Communication method and apparatus

Abstract

A communication method and apparatus. The method is applied to a first device, and the first device is a data sending end device. In the method, the first device sends first information to a second device, the first information indicating candidate models; then, the first device receives second information from the second device, the second information indicating a target model, the target model being one of the candidate models, and the second device being a network device; and finally, the first device encodes first data on the basis of the target model to obtain second data, and sends the second data. In this way, the first device can use the target model selected by the second device to encode the first data, so that the second data obtained by encoding the first data is suitable for transmission by the second device, thereby optimizing communication quality.

Description

Communication method and apparatus

[0001] Cross-reference to Related Applications

[0002] This application claims priority to the Chinese Patent Application No. 202410895180.8, filed on July 4, 2024, and entitled "A Communication Method and Apparatus", the content of which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0003] The present application relates to the technical field of wireless communication, and in particular to a communication method and apparatus. BACKGROUND

[0004] In some application scenarios of a wireless communication system, semantic communication is adopted to reduce the transmission amount and the demand for transmission bandwidth. Semantic communication refers to a technology of encoding (including selective feature extraction, compression, etc.) original data and then transmitting the encoded data to realize communication by using semantic representation information.

[0005] When facing network fluctuations, the quality of semantic communication will decrease. Therefore, how to optimize the communication quality is a technical problem to be solved at present. SUMMARY

[0006] Embodiments of the present application provide a communication method and apparatus for optimizing the communication quality.

[0007] In a first aspect, the present application provides a communication method. The method is applied to a first device, and the first device is a data sending end device. The first device can be a network device, such as an application server on a network side, a module (such as a circuit, a chip or a chip system, etc.) in the application server, or a logical node, a logical module or software capable of realizing all or part of the functions of the application server. The first device can also be a terminal device, or a chip, a unit or a module in the terminal device, or a communication apparatus with terminal functions, or a chip, a unit or a module inside the communication apparatus with terminal functions. In the method, the first device sends first information to a second device, wherein the first information indicates a candidate model; then, the first device receives second information from the second device, wherein the second information indicates a target model, the target model is one of the candidate models, and the second device is a network device; finally, the first device encodes first data according to the target model to obtain second data, and sends the second data to a third device, the third device being a data receiving end device.

[0008] By using the above method, the first device can send the candidate model to the second device before encoding the data to be transmitted (i.e., the first data), so that the second device selects a target model from the candidate model and indicates the target model through the second information. Thus, the first device can use the target model to encode the first data, so that the second data obtained after encoding the first data is suitable for transmission by the second device, thereby optimizing the communication quality.

[0009] In a possible implementation, the candidate models are associated with network states, and the at least two candidate models are associated with different network states, and the target model is associated with a current network state.

[0010] In the above implementation, the candidate models are associated with different network states, so that the second device determines the target model associated with the current network state from the candidate models after determining the current network state, and indicates the target model through the second information. Thus, the first device can use the target model to encode the first data, so that the second data obtained after encoding the first data is suitable for transmission in the current network state, thereby optimizing the communication quality.

[0011] In a possible implementation, the candidate model includes at least two candidate models associated with the same network state, and the first information further indicates priorities of the at least two candidate models.

[0012] By using the above method, for different network states, any network state can be associated with at least two candidate models, and the at least two candidate models are marked with priorities to represent the levels of advantages and disadvantages of encoding the first data by the at least two candidate models, so that the optimal one is selected, thereby optimizing the communication quality.

[0013] In a possible implementation, the second information further indicates an effective time of the target model; and the encoding the first data according to the target model includes: encoding the first data according to the target model within the effective time.

[0014] By using the above method, based on the change of the network state, the target model is used only in the current period through the effective time, so that the situation that the target model is not suitable for the changed network state is prevented, and the accuracy of using the target model is ensured.

[0015] In a possible implementation, the effective time includes a starting moment, and the encoding the first data according to the target model within the effective time includes: encoding the first data according to the target model when the starting moment is reached; or the effective time includes a starting moment and an effective duration, and the encoding the first data according to the target model within the effective time includes: encoding the first data according to the target model when the starting moment is reached and within the effective duration; or the effective time includes a starting moment and an ending moment, and the encoding the first data according to the target model within the effective time includes: encoding the first data according to the target model within a time period from the starting moment to the ending moment.

[0016] In a possible implementation, before the first information is sent to the second device, the method further includes: determining the candidate model according to an attribute of the first data, the attribute of the first data including at least one of: a data amount, a frame rate, and an application scenario.

[0017] In this way, the candidate model is associated with the first data, so that the second data is better adapted to transmission in the current network state, and the communication quality is optimized.

[0018] In a possible implementation, the first device is an application server, the second device is a network device, and the third device is a terminal device, where the network device is a core network device or an access network device; or the first device is a terminal device, the second device is a network device, and the third device is a terminal device, where the network device is a core network device or an access network device; or the first device is an application server, the second device is a routing device, and the third device is a terminal device.

[0019] In a possible implementation, the first data is video data, and the candidate model is a model used for encoding the video data.

[0020] In a second aspect, the present application provides a communication method, which can be applied to a second device. The second device can be a network device, such as an access network device, a module (e.g., a circuit, a chip or a chip system, etc.) in the access network device, or a logic node, a logic module or software capable of implementing all or part of the functions of the access network device. For example, the second device can be a core network device, a module (e.g., a circuit, a chip or a chip system, etc.) in the core network device, or a logic node, a logic module or software capable of implementing all or part of the functions of the core network device. In the method, the second device receives first information from a first device, wherein the first information indicates a candidate model, and the candidate model is used for data encoding. Then, the second device sends second information to the first device, wherein the second information indicates a target model, and the target model is one of the candidate models.

[0021] By using the above method, the second device can select a target model from the candidate models and indicate the target model through the second information. Thus, the first device can use the target model to encode the first data, so that the second data is adapted to transmission by the second device, thereby optimizing the communication quality.

[0022] In a possible implementation, the candidate models are associated with network states, and at least two candidate models are associated with different network states. Based on this, the second device selects a target model from the candidate models according to a current network state.

[0023] In a possible implementation, the selecting a target model from the candidate models according to a current network state includes: selecting at least two candidate models associated with the current network state from the candidate models, and then determining a target model from the at least two candidate models according to priorities of the at least two candidate models.

[0024] By using the above method, the second device can select a target model from the candidate models according to a current network state, so that the first device uses the target model to encode the first data, so that the second data is adapted to transmission in the current network state, thereby optimizing the communication quality.

[0025] In a possible implementation, the first information further indicates priorities of the at least two candidate models.

[0026] In a possible implementation, the second information further indicates a validity time of the target model.

[0027] In a possible implementation, the validity time includes a start time; or the validity time includes a start time and a validity duration; or the validity time includes a start time and an end time.

[0028] In a possible implementation, the method further includes: receiving second data from the first device, and sending the second data to a third device, wherein the second data is obtained by encoding first data according to the target model by the first device.

[0029] In a possible implementation, the first device is an application server, and the second device is a network device, wherein the network device is a core network device or an access network device; or the first device is a terminal device, and the second device is a network device, wherein the network device is a core network device or an access network device; or the first device is an application server, and the second device is a routing device.

[0030] In a third aspect, the present application provides a communication method, which is applied to a first device. In the method, the first device receives third information sent by a second device, wherein the third information indicates a current network state; then, the first device selects a target model associated with the current network state from candidate models according to the current network state, wherein the candidate models are associated with network states, and network states associated with at least two candidate models are different; further, the first device encodes first data according to the target model, obtains second data, and sends the second data to a third device, wherein the third device is a data receiving end device.

