WO2023015499A1 - Wireless communication method and device - Google Patents

Abstract

A wireless communication method and device, which achieves adaptive update of an encoding network and a decoding network when a channel scenario changes, improves adaptive generalization capabilities of the encoding network and the decoding network, and facilitates improvement of compression feedback accuracy of channel information feedback when channel environment features change. The method comprises: when the channel scenario changes from a first channel scenario to a second channel scenario, performing transfer training, according to a target training data set, on a first encoding network deployed on a terminal device and a first decoding network deployed on a network device to obtain a second encoding network and a second decoding network, wherein the target training data set comprises channel data in the second channel scenario, and the first encoding network and the first decoding network adapt to the first channel scenario.

Description

Method and device for wireless communication technical field

The embodiments of the present application relate to the communication field, and in particular to a method and device for wireless communication.

Background technique

In wireless communication, terminal devices can use encoders to compress channel information, and network devices can use decoders to reconstruct channel information. However, due to the increasing complexity of the current channel environment, the channels of different cells also have different potential characteristics. In this case, how to perform channel information feedback is an urgent problem to be solved.

Contents of the invention

The present application provides a wireless communication method and device. When the channel scene is changed, migration training is performed on the encoding network on the terminal device and the decoding network on the network device based on the data set corresponding to the changed channel scene. The adaptive update of the encoding network and the decoding network for channel scene changes is realized, the adaptation and generalization capabilities of the encoding network and the decoding network are improved, and it is beneficial to improve the compression feedback accuracy of channel information feedback when the channel environment characteristics change.

In the first aspect, a wireless communication method is provided, including: when the channel scene is changed from the first channel scene to the second channel scene, the network device performs the first encoding network deployment on the terminal device according to the target training data set Perform migration training with the first decoding network deployed on the network device to obtain a second encoding network and a second decoding network, wherein the target training data set includes channel data in the second channel scenario, and the second an encoding network and the first decoding network are adapted to the first channel scenario;

The network device decodes the target bit stream through the second decoding network to obtain target channel data, where the target bit stream is the target bit stream obtained by the terminal device under the second channel scenario through the second encoding network obtained by encoding the channel data.

In a second aspect, a wireless communication method is provided, including: a network device sends second information to a terminal device, and the second information is used for the terminal device to update the terminal device when the channel scene changes. Migration training is performed on the first encoding network deployed on the network device and the first decoding network deployed on the network device to obtain the second encoding network and the second decoding network, wherein the first encoding network and the first decoding network are adapted to The channel scene before the change is adapted, and the second encoding network and the second decoding network are adapted to the channel scene after the change.

In a third aspect, a wireless communication method is provided, including: when the channel scenario is changed from the first channel scenario to the second channel scenario, the terminal device performs the first channel deployment on the terminal device according to the target training data set. The encoding network and the first decoding network deployed on the network device perform migration training to obtain a second encoding network and a second decoding network, wherein the target training data set includes channel data in the second channel scenario, and the second an encoding network and the first decoding network are adapted to the first channel scenario;

The terminal device encodes the channel data in the second channel scenario through the second encoding network to obtain a target bit stream.

In a fourth aspect, a wireless communication method is provided, including: a terminal device sends first information to a network device, and the first information is used for the network device to deploy information on the terminal device when the channel scene changes. Migration training is performed on the first encoding network and the first decoding network deployed on the network device to obtain the second encoding network and the second decoding network, wherein the first encoding network and the first decoding network are adaptively changed In the previous channel scenario, the second encoding network and the second decoding network adapt to the changed channel scenario. In a fifth aspect, a terminal device is provided, configured to execute the method in any one of the above-mentioned third aspect to the fourth aspect or in each implementation manner thereof.

Specifically, the terminal device includes a functional module configured to execute any one of the third aspect to the fourth aspect or the method in each implementation manner thereof.

In a sixth aspect, a network device is provided, configured to execute the method in any one of the first aspect to the second aspect or each implementation manner thereof.

Specifically, the network device includes a functional module configured to execute any one of the above first aspect to the second aspect or the method in each implementation manner thereof.

In a seventh aspect, a terminal device is provided, including a processor and a memory. The memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory, and execute the functional modules of any one of the above third to fourth aspects or the method in each implementation manner.

In an eighth aspect, a network device is provided, including a processor and a memory. The memory is used to store a computer program, and the processor is used to invoke and run the computer program stored in the memory to execute any one of the above first to second aspects or the method in each implementation manner.

In a ninth aspect, a chip is provided, configured to implement any one of the foregoing first to fourth aspects or the method in each implementation manner thereof.

Specifically, the chip includes: a processor, configured to call and run a computer program from the memory, so that the device installed with the device executes any one of the above-mentioned first to fourth aspects or any of the implementations thereof. method.

