B. Correspondingly, the communication device B receives the verification result.

[0192] The verification result indicates that the first model is aligned with the second model. When the first model is aligned with the second model, optionally, S706 can also be executed.

[0193] Of course, when the communication device A verifies that the first model and the second model are not aligned, it can also send a verification result indicating that the first model and the second model are not aligned to the communication device B.

[0194] S706: The communication device A and the communication device B associate the first model and the second model.

[0195] This step is of the same type as S407. For specific details, please refer to the relevant description of S407, so it will not be elaborated here.

[0196] In this embodiment, when both the communication device A and the communication device B support the model-based alignment mode, the alignment of the first model and the second model can be performed based on the model-based alignment model. And when the first model is a received model, the training and verification of the first model can be performed based on the local training sample data and verification sample data, which can improve the model alignment efficiency and reduce the overhead during the alignment process without frequent interaction with the communication device B.

[0197] In the above method embodiments of the present application, before the communication parties perform model alignment, one of the communication parties notifies the other party of its own model alignment ability, so that the model alignment mode supported by both communication parties can be selected for model alignment, enabling flexible decision-making on the target model alignment mode between communication devices with different capabilities, states, or requirements, and improving the model alignment efficiency and success rate. Further, after the first model and the second model are aligned, the first model and the second model can be associated to facilitate the selection / switching / activation / monitoring, etc. of the model based on the association relationship between the models in the subsequent process, and facilitate the use and management of the aligned models.

[0198] Please refer to Figure 8 , the embodiment of the present application provides a communication device 800, which can implement the functions of the second communication device or the first communication device in the above method embodiments, and thus can also achieve the beneficial effects of the above method embodiments. In the embodiment of the present application, the communication device 800 can be the first communication device (or the second communication device), or an integrated circuit or component inside the second communication device (or the first communication device), such as a chip.

[0199] It should be noted that the transceiver unit 802 may include a sending unit and a receiving unit, which are respectively used to perform sending and receiving.

[0200] In a possible implementation, when the device 800 is used to execute the method performed by the first communication device in the foregoing embodiment, the device 800 includes a processing unit 801 and a transceiver unit 802; the transceiver unit 802 is used to send a first piece of information, and the first piece of information indicates the model alignment mode supported by the first communication device. The transceiver unit 802 is used to receive a second piece of information, and the second piece of information indicates a target model alignment mode. The target model alignment mode is the model alignment mode supported by the second communication device, and the target alignment mode is the model alignment mode among the model alignment modes supported by the first communication device. The processing unit 801 is used to perform model alignment based on the target model alignment mode.

[0201] In a possible implementation, the model alignment mode includes at least one of the following: model-based alignment mode; gradient-based alignment mode; dataset-based alignment mode.

[0202] In a possible implementation, the transceiver unit 802 is used to send or receive target information corresponding to the target model alignment mode, and the target information is used to train the first model.

[0203] In a possible implementation, the target alignment mode is a model-based alignment mode, and the target information includes a second model;

[0204] Wherein, the first model is the model of the first communication device, and the second model is the model of the second communication device; or, the first model is the model of the second communication device, and the second model is the model of the first communication device.

[0205] In a possible implementation, the target alignment mode is a gradient-based alignment mode, and the target information includes a gradient, and the gradient is obtained according to the output of the first model and the second model; the first model is a sending neural network model, and the second model is the receiving neural network model corresponding to the first model;

[0206] Wherein, the first model is the model of the first communication device, and the second model is the model of the second communication device; or, the first model is the model of the second communication device, and the second model is the model of the first communication device.

[0207] In a possible implementation, the target alignment mode is a dataset-based alignment mode, and the target information includes a dataset, and the dataset includes the input data and / or output data of the second model;

[0208] Wherein, the first model is the model of the first communication device, and the second model is the model of the second communication device; or, the first model is the model of the second communication device, and the second model is the model of the first communication device.

[0209] In a possible implementation manner, based on the target model alignment mode for model alignment, it further includes:

[0210] The first communication device sends or receives verification information, the verification information is obtained according to the first model, and the verification information is used to verify whether the first model is aligned with the second model.

[0211] In a possible implementation manner, the processing unit 801 is used to associate the first model and the second model when it is determined that the first model is aligned with the second model.

[0212] In a possible implementation manner, the first information further indicates the priority of the model alignment mode supported by the first communication device, and the target model alignment mode is the model alignment mode with the highest priority among the model alignment modes supported by the first communication device and the second communication device.