[0031] In a possible implementation, the third information further indicates a validity time of the target model; and the encoding of the first data according to the target model includes: encoding the first data according to the target model within the validity time.

[0032] In a possible implementation, the validity time includes a start time, and the encoding of the first data according to the target model within the validity time includes: encoding the first data according to the target model when the start time is reached; or the validity time includes a start time and a validity duration, and the encoding of the first data according to the target model within the validity time includes: encoding the first data according to the target model when the start time is reached and within the validity duration; or the validity time includes a start time and an end time, and the encoding of the first data according to the target model within the validity time includes: encoding the first data according to the target model within a time period from the start time to the end time.

[0033] In a possible implementation, before determining the target model associated with the current network state from the candidate models according to the current network state, the method further includes: determining the candidate models according to attributes of the first data, the attributes of the first data including at least one of the following: data volume, frame rate, application scenario.

[0034] In a possible implementation, the first device is an application server, the second device is a network device, and the third device is a terminal device, where the network device is a core network device or an access network device; or the first device is a terminal device, the second device is a network device, and the third device is a terminal device, where the network device is a core network device or an access network device; or the first device is an application server, the second device is a routing device, and the third device is a terminal device.

[0035] In a possible implementation, the first data is video data, and the candidate models are models used for encoding the video data.

[0036] In a fourth aspect, the present application provides a communication method, which can be applied to a second device. In the method, the second device sends third information to a first device, where the third information indicates a current network state; then the second device receives second data from the first device and sends the second data to a third device, where the second data is obtained by encoding first data by the first device according to a target model, and the target model is associated with the current network state. Optionally, the second device periodically sends the third information to the first device.

[0037] In a fifth aspect, the present application provides a communication apparatus, which has the functions of the first aspect and the third aspect, for example, the communication apparatus includes modules or units or means corresponding to the operations of the first aspect, which can be implemented by software, or by hardware, or by a combination of software and hardware.

[0038] In a sixth aspect, the present application provides a communication apparatus, which has the functions of the second aspect and the fourth aspect, for example, the communication apparatus includes modules or units or means corresponding to the operations of the second aspect, which can be implemented by software, or by hardware, or by a combination of software and hardware.

[0039] In a seventh aspect, the present application provides a communication apparatus, which comprises an interface circuit and one or more processors. The one or more processors are coupled with a memory. The memory is configured to store part or all of the computer program or instructions necessary to implement the functions related to the first aspect to the fourth aspect. The one or more processors can execute the computer program or instructions, when the computer program or instructions are executed, to cause the communication apparatus to implement the method in any possible design or implementation manner of the first aspect to the fourth aspect. The interface circuit is configured to implement the communication function within the communication apparatus and / or the communication function of the communication apparatus with other apparatuses or components.

[0040] In a possible design, the processor is configured to communicate with other apparatuses or components via the interface circuit.

[0041] In a possible design, the communication apparatus can further comprise the memory.

[0042] The communication apparatus can be a terminal, a communication / processing module in the terminal, a chip responsible for the communication function (such as a modem chip, also referred to as a baseband chip) or an SoC or SIP chip containing a modem module in the terminal, or a circuit or chip responsible for the processing function (such as a GPU) in the terminal.

[0043] In an eighth aspect, the present application provides a communication system, which comprises a communication apparatus configured to execute the method in any possible design of the first aspect and the third aspect, and a communication apparatus configured to execute the method in any possible design of the second aspect and the fourth aspect.

[0044] In a ninth aspect, the present application provides a computer readable storage medium, which stores computer readable instructions, when the computer readable instructions are read and executed by a computer, the computer is caused to execute the method in any possible design of the first aspect to the fourth aspect.

[0045] In a tenth aspect, the present application provides a computer program product, when the computer program product is read and executed by a computer, the computer is caused to execute the method in any possible design of the first aspect to the third aspect.

[0046] The technical effects that can be achieved in any of the fourth aspect to the tenth aspect can be described with reference to the technical effects that can be achieved in the first aspect, the second aspect, the third aspect and / or the fourth aspect, and details are not discussed herein. BRIEF DESCRIPTION OF DRAWINGS

[0047] FIG. 1 is a possible, non-limiting system schematic diagram;

[0048] FIG. 2 is a possible application framework schematic diagram in a communication system;

[0049] FIG. 3 is a schematic diagram of another possible application framework in a communication system;

[0050] FIG. 4 is a schematic diagram of a semantic communication;

[0051] FIG. 5A is a schematic diagram of an application scenario provided in the present application;

[0052] FIG. 5B is a schematic diagram of an application scenario provided in the present application;

[0053] FIG. 5C is a schematic diagram of an application scenario provided in the present application;

[0054] FIG. 6 is a schematic diagram of a flow of a communication method provided in the present application;

[0055] FIG. 7 is a schematic diagram of a flow of another communication method provided in the present application;

[0056] FIG. 8 is a schematic diagram of a structure of a communication apparatus provided in the present application;

[0057] FIG. 9 is a schematic diagram of a structure of another communication apparatus provided in the present application;

[0058] FIG. 10 is a schematic diagram of a structure of a terminal provided in the present application. DETAILED DESCRIPTION

[0059] FIG. 1 is a schematic diagram of a possible, non-limiting system. As shown in FIG. 1, the communication system 10 includes a radio access network (RAN) 100 and a core network (CN) 200. Optionally, the communication system further includes an Internet 300. The RAN 100 includes at least one RAN node (e.g., 110a and 110b in FIG. 1, collectively referred to as 110) and at least one terminal (e.g., 120a-120j in FIG. 1, collectively referred to as 120). Other RAN nodes, such as wireless relay devices and / or wireless backhaul devices (not shown in FIG. 1), etc., can also be included in the RAN 100. The terminal 120 is connected to the RAN node 110 in a wireless manner. The RAN node 110 is connected to the core network 200 in a wireless or wired manner. The core network device in the core network 200 and the RAN node 110 in the RAN 100 can be different physical devices respectively, or can be the same physical device integrated with the logical functions of the core network and the radio access network.

[0060] The RAN 100 can be a 3rd generation partnership project (3GPP) -related cellular system, e.g., a 4G, 5G mobile communication system, or a future-oriented evolved system. The RAN 100 can also be an open radio access network (O-RAN or ORAN), a cloud radio access network (CRAN), or a wireless fidelity (Wi-Fi) system. The RAN 100 can also be a communication system that combines two or more of the above systems.

[0061] The RAN nodes 110, which can also be referred to as access network devices, RAN entities, or access nodes, etc., form part of the communication system 100 and help terminals to access the wireless access. The RAN nodes 110 in the communication system 100 can be of the same type or of different types. In some scenarios, the roles of the RAN nodes 110 and the terminals 120 are relative, e.g., the network element 120i in Figure 1 can be a helicopter or a drone, which can be configured as a mobile base station. For those terminals 120j that access the RAN 100 through the network element 120i, the network element 120i is a base station; but for the base station 110a, the network element 120i is a terminal. The RAN nodes 110 and the terminals 120 are sometimes referred to as communication apparatuses, e.g., the network elements 110a and 110b in Figure 1 can be understood as communication apparatuses with base station functions, and the network elements 120a-120j can be understood as communication apparatuses with terminal functions.