In a tenth aspect, there is provided a computer-readable storage medium for storing a computer program, and the computer program causes a computer to execute any one of the above-mentioned first to fourth aspects or the method in each implementation manner thereof.

In an eleventh aspect, a computer program product is provided, including computer program instructions, the computer program instructions causing a computer to execute any one of the above first to fourth aspects or the method in each implementation manner thereof.

A twelfth aspect provides a computer program that, when running on a computer, causes the computer to execute any one of the above first to fourth aspects or the method in each implementation manner.

Through the above technical solution, when the channel scene changes, the network device or terminal device can migrate the encoding network deployed on the terminal device and the decoding network deployed on the network device according to the target training data set corresponding to the changed channel scene Training, so as to realize the adaptive update of the encoding network and the decoding network when the channel scene changes, improve the adaptation and generalization ability of the encoding network and the decoding network, and help improve the compression feedback of the channel information feedback when the channel environment characteristics change precision.

Description of drawings

FIG. 1 is a schematic diagram of a communication system architecture applied in an embodiment of the present application.

Fig. 2 is a schematic diagram of a neural network provided by the present application.

Fig. 3 is a schematic diagram of a convolutional neural network provided in the present application.

Fig. 4 is a schematic diagram of an LSTM unit provided in the present application.

Fig. 5 is a schematic diagram of channel information feedback provided by the present application.

Fig. 6 is a schematic diagram of another channel information feedback provided by the present application.

Fig. 7 is a schematic interaction diagram of a wireless communication method provided according to an embodiment of the present application.

FIG. 8 is a schematic interaction diagram of an example of network equipment performing channel scene migration training provided by an embodiment of the present application.

FIG. 9 is a schematic interaction diagram of another example of channel scene migration training performed by a network device provided in an embodiment of the present application.

FIG. 10 is another schematic interaction diagram of a network device performing channel scene migration training provided by an embodiment of the present application.

FIG. 11 is a schematic interaction diagram of yet another example of network equipment performing channel scene migration training provided by an embodiment of the present application.

Fig. 12 is a schematic interaction diagram of another wireless communication method provided according to an embodiment of the present application.

FIG. 13 is a schematic interaction diagram of an example of channel scene migration training performed by a terminal device provided in an embodiment of the present application.

FIG. 14 is another schematic interaction diagram of a terminal device performing channel scene migration training provided by an embodiment of the present application.

Fig. 15 is a schematic block diagram of a network device provided according to an embodiment of the present application.

Fig. 16 is a schematic block diagram of a terminal device provided according to an embodiment of the present application.

Fig. 17 is a schematic block diagram of another terminal device provided according to an embodiment of the present application.

Fig. 18 is a schematic block diagram of another network device provided according to an embodiment of the present application.

Fig. 19 is a schematic block diagram of a communication device provided according to an embodiment of the present application.

Fig. 20 is a schematic block diagram of a device provided according to an embodiment of the present application.

Fig. 21 is a schematic block diagram of a communication system provided according to an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all of the embodiments. With regard to the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

The technical solution of the embodiment of the present application can be applied to various communication systems, such as: Global System of Mobile communication (Global System of Mobile communication, GSM) system, code division multiple access (Code Division Multiple Access, CDMA) system, broadband code division multiple access (Wideband Code Division Multiple Access, WCDMA) system, General Packet Radio Service (GPRS), Long Term Evolution (LTE) system, Advanced long term evolution (LTE-A) system , New Radio (NR) system, evolution system of NR system, LTE (LTE-based access to unlicensed spectrum, LTE-U) system on unlicensed spectrum, NR (NR-based access to unlicensed spectrum) on unlicensed spectrum unlicensed spectrum (NR-U) system, Non-Terrestrial Networks (NTN) system, Universal Mobile Telecommunications System (UMTS), Wireless Local Area Networks (WLAN), Wireless Fidelity (Wireless Fidelity, WiFi), fifth-generation communication (5th-Generation, 5G) system, sixth-generation communication (6th-Generation, 6G) system or other subsequent evolution communication systems, etc.

Generally speaking, the number of connections supported by traditional communication systems is limited and easy to implement. However, with the development of communication technology, mobile communication systems will not only support traditional communication, but also support, for example, Device to Device (Device to Device, D2D) communication, Machine to Machine (M2M) communication, Machine Type Communication (MTC), Vehicle to Vehicle (V2V) communication, or Vehicle to everything (V2X) communication, etc. , the embodiments of the present application may also be applied to these communication systems.

In some embodiments, the communication system in the embodiment of the present application can be applied to a carrier aggregation (Carrier Aggregation, CA) scenario, can also be applied to a dual connectivity (Dual Connectivity, DC) scenario, and can also be applied to an independent (Standalone, SA ) meshing scene.

In some embodiments, the communication system in the embodiment of the present application can be applied to an unlicensed spectrum, where the unlicensed spectrum can also be considered as a shared spectrum; or, the communication system in the embodiment of the present application can also be applied to a licensed spectrum, Wherein, the licensed spectrum can also be regarded as a non-shared spectrum.