[0213] In a possible implementation manner, the first information includes at least one of the following: a first bitmap, the first bitmap indicating the model alignment mode supported by the first communication device; an index of the model alignment mode supported by the first communication device.

[0214] In a possible implementation manner, the second information includes at least one of the following: a second bitmap, the second bitmap indicating the target model alignment mode; an index of the target model alignment mode.

[0215] In a possible implementation manner, when the device 800 is used to execute the method performed by the second communication device in the foregoing embodiments, the device 800 includes a processing unit 801 and a transceiver unit 802; the transceiver unit 802 is used to receive the first information, and the first information indicates the model alignment mode supported by the first communication device. The transceiver unit 802 is used to send the second information, and the second information indicates the target model alignment mode, the target model alignment mode is the model alignment mode supported by the second communication device, and the target alignment mode is the model alignment mode among the model alignment modes supported by the first communication device. The processing unit 801 is used to perform model alignment based on the target model alignment mode.

[0216] In a possible implementation manner, the model alignment mode includes at least one of the following: a model-based alignment mode; a gradient-based alignment mode; a dataset-based alignment mode.

[0217] In a possible implementation, the second communication device performs model alignment based on a target model alignment mode, including: the second communication device sends or receives target information corresponding to the target model alignment mode, and the target information is used to train the first model.

[0218] In a possible implementation, the target alignment mode is a model-based alignment mode, and the target information includes a second model; wherein, the first model is the model of the first communication device, and the second model is the model of the second communication device; or, the first model is the model of the second communication device, and the second model is the model of the first communication device.

[0219] In a possible implementation, the target alignment mode is a gradient-based alignment mode, and the target information includes a gradient, and the gradient is obtained according to the output of the first model and the second model; the first model is a transmitting neural network model, and the second model is a receiving neural network model corresponding to the first model;

[0220] wherein, the first model is the model of the first communication device, and the second model is the model of the second communication device; or, the first model is the model of the second communication device, and the second model is the model of the first communication device.

[0221] In a possible implementation, the target alignment mode is a dataset-based alignment mode, and the target information includes a dataset, and the dataset includes the input data and / or output data of the second model;

[0222] wherein, the first model is the model of the first communication device, and the second model is the model of the second communication device; or, the first model is the model of the second communication device, and the second model is the model of the first communication device.

[0223] In a possible implementation, the transceiver unit 802 is further configured to send or receive verification information, and the verification information is obtained according to the first model, and the verification information is used to verify whether the first model is aligned with the second model.

[0224] In a possible implementation, the processing unit 801 is configured to associate the first model and the second model when the first model is aligned with the second model.

[0225] In a possible implementation, the first information further indicates the priority of the model alignment mode supported by the first communication device, and the target model alignment mode is the model alignment mode with the highest priority among the model alignment modes supported by the first communication device and the second communication device.

[0226] In a possible implementation, the first information includes at least one of the following: a first bitmap, and the first bitmap indicates the model alignment modes supported by the first communication device; an index of the model alignment modes supported by the first communication device.

[0227] In a possible implementation, the second information includes at least one of the following: a second bitmap, where the second bitmap indicates the alignment mode of the target model; an index of the alignment mode of the target model.

[0228] It should be noted that for the information execution process and the like of the units of the above communication device 800, specific details can be found in the descriptions in the method embodiments shown above in this application, and will not be elaborated here.

[0229] Please refer to Figure 9 , which is another schematic structural diagram of the communication device 900 provided by this application. The communication device 900 includes a logic circuit 901 and an input / output interface 902. Among them, the communication device 900 can be a chip or an integrated circuit.

[0230] Among them, Figure 8 The shown transceiver unit 802 can be a communication interface, and this communication interface can be Figure 9 the input / output interface 902 in

[0231] the input / output interface 902 can include an input interface and an output interface. Alternatively, this communication interface can also be a transceiver circuit, and the transceiver circuit can include an input interface circuit and an output interface circuit.

[0232] Optionally, the logic circuit 901 is used to determine first information, where the first information indicates the model alignment mode supported by the first communication device; the input / output interface 902 is used to send the first information.

[0233] Among them, the logic circuit 901 and the input / output interface 902 can also execute other steps performed by the first communication device or the second communication device in any embodiment and achieve the corresponding beneficial effects, which will not be elaborated here.