[0062] In a possible scenario, the RAN node can be a base station, an evolved NodeB (eNodeB), an access point (AP), a transmission reception point (TRP), a next generation NodeB (gNB), a base station in a future mobile communication system, or an access node in a Wi-Fi system, etc. The RAN node can be a macro base station (such as 110a in FIG. 1), a micro base station or an indoor station (such as 110b in FIG. 1), a relay node or a donor node, or a wireless controller in a CRAN scenario. Optionally, the RAN node can also be a server, a wearable device, a vehicle or a vehicle-mounted device, etc. For example, the access network device in vehicle to everything (V2X) technology can be a road side unit (RSU). All or part of the functions of the RAN node in this application can also be implemented by software functions running on hardware, or by virtualized functions instantiated on a platform (such as a cloud platform). The RAN node can also be provided with a communication module, circuit or chip for performing corresponding communication functions, and program instructions for performing corresponding communication functions. The RAN node in this application can also be a logical node, a logical module or software that can implement all or part of the functions of the RAN node.

[0063] In another possible scenario, multiple RAN nodes cooperate to assist a terminal to implement wireless access, and different RAN nodes respectively implement part of the functions of a base station. For example, the RAN node 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 arranged, or can be included in the same network element, such as a baseband unit (BBU). The RU can be included in a radio frequency device or a radio frequency unit, such as a remote radio unit (RRU), an active antenna processing unit (AAU), or a remote radio head (RRH).

[0064] In different systems, the CU (or CU-CP and CU-UP), DU or RU can also have different names, but those skilled in the art can understand their meanings. For example, in the ORAN system, the CU can also be referred to as O-CU (open CU), the DU can also be referred to as O-DU, the CU-CP can also be referred to as O-CU-CP, the CU-UP can also be referred to as O-CU-UP, and the RU can also be referred to as O-RU. For the convenience of description, the CU, CU-CP, CU-UP, DU and RU are taken as examples for description in this application. 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.

[0065] A terminal can be a device or module with corresponding communication functions for accessing the above-mentioned communication system. The terminal can also be referred to as a terminal device, a user equipment (UE), a mobile station, a mobile terminal, etc. The terminal can be widely used in various scenarios, such as device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine-type communication (MTC), internet of things (IOT), virtual reality, augmented reality, industrial control, autonomous driving, remote medical treatment, smart power grid, smart furniture, smart office, smart wear, smart transportation, smart city, etc. The terminal can be a mobile phone, a tablet computer, a computer with wireless transceiver function, a wearable device, a vehicle, a drone, a helicopter, an airplane, a ship, a robot, a mechanical arm, a smart home device, a transport vehicle with wireless communication function, a communication module, etc. Embodiments of the present application do not limit the device form of the terminal. The terminal is usually provided with a communication module, circuit or chip for executing corresponding communication functions. The terminal is also configured with program instructions for executing corresponding communication functions.

[0066] In order to support artificial intelligence (AI) technology in a wireless network, an AI node can also be introduced in the network.

[0067] The AI node can be deployed in one or more of the following positions in the communication system: an access network node (RAN node), a terminal device, or a core network device, etc., or the AI node can also be deployed separately, for example, in a position other than any of the above-mentioned devices, such as a host or a cloud server of an over the top (OTT) system. The AI node can communicate with other devices in the communication system, which can be one or more of the following: a network device, a terminal device, or a network element of a core network, etc.

[0068] It can be understood that the present application does not limit the number of AI nodes. For example, when there are multiple AI nodes, the multiple AI nodes can be divided based on functions, such as different AI nodes being responsible for different functions.

[0069] It can also be understood that the AI node can be a separate device, can be integrated into the same device to implement different functions, or can be a network element in a hardware device, or a software function running on a dedicated hardware, or a virtualized function instantiated on a platform (such as a cloud platform), and the present application does not limit the specific form of the AI node.

[0070] The AI node can be an AI network element or an AI module.

[0071] FIG. 2 is a schematic diagram of a possible application framework in a communication system. As shown in FIG. 2, the network elements in the communication system are connected through interfaces (such as NG, Xn), or air interfaces. These network element nodes, such as one or more of the core network devices, access network nodes (RAN nodes), terminals, or devices in operations administration and maintenance (OAM), are provided with one or more AI modules (only 1 is shown in FIG. 2 for clarity). The access network node can be a separate RAN node, or can include multiple RAN nodes, such as a CU and a DU. The CU and / or the DU can also be provided with one or more AI modules. The CU can also be split into a CU-CP and a CU-UP, and the CU-CP and / or the CU-UP are provided with one or more AI modules.

[0072] The AI module is configured to implement a corresponding AI function. AI modules deployed in different network elements can be the same or different. The model of the AI module can implement different functions according to different parameter configurations. The model of the AI module can be configured based on one or more of the following parameters: a structure parameter (for example, at least one of a number of neural network layers, a width of a neural network, a connection relationship between layers, a weight of a neuron, an activation function of a neuron, or a bias in the activation function), an input parameter (for example, a type of input parameter and / or a dimension of the input parameter), or an output parameter (for example, a type of output parameter and / or a dimension of the output parameter). The bias in the activation function can also be referred to as a bias of the neural network.

[0073] In one example, the neural network described above can be a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), or a generative adversarial network (GAN).

[0074] A DNN is an artificial neural network architecture that has multiple layers of nonlinear transformation units stacked together in a hierarchical structure, forming a deep computational model. Compared with a shallow neural network, a deep neural network has more hidden layers, allowing the network model to capture more complex internal structures of data and high-level abstract features.

[0075] A CNN is a deep neural network with a convolutional structure. The CNN includes a feature extractor composed of convolutional layers and subsampling layers. The feature extractor can be regarded as a filter, and the convolution process can be regarded as using a trainable filter to convolve an input image or a convolution feature plane.

[0076] An RNN is a type of recursive neural network that takes sequence data as input, performs recursion in the evolution direction of the sequence, and connects all nodes (recurrent units) in a chain.

[0077] A GAN is a deep learning model. It is composed of a generator and a discriminator, and is trained through adversarial learning. The purpose is to estimate the latent distribution of data samples and generate new data samples.

[0078] An AI module can have one or more models. A model can infer an output including one or more parameters. The learning process, training process, or inference process of different models can be deployed in different nodes or devices, or can be deployed in the same node or device.

[0079] FIG. 3 is a schematic diagram of a possible application framework in a communication system. As shown in FIG. 3, the communication system includes a RAN intelligent controller (RIC). The RIC can be an AI module as shown in FIG. 2, for example, to implement AI related functions. The RIC includes a near-real time RIC (near-RT RIC) and a non-real time RIC (Non-RT RIC). The Non-RT RIC mainly processes non-real time information, such as data that is not sensitive to latency, which can be in the order of seconds. The near-RT RIC mainly processes near-real time information, such as data that is relatively sensitive to latency, which can be in the order of tens of milliseconds.

[0080] The near-RT RIC is used for model training and inference. For example, for training an AI model, and using the AI model for inference. The near-RT RIC can obtain network side and / or terminal side information from RAN nodes (such as CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. The information can be used as training data or inference data. The inference result can be submitted to the RAN nodes and / or terminals. The inference result can be exchanged between CU and DU, and / or between DU and RU. For example, the near-RT RIC submits the inference result to the DU, which is sent to the RU.

[0081] The Non-RT RIC is also used for model training and inference. For example, for training an AI model, and using the model for inference. The Non-RT RIC can obtain network side and / or terminal side information from RAN nodes (such as CU, CU-CP, CU-UP, DU, and / or RU) and / or terminals. The information can be used as training data or inference data, and the inference result can be submitted to the RAN nodes and / or terminals. The inference result can be exchanged between CU and DU, and / or between DU and RU. For example, the Non-RT RIC submits the inference result to the DU, which is sent to the RU.

[0082] The near-real-time RIC and the non-real-time RIC can also be separately set as a network element. The near-real-time RIC and the non-real-time RIC can also be part of other devices, for example, the near-real-time RIC is set in the RAN node (for example, in the CU or the DU), and the non-real-time RIC is set in the OAM, the cloud server, the core network device, or other network devices.