The embodiments of the present application describe various embodiments in conjunction with network equipment and terminal equipment, wherein the terminal equipment may also be referred to as user equipment (User Equipment, UE), access terminal, user unit, user station, mobile station, mobile station, remote station, remote terminal, mobile device, user terminal, terminal, wireless communication device, user agent or user device, etc.

The terminal device can be a station (STATION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (Session Initiation Protocol, SIP) phone, a wireless local loop (Wireless Local Loop, WLL) station, a personal digital assistant (Personal Digital Assistant, PDA) devices, handheld devices with wireless communication functions, computing devices or other processing devices connected to wireless modems, vehicle-mounted devices, wearable devices, next-generation communication systems such as terminal devices in NR networks, or future Terminal equipment in the evolved public land mobile network (Public Land Mobile Network, PLMN) network, etc.

In the embodiment of this application, the terminal device can be deployed on land, including indoor or outdoor, handheld, wearable or vehicle-mounted; it can also be deployed on water (such as ships, etc.); it can also be deployed in the air (such as aircraft, balloons and satellites) superior).

In this embodiment of the application, the terminal device may be a mobile phone (Mobile Phone), a tablet computer (Pad), a computer with a wireless transceiver function, a virtual reality (Virtual Reality, VR) terminal device, an augmented reality (Augmented Reality, AR) terminal Equipment, wireless terminal equipment in industrial control, wireless terminal equipment in self driving, wireless terminal equipment in remote medical, wireless terminal equipment in smart grid , wireless terminal equipment in transportation safety, wireless terminal equipment in smart city, or wireless terminal equipment in smart home.

As an example but not a limitation, in this embodiment of the present application, the terminal device may also be a wearable device. Wearable devices can also be called wearable smart devices, which is a general term for the application of wearable technology to intelligently design daily wear and develop wearable devices, such as glasses, gloves, watches, clothing and shoes. A wearable device is a portable device that is worn directly on the body or integrated into the user's clothing or accessories. Wearable devices are not only a hardware device, but also achieve powerful functions through software support, data interaction, and cloud interaction. Generalized wearable smart devices include full-featured, large-sized, complete or partial functions without relying on smart phones, such as smart watches or smart glasses, etc., and only focus on a certain type of application functions, and need to cooperate with other devices such as smart phones Use, such as various smart bracelets and smart jewelry for physical sign monitoring.

In the embodiment of the present application, the network device may be a device for communicating with the mobile device, and the network device may be an access point (Access Point, AP) in WLAN, a base station (Base Transceiver Station, BTS) in GSM or CDMA , or a base station (NodeB, NB) in WCDMA, or an evolved base station (Evolutional Node B, eNB or eNodeB) in LTE, or a relay station or access point, or a vehicle-mounted device, a wearable device, and an NR network A network device or a base station (gNB) in a network device or a network device in a future evolved PLMN network or a network device in an NTN network.

As an example but not a limitation, in this embodiment of the present application, the network device may have a mobile feature, for example, the network device may be a mobile device. In some embodiments, the network equipment may be a satellite, balloon station. For example, the satellite can be a low earth orbit (low earth orbit, LEO) satellite, a medium earth orbit (medium earth orbit, MEO) satellite, a geosynchronous earth orbit (geosynchronous earth orbit, GEO) satellite, a high elliptical orbit (High Elliptical Orbit, HEO) satellite. ) Satellite etc. In some embodiments, the network device may also be a base station installed on land, in water, or other locations.

In this embodiment of the present application, the network device may provide services for a cell, and the terminal device communicates with the network device through the transmission resources (for example, frequency domain resources, or spectrum resources) used by the cell, and the cell may be a network device ( For example, a cell corresponding to a base station), the cell may belong to a macro base station, or may belong to a base station corresponding to a small cell (Small cell), and the small cell here may include: a metro cell (Metro cell), a micro cell (Micro cell), a pico cell ( Pico cell), Femto cell, etc. These small cells have the characteristics of small coverage and low transmission power, and are suitable for providing high-speed data transmission services.

Exemplarily, a communication system applied in this embodiment of the application may be shown in FIG. 1 . As shown in FIG. 1 , the communication system 100 may include a network device 110, and the network device 110 may be a device for communicating with a terminal device 120 (or called a communication terminal, terminal). The network device 110 can provide communication coverage for a specific geographical area, and can communicate with terminal devices located in the coverage area.

FIG. 1 exemplarily shows one network device and two terminal devices. In some embodiments, the communication system 100 may include multiple network devices and each network device may include other numbers of terminal devices within the coverage area. This embodiment of the present application does not limit it.

In some embodiments, the communication system 100 may further include other network entities such as a network controller and a mobility management entity, which is not limited in this embodiment of the present application.