[0234] In a possible implementation, Figure 8 the shown processing unit 801 can be Figure 9 the logic circuit 901 in

[0235] Optionally, the logic circuit 901 can be a processing device, and the functions of the processing device can be implemented partially or entirely by software. Among them, the functions of the processing device can be implemented partially or entirely by software.

[0236] Optionally, the processing device may include a memory and a processor. The memory is used to store a computer program, and the processor reads and executes the computer program stored in the memory to perform the corresponding processing and / or steps in any one of the method embodiments.

[0237] Optionally, the processing device may only include a processor. The memory for storing the computer program is located outside the processing device, and the processor is connected to the memory through a circuit / wire to read and execute the computer program stored in the memory. The memory and the processor may be integrated together or physically independent of each other.

[0238] 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 processing circuits (DSPs), microcontroller units (MCUs), programmable logic devices (PLDs), or other integrated chips, or any combination of the above chips or processors, etc.

[0239] Please refer to Figure 10 the communication device 1000 involved in the above embodiments provided for the embodiments of the present application. The communication device 1000 may specifically be the communication device serving as a terminal device in the above embodiments. Figure 10 The example shown is implemented by the terminal device (or a component in the terminal device).

[0240] Among them, a possible schematic diagram of the logical structure of the communication device 1000. The communication device 1000 may include but is not limited to at least one processor 1001 and a communication port 1002.

[0241] Among them, Figure 8 the shown transceiver unit 802 may be a communication interface, and this communication interface may be Figure 10 the communication port 1002 in, and the communication port 1002 may include an input interface and an output interface. Alternatively, the communication port 1002 may also be a transceiver circuit, and the transceiver circuit may include an input interface circuit and an output interface circuit.

[0242] Further optionally, the apparatus may further include at least one of a memory 1003 and a bus 1004. In an embodiment of the present application, the at least one processor 1001 is configured to control and process the operations of the communication apparatus 1000.

[0243] In addition, the processor 1001 may 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, transistor logic device, hardware component, or any combination thereof. It may implement or execute various exemplary logical blocks, modules, and circuits described in connection with the disclosure of the present application. The processor may 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 can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, apparatuses, and units described above may refer to the corresponding processes in the foregoing method embodiments, and will not be described in detail herein.

[0244] It should be noted that Figure 10 the illustrated communication apparatus 1000 may specifically be configured to implement the steps implemented by the terminal device in the foregoing method embodiments, and achieve the technical effects corresponding to the terminal device. Figure 10 For the specific implementation manners of the illustrated communication apparatus, reference may be made to the descriptions in the foregoing method embodiments, and details will not be repeated herein.

[0245] Please refer to Figure 11 , which is a schematic structural diagram of the communication apparatus 1100 involved in the foregoing embodiments provided in the embodiments of the present application. The communication apparatus 1100 may specifically be the communication apparatus acting as a network device in the foregoing embodiments. Figure 11 The illustrated example is implemented by a network device (or a component in the network device). Among them, the structure of the communication apparatus may refer to Figure 11 the structure shown.

[0246] The communication device 1100 includes at least one processor 1111 and at least one network interface 1114. Further optionally, the communication device further includes at least one memory 1112, at least one transceiver 1113, and one or more antennas 1115. The processor 1111, the memory 1112, the transceiver 1113, and the network interface 1114 are connected, for example, connected by a bus. In the embodiments of the present application, this connection may include various interfaces, transmission lines, or buses, etc., and this embodiment does not limit this. The antenna 1115 is connected to the transceiver 1113. The network interface 1114 is used to enable the communication device to communicate with other communication devices through a communication link. For example, the network interface 1114 may include a network interface between the communication device and a core network device, such as an S1 interface. 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.

[0247] Among them, Figure 8 The shown transceiver unit 802 may be a communication interface, and this communication interface may be Figure 11 the network interface 1114 in, and this network interface 1114 may include an input interface and an output interface. Alternatively, the network interface 1114 may also be a transceiver circuit, and this transceiver circuit may include an input interface circuit and an output interface circuit.

[0248] The processor 1111 is mainly used to process communication protocols and communication data, and control the entire communication device, execute software programs, and process the data of the software programs. For example, it is used to support the communication device to perform the actions described in the embodiments. The communication device may include a baseband processor and a central processor. The baseband processor is mainly used to process communication protocols and communication data, and the central processor is mainly used to control the entire terminal device, execute software programs, and process the data of the software programs. Figure 11 The processor 1111 in may integrate the functions of the baseband processor and the central processor. Those skilled in the art can understand that the baseband processor and the central processor may also be independent processors and are interconnected through technologies such as a bus. Those skilled in the art can understand that the terminal device may include multiple baseband processors to adapt to different network systems, the terminal device may include multiple central processors to enhance its processing ability, and various components of the terminal device may be connected through various buses. The baseband processor may also be expressed as a baseband processing circuit or a baseband processing chip. The central processor may also be expressed as a central processing circuit or a central processing chip. The function of processing communication protocols and communication data may be built into the processor or stored in the memory in the form of a software program, and the processor executes the software program to implement the baseband processing function.