[0083] In some application scenarios of a wireless communication system, in order to reduce the transmission amount and reduce the demand for transmission bandwidth, the original data is generally transmitted by encoding, such as semantic communication. Semantic communication refers to encoding (including selective feature extraction, compression, etc.) of original data, and then transmitting the encoded data to realize communication by using semantic representation information. Referring to FIG. 4, data x (such as a picture) is sequentially encoded by a semantic source encoder (for semantic source encoding) and a joint channel encoder (for channel encoding) to extract semantic information of the data x, which can be source signal recovery or intelligent task execution. Generally, the semantic source encoder can be understood as a semantic source decoding model, and the channel encoder can be understood as a channel encoding model. It can be understood that it also includes data or models that are helpful to the above-mentioned encoding models. After receiving the semantic information of the data x, the receiver decodes the semantic information based on a decoder (including a semantic source decoding model and a channel decoding model), and then restores the semantic information to data x'. Taking the data x as a picture as an example, the picture restored by the receiver based on the decoder may not be completely correct in terms of pixel points, but is correct in terms of semantics.

[0084] However, when facing network fluctuations, a suitable encoding model cannot be selected according to the real-time network state, so that the encoded data (i.e., semantic information) may have a high semantic information transmission delay due to high latency and small bandwidth, which causes the quality of semantic communication to decrease and affects the communication quality between the data sending end and the data receiving end. Therefore, the present application provides a communication method to optimize the communication quality.

[0085] The communication method and the communication device provided in the present application are further described below with reference to the drawings. It can be understood that the first device and the second device are taken as an example to illustrate the execution subject of the interaction in the present application, but the present application does not limit the execution subject of the interaction. For example, the method executed by the first device in the present application can also be implemented by a module (such as a circuit, a chip or a chip system, etc.) in the first device, or a logical node, a logical module or software capable of realizing all or part of the function of the first device. The method executed by the second device in the present application can also be implemented by a module (such as a circuit, a chip or a chip system, etc.) in the second device, or a logical node, a logical module or software capable of realizing all or part of the function of the second device. For different application scenarios, the first device can be an application server, a terminal device, etc.; the second device can be a core network device, an access network device, a routing device, etc. For example, the following application scenarios are included:

[0086] Application scenario 1, as shown in FIG. 5A, the first device is an application server (such as a cloud server or an application (APP) server, etc.), the second device is a network device (such as a core network device or an access network device), and the terminal device (UE in the figure) is a data receiving end device. The application server communicates with a user plane function (UPF) network element of the core network device through a data network (DN), and the UPF network element communicates with the terminal device through an access network (AN). It can be understood that the core network device also includes a plurality of NF network elements. For example, the NF network elements include part or all of the following network elements:

[0087] Network Slice Selection Function (NSSF) network element, Authentication Server Function (AUSF) network element, unified data management (UDM) network element, network exposure function (NEF) network element, NF Repository Function (NRF) network element, Access and Mobility Management Function (AMF) network element, session management function (SMF) network element, policy control function (PCF) network element, Application Function (AF), The Service Communication Proxy (SCP) network element. The description and function of each network element can refer to the 5G related protocol, which will not be expanded here.

[0088] Any two NF network elements are connected through a service interface to call the corresponding service operation, and the service interface is generally represented by a sequence number. For example, the sequence number part or all of the following service interfaces are included: N1, N2, N3, N4, N5, N6, N7, N11, N33, and the meanings of these interface sequence numbers are as follows:

[0089] N1: the interface between the AMF network element and the UE, used to deliver non access stratum (NAS) signaling (such as including QoS rules from the AMF network element) to the terminal device, etc.

[0090] N2: the interface between the AMF network element and the AN, used to deliver core network side to access network device radio bearer control information, etc.

[0091] N3: the interface between the AN and the UPF network element, used to deliver uplink and downlink user plane data between the access network device and the UPF network element.

[0092] N4: the interface between the SMF network element and the UPF network element, used to deliver information between the control plane and the user plane, including the delivery of control plane to user plane forwarding rules, QoS rules, traffic statistics rules, etc. and the information reporting of the user plane.

[0093] N5: Interface between the AF network element and the PCF network element, used to transfer information between the AF network element and the PCF network element.

[0094] N6: Interface between the UPF network element and the DN, used to transfer uplink and downlink user data flow between the UPF network element and the DN.

[0095] N7: Interface between the SMF network element and the PCF network element, used to transfer information between the SMF network element and the PCF network element.

[0096] N11: Interface between the AMF network element and the SMF network element, used to transfer information between the AMF network element and the SMF network element.

[0097] N33: Interface between the AF network element and the NEF network element, used to transfer information between the AF network element and the NEF network element.

[0098] It can be understood that the above-mentioned network elements or functions can be network elements in a hardware device, or software functions running on a dedicated hardware, or virtualized functions instantiated on a platform (for example, a cloud platform). As a possible implementation method, the above-mentioned network elements or functions can be implemented by one device, or implemented by multiple devices together, or implemented as a functional module in one device, and the embodiments of the present application do not make specific limitations thereon. In addition, each of the above NF network elements can also be referred to as NF, for example, the AMF network element can be referred to as AMF.

[0099] Application scenario 2, as shown in FIG. 5B, the first device is a terminal device (UE1 in the figure), the second device is a network device, and UE2 is a data receiving end device. For example, in the tactile internet, UE1 is a terminal interacting with a master domain tactile user, the second device is a core network device or an access network device (such as AN1 or AN2 in the figure), and UE2 is a remote control robot in a controlled domain. Among them, UE1 communicates with the UPF network element of the core network device through AN1, and the UPF network element communicates with UE2 through AN2. The description of the core network device is referred to the above-mentioned application scenario 1, which is not repeated here.

[0100] Application scenario 3, as shown in FIG. 5C, the first device is an application server, the second device is a routing device, and UE is a data receiving end device. For example, the second device is a Wi-Fi router, an access point (AP), or a set-top box, etc. Among them, the application server communicates with the second device through a fixed network, and the second device communicates with the UE through a Wi-Fi communication protocol. When the second device and the UE communicate based on the Wi-Fi communication protocol, the following standards can be used, but are not limited to: 802.11, 802.11b, 802.11a / g, 802.11n, 802.11ac, 802.11ax.

[0101] Based on the above application scenarios, FIG. 6 is a flow diagram of a communication method provided by the present application. The method includes the following steps:

[0102] Step 601: The first device sends first information to the second device, the first information indicating a candidate model.

[0103] In this step, the candidate model is multiple (i.e., at least two) based on different model parameters (such as coding rate, quantization step, compression rate, etc.).

[0104] In one possible implementation, the candidate model is associated with a network state. And at least two of the candidate models are associated with different network states. It can be understood that the network state is determined by a device (such as a base station) that communicates with a data receiving end device (for the sake of brevity, hereinafter referred to as a third device).

[0105] In one possible implementation, the network state can be divided according to at least one factor, including but not limited to: bandwidth, delay, packet loss rate, etc. For example, if the channel bandwidth corresponding to the third device at the current moment is in a first range (such as 5MHz-15MHz), then the current moment corresponds to one network state; if the channel bandwidth corresponding to the third device at the current moment is in a second range (such as 15MHz-25MHz), then the current moment corresponds to another network state. It can be understood that the network state can be divided according to the above single factor or multiple factors, which is not limited here.

[0106] In one possible implementation, the network state is divided into different levels, and the level is associated with network transmission rate, base station computing power, energy consumption, etc. For example, the network state is divided into 3 levels, level 1 is associated with faster network transmission rate, level 2 is associated with medium network transmission rate, and level 3 is associated with slower network transmission rate. It can be understood that the network transmission rate is related to factors such as bandwidth, delay, and packet loss rate, and the network state can be divided into more levels according to the range of network transmission rate, which is not limited by the present application, and the fast and slow of network transmission rate is not defined.