It should be understood that a device with a communication function in the network/system in the embodiment of the present application may be referred to as a communication device. Taking the communication system 100 shown in FIG. 1 as an example, the communication equipment may include a network equipment 110 and a terminal equipment 120 with communication functions. The network equipment 110 and the terminal equipment 120 may be the specific equipment described above, and will not be repeated here. The communication device may also include other devices in the communication system 100, such as network controllers, mobility management entities and other network entities, which are not limited in this embodiment of the present application.

It should be understood that the terms "system" and "network" are often used interchangeably herein. The term "and/or" in this article is just an association relationship describing associated objects, which means that there can be three relationships, for example, A and/or B can mean: A exists alone, A and B exist simultaneously, and there exists alone B these three situations. In addition, the character "/" in this article generally indicates that the contextual objects are an "or" relationship.

The terms used in the embodiments of the present application are only used to explain specific embodiments of the present application, and are not intended to limit the present application. The terms "first", "second", "third" and "fourth" in the specification and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order . Furthermore, the terms "include" and "have", as well as any variations thereof, are intended to cover a non-exclusive inclusion.

It should be understood that the "indication" mentioned in the embodiments of the present application may be a direct indication, may also be an indirect indication, and may also mean that there is an association relationship. For example, A indicates B, which can mean that A directly indicates B, for example, B can be obtained through A; it can also indicate that A indirectly indicates B, for example, A indicates C, and B can be obtained through C; it can also indicate that there is an association between A and B relation.

In the description of the embodiments of the present application, the term "corresponding" may indicate that there is a direct or indirect correspondence between the two, or that there is an association between the two, or that it indicates and is indicated, configuration and is configuration etc.

In this embodiment of the application, "predefined" or "preconfigured" can be realized by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in devices (for example, including terminal devices and network devices). The application does not limit its specific implementation. For example, pre-defined may refer to defined in the protocol.

In the embodiment of the present application, the "protocol" may refer to a standard protocol in the communication field, for example, may include the LTE protocol, the NR protocol, and related protocols applied to future communication systems, which is not limited in the present application.

In order to facilitate understanding of the technical solutions of the embodiments of the present application, the technical solutions of the present application are described in detail below through specific examples. The following related technologies may be optionally combined with the technical solutions of the embodiments of the present application as optional solutions, and all of them belong to the protection scope of the embodiments of the present application. The embodiment of the present application includes at least part of the following contents.

To facilitate a better understanding of the embodiments of the present application, the channel information feedback related to the present application will be described.

In the NR system, for the channel state information (Channel State Information, CSI) feedback scheme, the eigenvector feedback based on the codebook is mainly used to enable the base station to obtain the downlink CSI. Specifically, the base station sends a downlink channel state information reference signal (Channel State Information Reference Signal, CSI-RS) to the terminal device, and the terminal device uses the CSI-RS to estimate the CSI of the downlink channel, and performs eigenvalue decomposition on the estimated downlink channel, Obtain the eigenvector corresponding to this downlink channel.NR system provides type 1 (Type 1) and type 2 (Type 2) two kinds of codebook design schemes, wherein, Type 1 codebook is used for the CSI feedback of conventional precision and single user multiple Input multiple output (Single User Multiple Input Multiple Output, SU-MIMO) and multiple user multiple input multiple output (Multiple User Multiple Input Multiple Output, MU-MIMO) transmission, Type 2 codebook is used to improve the transmission performance of MU-MIMO Generally, compared with the Type 1 codebook, the Type 2 codebook uses a higher number of feedback bits to obtain higher-precision CSI feedback performance.

In order to facilitate a better understanding of the embodiments of the present application, the neural network and deep learning related to the present application will be described.

A neural network is an operational model composed of multiple neuron nodes connected to each other, in which the connection between nodes represents the weighted value from the input signal to the output signal, called weight; each node performs weighted summation of different input signals (summation, SUM), and output through a specific activation function (f).

A simple neural network is shown in Figure 2, which includes an input layer, a hidden layer, and an output layer. Through different connection methods, weights, and activation functions of multiple neurons, different outputs can be generated, and then fitted from input to output. Mapping relations.

Deep learning uses a deep neural network with multiple hidden layers, which greatly improves the ability of the network to learn features, and can fit complex nonlinear mappings from input to output, so it is widely used in the fields of speech and image processing. In addition to deep neural networks, in the face of different tasks, deep learning also includes common basic structures such as convolutional neural network (CNN), recurrent neural network (Recurrent Neural Network, RNN).

The basic structure of a convolutional neural network includes: an input layer, multiple convolutional layers, multiple pooling layers, a fully connected layer, and an output layer, as shown in Figure 3. Each neuron of the convolution kernel in the convolution layer is locally connected to its input, and the local maximum or average feature of a certain layer is extracted by introducing a pooling layer, which effectively reduces the parameters of the network and mines local features. It enables the convolutional neural network to converge quickly and obtain excellent performance.