[0249] The memory is mainly used to store software programs and data. The memory 1112 can exist independently and be connected to the processor 1111. Optionally, the memory 1112 can be integrated with the processor 1111, for example, integrated within a single chip. Among them, the memory 1112 can store the program code for implementing the technical solution of the embodiments of the present application, and is controlled by the processor 1111 for execution. Various types of computer program codes being executed can also be regarded as the driver programs of the processor 1111.

[0250] Figure 11 Only one memory and one processor are shown. In an actual terminal device, there can be multiple processors and multiple memories. The memory can also be referred to as a storage medium or a storage device, etc. The memory can be a storage element on the same chip as the processor, that is, an on-chip storage element, or an independent storage element. The embodiments of the present application do not make any limitations in this regard.

[0251] The transceiver 1113 can be used to support the reception or transmission of radio frequency signals between the communication device and the terminal. The transceiver 1113 can be connected to the antenna 1115. The transceiver 1113 includes a transmitter Tx and a receiver Rx. Specifically, one or more antennas 1115 can receive radio frequency signals. The receiver Rx of the transceiver 1113 is used to receive the radio frequency signals from the antenna, convert the radio frequency signals into digital baseband signals or digital intermediate frequency signals, and provide the digital baseband signals or digital intermediate frequency signals to the processor 1111, so that the processor 1111 can perform further processing on the digital baseband signals or digital intermediate frequency signals, such as demodulation processing and decoding processing. In addition, the transmitter Tx in the transceiver 1113 is also used to receive the modulated digital baseband signals or digital intermediate frequency signals from the processor 1111, convert the modulated digital baseband signals or digital intermediate frequency signals into radio frequency signals, and transmit the radio frequency signals through one or more antennas 1115. Specifically, the receiver Rx can selectively perform one-stage or multi-stage down-conversion processing and analog-to-digital conversion processing on the radio frequency signals to obtain digital baseband signals or digital intermediate frequency signals, and the order of the down-conversion processing and the analog-to-digital conversion processing can be adjusted. The transmitter Tx can selectively perform one-stage or multi-stage up-conversion processing and digital-to-analog conversion processing on the modulated digital baseband signals or digital intermediate frequency signals to obtain radio frequency signals, and the order of the up-conversion processing and the digital-to-analog conversion processing can be adjusted. Digital baseband signals and digital intermediate frequency signals can be collectively referred to as digital signals.

[0252] The transceiver 1113 can also be referred to as a transceiver unit, a transceiver, a transceiver device, etc. Optionally, the devices used to implement the receiving function in the transceiver unit can be regarded as the receiving unit, and the devices used to implement the sending function in the transceiver unit can be regarded as the sending unit. That is, the transceiver unit includes a receiving unit and a sending unit. The receiving unit can also be referred to as a receiver, an input port, a receiving circuit, etc., and the sending unit can be referred to as a transmitter, a transmitter, or a transmitting circuit, etc.

[0253] It should be noted that Figure 11 The illustrated communication device 1100 can specifically be used to implement the steps implemented by the network device in the foregoing method embodiments and achieve the corresponding technical effects of the network device. Figure 11 For the specific implementation manners of the illustrated communication device 1100, reference can be made to the descriptions in the foregoing method embodiments, and details are not described herein again.

[0254] Please refer to Figure 12 , which is a schematic structural diagram of the communication device involved in the foregoing embodiments provided by the embodiments of the present application.

[0255] It can be understood that the communication device 120 includes, for example, modules, units, components, circuits, or interfaces, etc., which are appropriately configured together to execute the technical solutions provided by the present application. The communication device 120 can be the terminal device or network device described above, or a component (such as a chip) in these devices, for implementing the methods described in the following method embodiments. The communication device 120 includes one or more processors 121. The processor 121 can be a general-purpose processor or a dedicated processor, etc. 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, a terminal, or a chip, etc.), execute software programs, and process the data of the software programs.