[0107] In a possible implementation, the candidate models can be determined by the first device according to attributes of the first data. The first data is data to be sent by the first device, and the attributes can also be referred to as parameters, specifications, or the like. Optionally, the first data can be video data, picture data, audio data, text data, or the like. Taking the first data as video data as an example, the candidate models are models for encoding the video data, that is, the candidate models are used for encoding the video data. For example, the candidate models are AI Codec models, which include but are not limited to a Convolutional Neural Networks (CNN) based video encoding model, a Transformer based video encoding model, a generative based video encoding model, and the like.

[0108] The attributes of the first data include at least one of a data amount, a frame rate, and an application scenario. The data amount represents a data size of the first data, the frame rate represents a frequency of continuous display of images in the first data, and the application scenario represents a scenario to which the first data is applied (or can be understood as a display scenario). For example, the application scenario includes an APP, a webpage, and the like, that is, the application scenario can represent a requirement on the definition of a displayed image. Optionally, the first device can determine a compression rate range according to the parameters of the first data, and then determine the candidate models according to the compression rate range. For example, the first data has a large data amount, a large frame rate, and a small requirement on the definition of the application scenario, and then the compression rate range corresponding to the first data can be a large range (such as 40%-60%). Because the model parameters are different, the compression rates of any two candidate models are different, and therefore the compression rate range generally corresponds to multiple candidate models. For example, the compression rate range corresponds to five candidate models, which are h1, h2, h3, h4, and h5.

[0109] In a possible implementation, the candidate models can be pre-transmitted to the second device, or the first device and the second device pre-agree the candidate models, and the candidate models have unique identities. In this way, the first information can include the unique identities of the candidate models to indicate the candidate models. Alternatively, the first information can include model parameters to indicate the candidate models. Similarly, the network states can also be pre-agreed by the first device and the second device. To this end, the first information can indicate the network state associated with each candidate model in the form of a mapping relationship (such as a key-value form). For example, the candidate models include h1, h2, h3, h4, and h5, and the network states include two levels, which are level 1 and level 2; wherein the candidate models h1, h3, and h4 are associated with the network state corresponding to level 1, and the candidate models h2 and h5 are associated with the network state corresponding to level 2. Optionally, one candidate model can be associated with at least one network state. For example, the candidate model h1 is associated with the network state corresponding to level 1 and the network state corresponding to level 2.

[0110] In a possible implementation, the first information indicates model parameters of the candidate models, and the model parameters are used by the second device to determine the target model from the candidate models. For details, refer to step 602.

[0111] In a possible implementation, the first information further indicates priorities of the at least two candidate models. Optionally, the at least two candidate models are associated with the same network state. It can be understood that for any network state, the priorities of the at least two candidate models associated with the network state are different, and the priority indicates the priority of selecting the candidate model as the target model. For example, in descending order of priority, the candidate models associated with the network state corresponding to level 1 are h3, h1, and h4, and vice versa.

[0112] In a possible implementation, the priorities of the at least two candidate models can also be indicated by the first device by transmitting another information to the second device, which is not limited in the present application.

[0113] Optionally, based on the change of the network state, the first information can be periodically transmitted by the first device, so as to periodically select the target model, ensure the real-time performance of the target model and the current network state, prevent the target model from being unsuitable for the changed network state, and ensure the accuracy of the use of the target model.

[0114] Step 602: The second device selects the target model from the candidate models according to the current network state.

[0115] In a possible implementation, the second device can determine the current network state by factors such as the current bandwidth, delay, packet loss rate, and the like, and specific descriptions can refer to descriptions in step 601, which are not repeated herein. After determining the current network state, the second device selects a candidate model associated with the current network state, and then selects the target model from the candidate model associated with the current network state. For example, the current network state is a network state corresponding to level 1, and the candidate models associated with the current network state include h1, h3, and h4.

[0116] In a possible implementation, the first information further indicates priorities of the at least two candidate models associated with the current network state. Therefore, the second device can determine the target model from the at least two candidate models associated with the current network state according to the priorities of the at least two candidate models associated with the current network state.

[0117] Optionally, the second device selects, as the target model, a candidate model with the highest priority from the at least two candidate models associated with the current network state. For example, the candidate models associated with the current network state are h3, h1, and h4 in descending order of priority, and therefore the second device selects the candidate model h3 as the target model.

[0118] Optionally, the second device selects the target model from the at least two candidate models associated with the current network state by a calculation manner such as weighted summation, according to model parameters of the candidate models, the current network state, resource scheduling of the third device, and the priorities of the at least two candidate models associated with the current network state. The weight of the resource scheduling of the third device, the weight of the model parameters, and the weight of the priorities can be preset values according to experience. It can be understood that the weight of the priorities corresponding to different network states and the weight of any model parameter are different. In addition, the calculation manner can also be other algorithms than the weighted summation, which are not limited herein.

[0119] For example, the resource scheduling of the third device includes but is not limited to the following information: data packet delay requirement, data packet remaining delay, data packet size, resource size allocated to the data packet, and the like. The current network state includes but is not limited to the following information: bandwidth of the network, base station computing power, energy consumption, and the like.

[0120] Optionally, the second device can assign weights to different model parameters and priorities according to the current network state and the resource scheduling of the third device, and then perform weighted summation according to the weights corresponding to different model parameters and priorities to obtain a score value corresponding to each candidate model. For example, the compression rate is assigned a weight w1, the encoding rate is assigned a weight w2, and the priority is assigned a weight w3. The score value corresponding to the candidate model is shown in the following formula (1): M = w1*q1 + w2*q2 + w3*q3 Formula (1);

[0121] wherein M is the score value of the candidate model, q1 represents the compression rate of the candidate model, q2 represents the encoding rate of the candidate model, q3 represents the priority of the candidate model, w1 represents the weight of the compression rate, w2 represents the weight of the encoding rate, and w3 represents the weight of the priority. It can be understood that the above formula (1) is only an example, and the model parameters can also include other parameters, which are not limited and described herein.

[0122] In a possible implementation, the second device can select a target model suitable for the current network state according to the current network state. For example, if the current network state is level 1 (i.e., the network transmission rate of the current network state is fast), the compression rate of the target model selected by the second device is small. It can be understood that the smaller the compression rate, the smaller the difference between the original data (i.e., the first data) and the encoded data, and similarly, the closer the decoded data is to the original data. Therefore, on the basis of meeting the transmission requirements of the data, the clarity and accuracy of the data received by the third device are ensured. For another example, if the current network state is level 3 (i.e., the network transmission rate of the current network state is slow), the compression rate of the target model selected by the second device is large. It can be understood that the larger the compression rate, the smaller the data amount of the encoded data, so as to ensure that the data meets the transmission requirements. That is, the second device can directly select a target model from the candidate models according to the model parameters of the candidate models based on the current network state. It can be understood that the parameters of the candidate models include but are not limited to the encoding rate, the quantization step, etc. The second device can select a target model according to multiple candidate model parameters, which are not limited herein.

[0123] Step 603: The second device sends second information to the first device, and the second information indicates the target model.

[0124] In this step, the second information also indicates the effective time of the target model, which represents the valid time of using the target model. For example, the first device encodes the first data according to the target model within the effective time.

[0125] In one possible implementation, the effective time includes the start time. Based on this, the first device can encode the first data according to the target model when the start time is reached.