RNN is a neural network that models sequence data. It has achieved remarkable results in the field of natural language processing, such as machine translation and speech recognition. The specific performance is that the network memorizes the information of the past moment and uses it in the calculation of the current output, that is, the nodes between the hidden layers are no longer connected but connected, and the input of the hidden layer includes not only the input layer but also the Includes the output of the hidden layer at the previous moment. Commonly used RNNs include structures such as Long Short-Term Memory (LSTM) and gated recurrent unit (GRU). Figure 4 shows a basic LSTM cell structure, which can contain a tanh activation function. Unlike RNN, which only considers the nearest state, the cell state of LSTM will determine which states should be kept and which states should be forgotten, solving the traditional Shortcomings of RNN in long-term memory.

In order to facilitate a better understanding of the embodiments of the present application, the deep learning-based channel information feedback method related to the present application will be described.

In view of artificial intelligence (AI) technology, especially deep learning has achieved great success in computer vision, natural language processing, etc., the field of communication has begun to try to use deep learning to solve technical problems that are difficult to solve by traditional communication methods, such as deep learning. study. The neural network architecture commonly used in deep learning is nonlinear and data-driven. It can extract features from the actual channel matrix data and restore the channel matrix information compressed and fed back by the terminal side as much as possible on the base station side. It is possible to reduce the CSI feedback overhead on the terminal side. The CSI feedback based on deep learning regards the channel information as the image to be compressed, uses the deep learning self-encoder to compress the channel information, and reconstructs the compressed channel image at the sending end, which can preserve the channel information to a greater extent , as shown in Figure 5.

A typical channel information feedback system is shown in FIG. 6 . The entire feedback system is divided into encoder and decoder parts, which are deployed at the sending end and receiving end respectively. After the transmitting end obtains the channel information through channel estimation, the channel information matrix is compressed and encoded through the neural network of the encoder, and the compressed bit stream is fed back to the receiving end through the air interface feedback link, and the receiving end passes the decoder according to the feedback bit stream The channel information is restored to obtain complete feedback channel information. The encoder shown in Figure 6 uses the superposition of multiple fully connected layers, and the design of the convolutional layer and residual structure is used in the decoder. Under the condition that the encoding and decoding framework remains unchanged, the network model structure inside the encoder and decoder can be flexibly designed.

In order to facilitate a better understanding of the embodiments of the present application, technologies and existing problems related to the present application are described.

The channel information feedback in the NR system is a codebook-based feedback scheme. Since this scheme performs quantitative feedback on the channel information according to the preset codebook vector, the codebook itself cannot be adjusted according to the real-time changes of the channel environment, so the codebook feedback obtained by the base station has a large error compared with the original CSI vector, and then Resulting in limited accuracy of CSI recovery.

AI-based channel information feedback considers that the encoder of the AI autoencoder is used to compress the channel information at the sending end, and the decoder of the AI autoencoder is used to reconstruct the channel information at the receiving end. The AI-based solution uses the nonlinear fitting ability of the neural network to compress and feed back the channel information, which can greatly improve the compression efficiency and feedback accuracy. However, due to the increasingly complex channel environment, the channels of different cells also have different potential characteristics. However, the inherent disadvantage of the generalization problem of the neural network itself in practical applications makes the trained network only suitable for the channel test set with the same characteristics as the channel data of the training set, that is, the training set is often difficult to cover all situations, when the scene characteristics change When , it is difficult for the trained model to continue to maintain good generalization performance. Therefore, how to realize the online update of the CSI autoencoder model is an urgent problem to be solved.

Based on the above problems, this application proposes a technical solution. When the channel scene changes, the network device or terminal device performs online scene migration training for the encoding network at the sending end and the decoding network at the receiving end, and realizes the encoding network and decoding network. The adaptive update when the channel scene changes improves the adaptation and generalization capabilities of the encoding network and the decoding network, thereby improving the compression feedback accuracy of the channel information feedback when the channel environment characteristics change.

The technical scheme of the present application is described in detail below through specific examples.

FIG. 7 is a schematic interaction diagram of a wireless communication method 200 provided according to an embodiment of the present application. As shown in FIG. 7, the method 200 includes the following content:

S210. The network device performs migration training on the first encoding network and the first decoding network according to the target training data set to obtain a second encoding network and a second decoding network.

It should be understood that the encoding network in the embodiment of the present application may also be called an encoder, and the decoding network may also be called a decoder. The encoder and decoder can constitute a CSI autoencoder, or a channel information feedback system. That is, the first encoding network may correspond to an encoder in the CSI autoencoder, and the first decoding network may correspond to a decoder in the CSI autoencoder.

In the embodiment of the present application, the encoding network may be deployed on terminal devices, and the decoding network may be deployed on network devices. The terminal device can encode the channel data through the encoding network to obtain the target bit stream, and further send the target bit stream to the network device, and the network device decodes the target bit stream through the decoding network to obtain the target channel data.