[0256] Optionally, in one design, the processor 121 can include a program 123 (sometimes also referred to as code or instructions), and the program 123 can be run on the processor 121, so that the communication device 120 executes the methods described in the following embodiments. In another possible design, the communication device 120 includes a circuit ( Figure 12 not shown).

[0257] Optionally, the communication device 120 can include one or more memories 122, on which there is a program 124 (sometimes also referred to as code or instructions), and the program 124 can be run on the processor 121, so that the communication device 120 executes the methods described in the above method embodiments.

[0258] Optionally, the processor 121 and / or the memory 122 may include AI modules 127, 128, which are used to implement AI-related functions. The AI modules may be implemented in software, hardware, or a combination of software and hardware. For example, the AI modules may include a radio intelligence control (RIC) module. For example, the AI module may be a near-real-time RIC or a non-real-time RIC.

[0259] Optionally, data may also be stored in the processor 121 and / or the memory 122. The processor and the memory may be provided separately or integrated together.

[0260] Optionally, the communication device 120 may further include a transceiver 125 and / or an antenna 126. The processor 121 is sometimes also referred to as a processing unit and controls the communication device (such as a RAN node or a terminal). The transceiver 125 is sometimes also referred to as a transceiver unit, a transceiver, a transceiver circuit, or a transceiver, etc., and is used to implement the transceiver function of the communication device through the antenna 126.

[0261] Among them, Figure 8 the shown transceiver unit 802 may be a communication interface, and this communication interface may be Figure 12 the transceiver 125 in, and this transceiver 125 may include an input interface and an output interface. Alternatively, this transceiver 125 may also be a transceiver circuit, and this transceiver circuit may include an input interface circuit and an output interface circuit.

[0262] The 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 manners of the first communication device or the second communication device in the foregoing embodiments.

[0263] The embodiment of the present application further provides a computer program product (or referred to as a computer program). When the computer program product is executed by the processor, the processor executes the method of the possible implementation manners of the above first communication device or second communication device.

[0264] The embodiments of the present application further provide a chip system, which includes at least one processor for supporting a communication device to implement the functions involved in the possible implementation manners of the above communication device. Optionally, the chip system further includes an interface circuit, and the interface circuit provides program instructions and / or data for the at least one processor. In a possible design, the chip system may further include a memory for storing the necessary program instructions and data of the communication device. The chip system may be composed of chips or may include chips and other discrete devices, where the communication device may specifically be the first communication device or the second communication device in the foregoing method embodiments.

[0265] The embodiments of the present application further provide a communication system, and the network system architecture includes the first communication device and the second communication device in any of the above embodiments.

[0266] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other may be through some interfaces, and the indirect couplings or communication connections of devices or units may be in electrical, mechanical, or other forms.

[0267] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0268] In addition, each functional unit in various embodiments of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may 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 may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that makes a contribution, or all or part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

Claims

1. A communication method, characterized in that, The method includes: The first communication device sends first information, and the first information indicates the model alignment mode supported by the first communication device; The first communication device receives second information, and the second information indicates a target model alignment mode, where the target model alignment mode is the model alignment mode supported by the second communication device and the target alignment mode is the model alignment mode among the model alignment modes supported by the first communication device; The first communication device performs model alignment based on the target model alignment mode.

2. The method according to claim 1, characterized in that, The model alignment mode includes at least one of the following: Model-based alignment mode; Gradient-based alignment mode; Dataset-based alignment mode.

3. The method according to claim 1 or 2, characterized in that, The first communication device performs model alignment based on the target model alignment mode, including: The first communication device sends or receives target information corresponding to the target model alignment mode, and the target information is used to train the first model.

4. The method according to claim 3, characterized in that, The target alignment mode is a model-based alignment mode, and the target information includes a second model; Wherein, the first model is the model of the first communication device and the second model is the model of the second communication device; or, the first model is the model of the second communication device and the second model is the model of the first communication device.

5. The method according to claim 3, characterized in that, The target alignment mode is a gradient-based alignment mode, and the target information includes a gradient, and the gradient is obtained according to the output of the first model and the second model; the first model is a sending neural network model, and the second model is the receiving neural network model corresponding to the first model; Wherein, the first model is the model of the first communication device and the second model is the model of the second communication device; or, the first model is the model of the second communication device and the second model is the model of the first communication device.

6. The method according to claim 3, characterized in that, The target alignment mode is a dataset-based alignment mode, and the target information includes a dataset, and the dataset includes the input data and / or output data of the second model; Wherein, the first model is the model of the first communication device and the second model is the model of the second communication device; or, the first model is the model of the second communication device and the second model is the model of the first communication device.