[0126] In one possible implementation, the effective time includes a start time and an effective duration. Based on this, the first device can encode the first data according to the target model when the start time is reached and within the effective duration. That is, as time goes by, if the current time is no longer within the effective duration, the first device will no longer use the target model to encode the first data.

[0127] In one possible implementation, the effective time includes a start time and an end time. Based on this, the first device can encode the first data according to the target model within the time period from the start time to the end time.

[0128] In one possible implementation, the effective time may take the form of, but is not limited to, the offset of the start time from the second device's decision time, the absolute start time, a time window (the start time is within the time window), the latest start time, etc. This application does not limit the form of the effective time.

[0129] Optionally, if the second device is an access network device (such as a base station) that communicates with the third device, then the second device can send the second information to the core network device through the N3 interface, and the core network device can then send the second information to the first device through the N6 interface.

[0130] Step 604: The first device encodes the first data according to the target model to obtain the second data.

[0131] In this step, after receiving the second information, the first device determines whether the currently used model is the target model. If so, it continues to use the target model. Otherwise, the parameters of the current model are adjusted to the parameters of the target model, i.e., the target model is used. The second data is the encoded data of the first data, such as the semantic information of the first data. It can be understood that the amount of the second data is less than that of the first data. The process of encoding the first data using the target model is not limited in this application.

[0132] Step 605: The first device sends the second data to the second device.

[0133] In this step, the first device sends the second data to the second device, which then forwards the second data to the third device. Upon receiving the second data, the third device decodes it to obtain the third data. Optionally, the second data may contain semantic information of the first data; therefore, the third data may not be completely identical to the first data, but they are semantically identical.

[0134] In summary, the first device sends the candidate models associated with different network states to the second device before encoding the first data, so that the second device determines the target model associated with the current network state from the candidate models after determining the current network state, and indicates the target model through the second information. Thus, the first device can use the target model to encode the first data, so that the second data is adapted to transmission in the current network state, thereby improving the communication quality.

[0135] FIG. 7 is a flowchart of a communication method provided by the present application. The method comprises the following steps:

[0136] Step 701: The second device sends third information to the first device, the third information indicating the current network state.

[0137] For the description of the current network state in step 601, the present application does not repeat it here.

[0138] Step 702: The first device selects a target model from the candidate models according to the current network state.

[0139] Referring to step 602, after the first device receives the current network state, it selects the candidate model associated with the current network state, and then selects the target model associated with the current network state from the candidate model associated with the current network state.

[0140] In a possible implementation, the first device can select the target model associated with the current network state according to the priority of the at least two candidate models associated with the current network state. Optionally, the candidate model with the highest priority among the at least two candidate models associated with the current network state is selected as the target model.

[0141] In a possible implementation, the third information further indicates the resource scheduling situation between the second device and the third device. Thus, the first device can further select the target model associated with the current network state from the at least two candidate models associated with the current network state by weighted summation or other calculation methods according to the model parameters of the candidate models, the current network state, the resource scheduling situation between the second device and the third device, and the priority of the at least two candidate models associated with the current network state. For details, refer to the description of step 602, which is not repeated here.

[0142] In a possible implementation, the third information further indicates the effective time of the target model. For details, refer to the description of step 603, which is not repeated here.

[0143] Step 703: The first device encodes the first data according to the target model to obtain the second data.

[0144] With reference to step 604, the present application will not be repeated here.

[0145] Step 704: The first device sends the second data to the second device.

[0146] With reference to step 605, the present application will not be repeated here.

[0147] In summary, the third information can be periodically sent by the second device. For this purpose, the first device can encode the first data according to the target model associated with the current network state, so that the second data is adapted for transmission in the current network state, thereby improving the communication quality.

[0148] It can be understood that, in order to implement the functions in the above embodiments, the network device and the terminal device comprise corresponding hardware structures and / or software modules for performing various functions. Those skilled in the art should easily realize that, in combination with the units and method steps of the examples described in the embodiments disclosed in the present application, the present application can be realized in the form of hardware or a combination of hardware and computer software. Whether a certain function is implemented in hardware or computer software driven hardware depends on the specific application scenarios and design constraints of the technical solutions.

[0149] FIGS. 6 and 7 are structural schematic diagrams of communication devices provided by embodiments of the present application. These communication devices can be used to implement the functions of the first device or the second device in the above method embodiments, and thus can also achieve the beneficial effects possessed by the above method embodiments. In embodiments of the present application, the first device can be one of the terminals 120a-120j as shown in FIG. 1, the first device can be the base station 110a or 110b as shown in FIG. 1, the first device can also be a module (such as a chip) applied to a terminal or a base station, and the first device can also be a module (such as a chip) applied to a terminal or a base station. In the following, the method shown in FIG. 6 is taken as an example, and the method shown in FIG. 7 will not be repeated.

[0150] As shown in FIG. 8, the communication device 800 comprises a processing unit 810 and a transceiver unit 820. The communication device 800 is used to implement the functions of the first device or the second device in the above method embodiments shown in FIG. 6.

[0151] When the communication apparatus 600 is configured to implement the function of the first device in the method embodiment shown in FIG. 6, the processing unit 810 is configured to: send, by the transceiver 820, first information to a second device, the first information indicating candidate models; receive, by the transceiver 820, second information from the second device, the second information indicating a target model, the target model being one of the candidate models; and encode, by the processing unit 810, first data according to the target model to obtain second data, and send the second data to a third device, the third device being a data receiving end device.

[0152] In a possible implementation, the candidate models are associated with network states, wherein the at least two candidate models are associated with different network states, and the target model is associated with a current network state.

[0153] In a possible implementation, the candidate models include at least two candidate models associated with a same network state, and the first information further indicates priorities of the at least two candidate models.

[0154] In a possible implementation, the second information further indicates a validity time of the target model, and the processing unit 810 is specifically configured to: encode the first data according to the target model within the validity time.

[0155] In a possible implementation, the validity time includes a start time, and the processing unit 810 is specifically configured to: encode the first data according to the target model when the start time is reached; or the validity time includes a start time and a validity duration, and the processing unit 810 is specifically configured to: encode the first data according to the target model when the start time is reached and within the validity duration; or the validity time includes a start time and an end time, and the processing unit 810 is specifically configured to: encode the first data according to the target model within a time period from the start time to the end time.

[0156] In a possible implementation, the processing unit 810 is further configured to: determine the candidate models according to attributes of the first data, the attributes of the first data including at least one of: a data volume, a frame rate, and an application scenario.

[0157] In a possible implementation, the first data is video data, and the candidate models are models for encoding the video data.

[0158] When the communication apparatus 800 is used to implement the function of the second device in the method embodiment shown in FIG. 6, the processing unit 810 is configured to receive, by the transceiver 820, first information from a first device, the first information indicating candidate models, the candidate models being used for data encoding; and then, the processing unit 810 is configured to send, by the transceiver 820, second information to the first device, the second information indicating the target model, the target model being one of the candidate models.

[0159] In a possible implementation, the candidate models are associated with network states, wherein at least two candidate models are associated with different network states; and the processing unit 810 is further configured to select the target model from the candidate models according to a current network state.

[0160] In a possible implementation, the processing unit 810 is specifically configured to select at least two candidate models associated with the current network state from the candidate models; and determine the target model from the at least two candidate models according to priorities of the at least two candidate models.

[0161] In a possible implementation, the first information further indicates the priorities of the at least two candidate models.

[0162] In a possible implementation, the second information further indicates an effective time of the target model.

[0163] In a possible implementation, the effective time includes a start time; or the effective time includes a start time and an effective duration; or the effective time includes a start time and an end time.

[0164] In a possible implementation, the processing unit 810 is further configured to receive, by the transceiver 820, second data from the first device, the second data being obtained by encoding first data according to the target model by the first device, and send the second data to a third device.