In this embodiment of the present application, the first encoding network and the first decoding network correspond to a first channel scenario, or in other words, a first channel environment. In other words, the first encoding network and the first decoding network are adapted to the first channel scenario.

It should be understood that adapting the first encoding network and the first decoding network to the first channel scenario may refer to:

In the first channel scenario, the terminal device encodes the channel data based on the first encoding network to obtain the target bit stream, and the network device decodes the target bit stream based on the first encoding network to obtain the target channel data, which can achieve relatively Excellent CSI feedback and recovery performance. In other words, in the first channel scenario, the autoencoder model based on the first encoding network and the first decoding network can achieve better CSI feedback and recovery performance.

When the channel scene changes, different channel environments may have different potential characteristics, and if the first encoding network and the first decoding network are continued to be used, CSI feedback and restoration performance will be affected.

Therefore, in this embodiment of the application, when the channel scene changes, the network device can perform migration training on the currently used model based on the channel data set corresponding to the changed channel scene, so that the model after migration training can adapt to the changed channel scenarios to improve CSI feedback and recovery performance.

For example, when the channel scene is changed from the first channel scene to the second channel scene, the network device can perform migration training on the currently used first encoding network and first decoding network according to the target training data set to obtain the second encoding network and a second decoding network, wherein the target training data set includes channel data corresponding to a second channel scenario.

Optionally, in some embodiments of the present application, the method 200 further includes:

S220. The terminal device acquires the model parameters of the second coding network from the network device.

For example, after the model training is completed, the network device may send the trained model parameters of the second encoding network to the terminal device.

Optionally, in some embodiments of the present application, the method 200 further includes:

S230, the terminal device may encode the channel data in the second channel scenario through the second encoding network to obtain the target bit stream;

S240, the terminal device sends the target bit stream to the network device.

S250, the network device may decode the target bit stream through the second decoding network to obtain target channel data.

That is to say, when the channel scene changes, the network device can update the currently used model based on the changed channel data set, so that the network device and the terminal device can use the model adapted to the changed channel scene. Compression and feedback of channel data in different channel scenarios is beneficial to improve CSI feedback and restoration performance.

In this embodiment of the present application, the change of the channel scene may be determined by the network device, or may also be determined by the terminal device.

In some implementations, the network device can monitor the channel quality indicator to determine whether the channel scene changes. For example, the network device may continuously or periodically detect the channel quality indicator, and further determine whether the channel scene is changed according to the change of the channel quality indicator.

As an example and not limitation, the channel quality indicator may include at least one of the following: CSI, Reference Signal Receiving Power (RSRP), Reference Signal Receiving Quality (Reference Signal Receiving Quality, RSRQ), received signal Received Signal Strength Indication (RSSI), Signal to Interference plus Noise Ratio (SINR).

For example, the network device determines that the channel scene is changed when the change amount of the channel quality index is greater than the first threshold. As an example, the network device may determine that the channel scene is changed when the change amount of the RSRP is greater than the first RSRP threshold.

Optionally, the first threshold may be determined according to the recovery performance of CSI feedback, for example, when the variation of the channel quality index is greater than a certain value, continue to use the current encoder model and decoder model, resulting in the recovery of CSI feedback The performance becomes worse, or the requirement of feedback accuracy is not met. In this case, this value may be set as the first threshold.

In some embodiments, different channel scenarios may correspond to corresponding channel quality indicator ranges, and the network device may determine the changed channel scenario according to the changed channel quality indicator and the channel quality indicator range.

It should be understood that the above judgment method of channel scene change is only an example, and the channel scene change in the embodiment of the present application may include any situation that causes model incompatibility, for example, the change amount of the channel quality index exceeds a certain threshold, or the terminal device Moving from one cell to another etc., the application is not limited thereto.

Optionally, in some embodiments of the present application, the method 200 further includes:

The network device sends first indication information to the terminal device, where the first indication information is used to indicate that a model is updated and/or a channel scene is changed, where the model update includes a model update of the encoding network and/or Model updates for the decoding network.

It should be understood that the first indication information may be sent through any downlink message or signaling, for example, the first indication information is sent through downlink control information (Downlink Control Information, DCI).

In other implementation manners, the terminal device may monitor the channel quality indicator to determine whether the channel scene changes. For example, the terminal device may continuously or periodically detect the channel quality indicator, and further determine whether the channel scene is changed according to the change of the channel quality indicator.

It should be understood that the manner in which the terminal device monitors the channel quality indicator may refer to the relevant implementation of the network device, and for the sake of brevity, details are not repeated here.

Optionally, in some embodiments of the present application, the method 200 further includes:

The network device receives second instruction information sent by the terminal device, where the second instruction information is used to indicate model update and/or channel scene change, where the model update includes model update of the coding network and/or Or model updates for decoding networks.