7. The method according to any one of claims 3 to 6, characterized in that, Performing model alignment based on the target model alignment mode further includes: The first communication device sends or receives verification information, and the verification information is obtained according to the first model, and the verification information is used to verify whether the first model is aligned with the second model.

8. The method according to claim 7, characterized in that, The method further includes: If the first model is aligned with the second model, the first communication device associates the first model and the second model.

9. The method according to any one of claims 1 to 8, characterized in that, The first information further indicates the priority of the model alignment mode supported by the first communication device, and the target model alignment mode is the model alignment mode with the highest priority among the model alignment modes supported by the first communication device and the second communication device.

10. The method according to any one of claims 1 to 9, characterized in that, The first information includes at least one of the following: A first bitmap, and the first bitmap indicates the model alignment mode supported by the first communication device; Index of the model alignment mode supported by the first communication device; and / or The second information includes at least one of the following: A second bitmap indicating the target model alignment mode; Index of the target model alignment mode.

11. A communication method, characterized in that, The method includes: The second communication device receives first information indicating the model alignment mode supported by the first communication device; The second communication device sends second information indicating the target model alignment mode, where the target model alignment mode is the model alignment mode supported by the second communication device and the target alignment mode is one of the model alignment modes supported by the first communication device; The second communication device performs model alignment based on the target model alignment mode.

12. According to the method described in claim 11, wherein, The model alignment mode includes at least one of the following: Model-based alignment mode; Gradient-based alignment mode; Dataset-based alignment mode.

13. According to the method described in claim 11 or 12, wherein, The second communication device performing model alignment based on the target model alignment mode includes: The second communication device sends or receives target information corresponding to the target model alignment mode, and the target information is used to train the first model.

14. According to the method described in claim 13, wherein, When the target alignment mode is a model-based alignment mode, the target information includes a second model; Wherein, the first model is the model of the first communication device and the second model is the model of the second communication device; or, the first model is the model of the second communication device and the second model is the model of the first communication device.

15. According to the method described in claim 13, wherein, When the target alignment mode is a gradient-based alignment mode, the target information includes a gradient, and the gradient is obtained according to the output of the first model and the second model; the first model is a sending neural network model and the second model is the receiving neural network model corresponding to the first model; Wherein, the first model is the model of the first communication device and the second model is the model of the second communication device; or, the first model is the model of the second communication device and the second model is the model of the first communication device.

16. According to the method described in claim 13, wherein, When the target alignment mode is a dataset-based alignment mode, the target information includes a dataset, and the dataset includes input data and / or output data of the second model; Wherein, the first model is the model of the first communication device and the second model is the model of the second communication device; or, the first model is the model of the second communication device and the second model is the model of the first communication device.

17. According to the method described in any one of claims 13 to 16, wherein, The second communication device performing model alignment based on the target model alignment mode further includes: The second communication device sends or receives verification information, and the verification information is obtained according to the first model, and the verification information is used to verify whether the first model is aligned with the second model.

18. According to the method described in claim 17, wherein, The method further includes: If the first model is aligned with the second model, the second communication device associates the first model and the second model.

19. According to the method described in any one of claims 11 to 18, wherein, The first information further indicates the priority of the model alignment modes supported by the first communication device, and the target model alignment mode is the model alignment mode with the highest priority among the model alignment modes supported by the first communication device and the second communication device.

20. According to the method described in any one of claims 11 to 19, wherein, The first information includes at least one of the following: A first bitmap, where the first bitmap indicates the model alignment modes supported by the first communication device; Indices of the model alignment modes supported by the first communication device; And / or The second information includes at least one of the following: A second bitmap, where the second bitmap indicates the target model alignment mode; Indices of the target model alignment mode.

21. A communication device, wherein, It includes a module for performing the method according to any one of claims 1 to 20.

22. A communication device, wherein, It includes at least one processor, and the at least one processor is coupled to a memory; the at least one processor is used to perform the method according to any one of claims 1 to 20.

23. The communication device according to claim 22, wherein The communication device is a chip or a chip system.

24. A readable storage medium, wherein A computer program or instruction is stored in the storage medium, and when the computer program or instruction is executed by the communication device, the method according to any one of claims 1 to 20 is implemented.

25. A computer program product, wherein It includes an instruction, and when the instruction runs on a computer, the computer is caused to execute the method according to any one of claims 1 to 20.

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