[0165] For more detailed description of the processing unit 810 and the transceiver 820, please refer to the related description of the method embodiment shown in FIG. 6, which will not be repeated here.

[0166] As shown in FIG. 9, the communication apparatus 900 includes a processor 910 and an interface circuit 920. The processor 910 and the interface circuit 920 are coupled with each other. It can be understood that the interface circuit 920 can be a transceiver or an input / output interface. Optionally, the communication apparatus 900 can further include a memory 930, used for storing instructions executed by the processor 910 or storing input data required by the processor 910 for executing instructions or storing data generated after the processor 910 executes instructions.

[0167] When the communication apparatus 900 is used to implement the method shown in FIG. 6, the processor 910 is configured to implement the functions of the processing unit 810, and the interface circuit 920 is configured to implement the functions of the transceiver unit 820.

[0168] When the communication apparatus is a terminal chip applied to the first device, the terminal chip implements the functions of the first device in the method embodiments. The terminal chip receives information from other modules (such as a radio frequency module or an antenna) in the first device, and the information is sent by the second device to the terminal chip; or the terminal chip sends information to other modules (such as a radio frequency module or an antenna) in the first device, and the information is sent by the first device to the second device.

[0169] When the communication apparatus is a module applied to the second device, the module implements the functions of the second device in the method embodiments. The module receives information from other modules (such as a radio frequency module or an antenna) in the second device, and the information is sent by the first device to the second device; or the module sends information to other modules (such as a radio frequency module or an antenna) in the second device, and the information is sent by the second device to the first device. The second device module herein can be a baseband chip of the second device, or a DU or other module, and the DU herein can be a DU under an open radio access network (O-RAN) architecture.

[0170] It can be understood that the processor in the embodiments of the present application can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0171] In the present application, another example of a communication device is provided, which comprises at least one processor and at least one memory coupled to the at least one processor, the at least one memory configured to store instructions that, when executed by the at least one processor, cause the communication device to perform the method in the above embodiments. For example, the communication device comprises one processor and one memory, as shown in FIG. 9, the communication device 900 comprises one processor 910 and one memory 930. The processor 910 and the memory 930 are coupled, and the memory 930 stores part or all of the instructions, when the instructions stored in the memory 930 are executed by the processor 910, the communication device 900 performs the method performed by the first device or the second device in the above embodiments. Optionally, the memory can be integrated in the processor 910.

[0172] The method steps in the embodiments of the present application can be implemented in hardware, or in software instructions executable by a processor. The software instructions can be composed of corresponding software modules, which can be stored in a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, a register, a hard disk, a mobile hard disk, a CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor, so that the processor can read information from, and write information to, the storage medium. The storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a network device or a terminal. The processor and the storage medium can also exist as discrete components in the network device or the terminal.

[0173] In the above embodiments, all or part of the embodiments can be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer programs or instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments are performed. The computer can be a general purpose computer, a special purpose computer, a computer network, a network device, a user equipment or other programmable apparatus. The computer programs or instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another computer readable storage medium, for example, the computer programs or instructions can be transferred from one website site, computer, server or data center to another website site, computer, server or data center through wired or wireless manner. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center and the like integrated with one or more available media. The available media can be a magnetic medium, such as a floppy disk, a hard disk, a magnetic tape; or an optical medium, such as a digital video disc; or a semiconductor medium, such as a solid state disk. The computer readable storage medium can be a volatile or non-volatile storage medium, or can include both volatile and non-volatile storage media.

[0174] In various embodiments of the present application, the terms and / or descriptions of different embodiments are consistent and can be referred to each other if there is no special description and logical conflict, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0175] Referring to FIG. 10, a structure schematic diagram of a terminal 1000 is provided in the embodiments of the present application, which can correspond to the first device shown in FIG. 6, and is used to implement the operations of the first device in the above embodiments. As shown in FIG. 10, the terminal includes one or more antennas 1010, a radio frequency processing system 1020, and a processor system 1030.

[0176] In the downlink or sidelink direction, the radio frequency processing system 1020 receives radio frequency signals through the antenna 1010, and sends the signals processed by the radio frequency to the processor system 1030 for further processing. In the uplink or sidelink direction, the processor system 1030 processes the information on the terminal side, and sends it to the radio frequency processing system 1020, which processes the signal by radio frequency and transmits it through the antenna 1010.

[0177] In one example, the radio frequency processing system 1020, which serves as a communication interface for the terminal to communicate with the outside, can include a radio frequency front end 1021 (RFFE) and a radio frequency transceiver 1022. The RFFE 1021 is mainly used for one or more of shaping, passband selection, or gain processing of RF signals received by an antenna or to be transmitted through an antenna, and can include one or more of radio frequency switches, duplexers, filters, power amplifiers, antenna tuning, and low-noise amplifiers. The RFFE 1021 can be circuitry composed of a plurality of discrete devices, or can be integrated and packaged in one or more chips. The radio frequency transceiver 1022 is used to process RF signals received by the RFFE into baseband / intermediate frequency signals for further processing by the processor system 1030, and to process baseband / intermediate frequency signals provided by the processor system 1030 into RF signals for transmission to the RFFE 1021. The baseband / intermediate frequency signals transmitted between the radio frequency transceiver 1022 and the processor system 1030 can be digital signals or analog signals. The radio frequency transceiver 1022 can be implemented by one or more chips, which are commonly referred to as radio frequency chips (RFIC).

[0178] In one example, the processor system 1030 can include one or more processors for processing signals and executing one or more communication protocols. Optionally, the processor system 1030 can further include a memory 1036. In one example, the one or more processors include at least one baseband processor 1031 (also referred to as a modem processor). The memory 1036 is used to store data and / or computer program instructions. Optionally, the processor system 1030 can further include one or more application processors 1032 for implementing processing of the terminal operating system and the application layer. The application processor 1032 can include a GPU, for example. Optionally, the processor system 1030 can further include one or more of a voice subsystem 1033, a multimedia subsystem 1034, or an interface circuit 1035. Among them, the voice subsystem 1033 is used to process voice signals, the multimedia subsystem 1034 is used to process multimedia related operations such as video encoding and decoding, image processing, etc., and the interface circuit 1035 is used to implement communication with other terminal components such as a display 1040, an input device 1050, a memory 1060, etc. The above-mentioned components in the processor system 1030 can communicate with each other through a bus or a communication interface circuit.

[0179] In one example, the processor system 1030 can be packaged as one processor chip, such as a SoC chip or a SIP chip. In one example, the processor system 1030 can be a system composed of multiple chips, for example, the baseband processor 1031 can be packaged as a separate chip, or packaged as a chip with part or all of the circuitry of the radio frequency processing system.

[0180] In one example, the memory 1036 can be an on-chip memory, i.e., located on the chip of the processor system 1030. In one example, the memory 1060 can be an off-chip memory, i.e., located off the chip of the processor system 1030.