It should be understood that the second indication information may be sent through any uplink message or signaling, for example, the second indication information is sent through uplink control information (Uplink Control Information, UCI).

In some embodiments of the present application, the method 200 further includes:

The network device acquires the model parameters of the first coding network from the terminal device.

For example, when the terminal device receives the first indication information sent by the network device, it reports the currently used encoder model to the network device, so that the network device performs migration training according to the encoder model and the decoder model currently used by the network device .

For another example, when the terminal device determines that the channel scene changes by monitoring the channel quality index, it reports the currently used encoder model to the network device, so that the network device performs migration training based on the encoder model and the decoder model currently used by the network device.

It should be understood that, in this embodiment of the application, the data set on which the network device performs migration training can be obtained from the terminal device, or it can also be pre-stored on the network device, and this application does not limit the method of obtaining the data set .

In some embodiments of the present application, the method 200 further includes:

The network device receives the target training data set reported by the terminal device.

For example, when the terminal device receives the first indication information sent by the network device, it reports the target training data set to the network device, so that the network device can perform an evaluation of the encoder model and The decoder model currently used by the network device performs migration training to obtain an encoder model and a decoder model adapted to the changed channel scenario.

For another example, when the terminal device determines that the channel scene changes by monitoring the channel quality index, it reports the target training data set to the network device, so that the network device can use the target training data set to update the encoder model and network currently used by the terminal device. The decoder model currently used by the device performs migration training to obtain an encoder model and a decoder model adapted to the changed channel scenario.

It should be understood that in this embodiment of the application, the model parameters of the first encoding network and the target training data set may be sent through the same message, or may also be sent through different messages, and this application does not make any limited.

It should also be understood that in the embodiment of the present application, the second indication information and the model parameters of the first encoding network may be sent through the same message, or may also be sent through different messages, which is not limited in this application .

For example, when the terminal device determines that the channel scene changes, it may send the second indication information to the network device, and at the same time report the currently used encoder model to the network device.

In some embodiments of the present application, the target training data set includes a CSI vector on at least one frequency domain unit within a first time period after the first channel scene is changed to the second channel scene.

It should be understood that the unit of the first duration may be a subframe, a time slot, or a symbol, or may also be other time units, which are not limited in the present application. As an example, the first duration includes N time slots, where N is a positive integer.

In some embodiments, the N is predefined or configured by the network device, for example, the network device can configure the parameter N through DCI.

It should be understood that the frequency domain unit may be a subband, a physical resource block (Physical Resource Block, PRB), or other frequency domain units, which are not limited in this application.

As an example, the at least one frequency domain unit includes K subbands, where K is a positive integer. In some embodiments, the K is predefined, or configured by the network device, for example, the network device can configure the parameter K through DCI.

As another example, the at least one frequency domain unit includes M PRBs, where M is a positive integer.

In some embodiments, the M is predefined or configured by the network device, for example, the network device can configure the parameter M through DCI.

Since the target training data set is used for model migration training, in order to meet the requirements of online update of the model, higher CSI feedback accuracy is required, and the CSI vector in the target training data set can be obtained based on the first codebook quantization, Wherein, the first codebook has higher precision, for example, the precision of the first codebook is higher than the precision of the Type 2 codebook.

In some embodiments, the first codebook may be a dedicated codebook for the data set for model migration training, that is, only use this codebook for feedback on the data set for model migration training.

In some embodiments, based on the Type 2 codebook, the terminal device may use higher-precision quantization bits to quantize the fed-back amplitude and phase to obtain the target training data set.

In some embodiments, the configuration of the first codebook is sent by the network device through DCI.

In other embodiments of the present application, the target training data set is determined according to at least one data set in multiple data sets prestored on the network device.

In some implementations, the network device can pre-store data sets corresponding to various channel scenarios, such as Line-of-Sight (LoS) scenarios, Non-Line-of-Sight (NLoS) scenarios, indoor scenes, outdoor scenes, low-speed moving scenes, high-speed moving scenes and other scenes corresponding to the data sets.

Further, when the channel scene is changed, the network device may determine the target training data set from the multiple pre-stored data sets according to the changed channel scene.

As an example, the network device may determine samples in the data set corresponding to the changed channel scene as the target training data set.

As another example, the target training data set includes samples in multiple data sets, where each data set corresponds to a corresponding weight, and the weight corresponding to each data set is based on the applicable channel scenario of the data set and the Correlation determination of the above-mentioned changed channel scene. For example, a data set corresponding to a channel scene with a large correlation with the changed channel scene can be given a larger weight, and a data set corresponding to a channel scene with a small correlation with the changed channel scene can be given a smaller weight . That is, the samples with high matching degree of channel scene are given higher weight, and the samples with low matching degree of channel scene are given lower weight.