[0181] In one example, the baseband processor 1031 can include one or more processor cores 10311 and interface circuitry 10314. The one or more processor cores 10311 are configured to process signals and perform one or more communication protocols. Optionally, the baseband processor 1031 can further include a memory 10312 configured to store at least part of corresponding computer program instructions and / or data. In one example, the one or more processor cores 10311 perform the above-mentioned operations (such as determining the candidate model according to the parameters of the first data) by executing the computer program instructions stored in the memory 10312. In this disclosure, the memory 10312 configured to store corresponding computer program instructions and / or data can mean that the memory 10312 is configured to store all corresponding computer program instructions and / or data for execution by the processor core 10311; or can mean that the memory 10312 is configured to store part of corresponding computer program instructions and / or data, which includes computer program instructions and / or data currently needed for execution by the processor core 10311, and the memory 10312 can store different parts of computer program instructions and / or data for execution by the processor core 10311 multiple times to perform the above-mentioned operations. The interface circuitry 10314 serves as a communication interface to communicate with other components, such as transmitting signals with the radio frequency processing system 1020, communicating with other subsystems and related components of the processor system 1030 through a bus, such as transmitting data control signals with the application processor 1032, and transmitting data or computer program instructions with the memory 1036 or the memory 1060. Optionally, to reduce the load of the processor core, a baseband signal processing circuit 10313 can be further provided to perform at least part of the processing of the baseband signals, including one or more of demodulation, modulation, encoding or decoding of the signals.

[0182] In one example, the communication device provided by the present application can be a terminal 1000, a communication module including a processor system 1030 and a radio frequency system 1020, the processor system 1030, or a baseband processor 1031.

[0183] The above-mentioned processor, processor system, application processor, baseband processor, processor circuit or processor core can be collectively referred to as a processor, which can include one or a combination of a central processing unit (CPU), a digital signal processor (DSP), a microprocessor unit (MPU), a microcontroller unit (MCU), a graphics processing unit (GPU), a field programmable gate array (FPGA), an artificial intelligence processor (AI processor) or a neural processing unit (NPU).

[0184] The above-mentioned memory can include one or more of the following storage media: random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), phase-change memory (PCM), resistive RAM (ReRAM), magnetoresistive RAM (MRAM), ferroelectric RAM (FRAM), cache, register, read-only memory (ROM), flash memory, erasable programmable ROM (EPROM), hard disk, etc. In one example, computer program instructions for implementing the above-embodiments can be stored on a non-volatile memory, such as at least part of the above-mentioned memory 1060 (e.g., one or more of ROM, flash memory, EPROM, or hard disk). During terminal operation, the corresponding computer program instructions can be loaded in whole or in part into a memory with faster transmission speed than the processor, such as at least part of the above-mentioned memory 1036 and / or memory 10312 (e.g., one or more of RAM, SRAM, DRAM, PCM, RERAM, MRAM, FRAM, cache, or register), for execution by the processor to implement the steps in the above-mentioned method embodiments.

[0185] In one example, the radio frequency transceiver 1022 and the radio frequency front end 1021 can also be packaged in one chip. In one example, the radio frequency transceiver 1022, the radio frequency front end 1021, and the baseband processor 1031 can also be packaged in one chip.

[0186] The terms "system" and "network" in the embodiments of the present application can be used interchangeably. "At least one" means one or more, and "multiple" means two or more. The "and / or" describes the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the cases of A alone, A and B together, and B alone, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the front and rear associated objects. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, "at least one of A, B or C" includes A, B, C, AB, AC, BC or ABC, and "at least one of A, B and C" can also be understood to include A, B, C, AB, AC, BC or ABC. In addition, unless otherwise specified, the ordinal numbers "first", "second" and the like mentioned in the embodiments of the present application are used to distinguish a plurality of objects, and are not used to limit the order, time sequence, priority or importance of the plurality of objects.

[0187] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0188] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce the functions specified in one or more flows in the flowcharts and / or one or more blocks in the block diagrams.

[0189] These computer program instructions can also be stored in a computer-readable storage medium that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable storage medium produce a product including instruction means, which implements the functions specified in one or more flows in the flowcharts and / or one or more blocks in the block diagrams.

[0190] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable devices to generate computer-implemented processes, so that the instructions executed on the computer or other programmable devices provide steps for implementing the functions specified in one or more flows in the flowchart and / or one or more blocks in the block diagram.

[0191] Obviously, various modifications and changes can be made to the present application by those skilled in the art without departing from the scope of the present application. Thus, it is intended that the present application embrace all such modifications and changes and, accordingly, the application is not to be limited by the above-described one or more embodiments.

Claims

1. A communication method characterized by comprising: Applied to a first device, the first device being a data sending end device, comprising: sending first information to a second device, the first information indicating candidate models; receiving second information from the second device, the second information indicating a target model, the target model being one of the candidate models; encoding first data according to the target model to obtain second data, and sending the second data to a third device, the third device being a data receiving end device.

2. The method of claim 1, wherein, The candidate models are associated with network states, wherein at least two candidate models are associated with different network states, and the target model is associated with a current network state.

3. The method of claim 2, wherein, The candidate models include at least two candidate models associated with a same network state, and the first information further indicates priorities of the at least two candidate models.

4. The method according to any one of claims 1 to 3, characterized in that, The second information further indicates a validity time of the target model. The encoding of the first data according to the target model comprises: encoding the first data according to the target model within the validity time.

5. The method of claim 4, wherein, The validity time includes a start time, and the encoding of the first data according to the target model within the validity time comprises: encoding the first data according to the target model when the start time is reached; or The validity time includes a start time and a validity duration, and the encoding of the first data according to the target model within the validity time comprises: encoding the first data according to the target model when the start time is reached and within the validity duration; or The validity time includes a start time and an end time, and the encoding of the first data according to the target model within the validity time comprises: encoding the first data according to the target model within a time period from the start time to the end time.

6. The method according to any one of claims 1 to 5, wherein, Before the sending of the first information to the second device, the method further comprises: determining the candidate models according to attributes of the first data, the attributes of the first data including at least one of the following: data volume, frame rate, application scenario.

7. The method according to any one of claims 1 to 6, wherein The first device is an application server, the second device is a network device, and the third device is a terminal device, wherein the network device is a core network device or an access network device; or The first device is a terminal device, the second device is a network device, and the third device is a terminal device, wherein the network device is a core network device or an access network device; or The first device is an application server, the second device is a routing device, and the third device is a terminal device.

8. The method according to any one of claims 1 to 7, wherein, The first data is video data, and the candidate models are models for encoding the video data.

9. A communication method characterized by comprising: Applied to a second device, the second device being a network device, comprising: receiving first information from a first device, the first information indicating candidate models, the candidate models being used for data encoding; sending second information to the first device, the second information indicating a target model, the target model being one of the candidate models.

10. The method of claim 9, wherein, The candidate models are associated with network states, wherein at least two candidate models are associated with different network states; further comprising: The target model is selected from the candidate models according to a current network state.

11. The method of claim 10, wherein, The selecting the target model from the candidate models according to the current network state comprises: selecting at least two candidate models associated with the current network state from the candidate models; determining the target model from the at least two candidate models according to priorities of the at least two candidate models.

12. The method of claim 11, wherein, The first information further indicates the priorities of the at least two candidate models.

13. The method according to any one of claims 9 to 12, wherein, The second information further indicates a validity time of the target model.

14. The method of claim 13, wherein, The validity time comprises a start time; or the validity time comprises a start time and a validity duration; or the validity time comprises a start time and an end time.

15. The method according to any one of claims 9 to 14, wherein, The first device is an application server, and the second device is a network device, wherein the network device is a core network device or an access network device; or The first device is a terminal device, and the second device is a network device, wherein the network device is a core network device or an access network device; or The first device is an application server, and the second device is a routing device.

16. A communications device, characterized by The apparatus comprises a module for performing the method of any one of claims 1 to 8.

17. A communications device, characterized by The apparatus comprises a module for performing the method of any one of claims 9 to 15.

18. A computer program product, characterised in that, The computer program product comprises instructions which, when executed, cause the method of any one of claims 1 to 8, or any one of claims 9 to 15, to be performed.

19. A computer-readable storage medium, characterized in that, The storage medium stores a computer program or instructions which, when executed, implement the method of any one of claims 1 to 8, or any one of claims 9 to 15.

Images