It should be understood that the changed channel scenario may be learned by the network device by monitoring the channel quality index, or may also be determined according to the third indication information sent by the terminal device, where the third indication information is used to indicate the changed channel scenario, wherein the terminal device can determine the changed channel scenario according to the changed channel quality indicator.

Optionally, the third indication information and the encoder model currently used by the terminal device may be sent through the same message, or may also be sent through different messages, which is not limited in this application.

To sum up, the terminal device may send to the network device first information for assisting the network device in performing model migration training, and the first information may include at least one of the following:

The second indication information is used to indicate that the model is updated and/or the channel scene is changed;

target training dataset;

The third indication information is used to indicate the changed channel scene;

The encoder model currently used by the terminal device.

Based on the above steps, the network device can learn the target training data set used for model migration training, the encoder model currently used by the terminal device, and the decoder model currently used by the network device. Further, the network device can perform migration training on the currently used encoder model and decoder model based on the target training data set. For example, fine-tuning the model parameters, as an example, fixing the parameters of some layers in the network structure and only adjusting the parameters of other layers, etc. This application does not limit the specific adjustment methods. Finally, the updated encoder model and decoder model converge on the target training data set.

It should be understood that in this embodiment of the application, the training rounds on the target training data set during migration training and the training loss function used for migration training, and the migration training parameters such as the training optimizer can be configured by the network device, or can be is predefined.

After the network device completes the online model update, the network device sends the updated encoder model parameters to the terminal device, that is, the network device can send the model parameters of the second encoding network obtained through migration training to the terminal device. Alternatively, after the network device completes the online model update, the terminal device may download the updated encoder model from the network device for subsequent CSI compression feedback.

It should be noted that the embodiment of the present application does not limit the network implementation of the encoder and decoder, and may include at least one network structure such as DNN, CNN, and recurrent neural network (eg, LSTM). For encoders and decoders with different network structures, model updates can be performed according to the technical solutions of the embodiments of the present application.

As an example, the encoder and decoder are implemented by CNN, and the terminal device can encode the channel data as an image to be compressed through CNN to obtain the target bit stream, and further send the target bit stream to the network device. Correspondingly, the network device uses the CNN to use the target bit stream as information obtained by encoding an image to decode the target bit stream to obtain target channel data.

As an example, the encoder and decoder are implemented using a cyclic neural network, and the terminal device can encode each CSI vector in the channel data as an element of the sequence through the cyclic neural network to obtain the target bit stream, and further the target bit stream sent to network devices. Correspondingly, the network device decodes the target bit stream by using the target bit stream as the information obtained by encoding the sequence through the cyclic neural network to obtain the target channel data.

Hereinafter, with reference to FIG. 8 to FIG. 11 , the specific implementation of the model updating method according to the embodiment of the present application will be described.

FIG. 8 is a schematic interaction diagram of an example of migration training performed by a network device provided by an embodiment of the present application. As shown in Figure 8, the following steps may be included:

S301. The terminal device monitors the channel quality index;

For example, the terminal device continuously or periodically monitors the channel quality indicator, and determines whether the channel scene is changed according to the change of the channel quality indicator. For specific implementation, refer to the relevant description of the foregoing embodiments.

S302. When it is determined that the channel scene is changed, the terminal device sends second indication information to the network device, which is used to indicate that the model is updated and/or the channel scene is changed.

S303, the terminal device reports the target training data set and the currently used encoder model to the network device.

Wherein, the target training data set includes channel data corresponding to the changed channel scene, and for the specific implementation of the target training data set, refer to the relevant description of the foregoing embodiments.

Optionally, when it is determined that the channel scene has changed, the terminal device may directly report the current encoder model to the network device without sending the second indication information to the network device, and the network device may receive the encoder model reported by the terminal device. In the case of a model, it may be determined that a model update is required.

S304, the network device performs joint migration training on the encoder model currently used by the terminal device and the decoder model currently used by the network device according to the target training data set, to obtain an updated encoder model and decoder model.

S305. The terminal device downloads the updated encoder model from the network device, or the network device sends the updated encoder model to the terminal device.

The updated model can be adapted to the changed channel scene, and the network device and the terminal device can perform CSI compression feedback based on the updated model when the channel scene does not change.

S306. The terminal device continues to monitor the channel quality index to determine whether the channel scene is changed. If the channel is changed, the above steps may be performed to implement online update of the model.

FIG. 9 is another schematic interaction diagram of a network device performing migration training provided by an embodiment of the present application. As shown in Figure 9, the following steps may be included:

S401, the network device monitors the channel quality index;

For example, the network device monitors the channel quality indicator continuously or periodically, and determines whether the channel scene is changed according to the change of the channel quality indicator. For specific implementation, refer to the relevant description of the foregoing embodiments.

S402. When it is determined that the channel scene is changed, the network device sends first indication information to the terminal device, which is used to indicate that the model is updated and/or the channel scene is changed.

S403, the terminal device reports the target training data set and the currently used encoder model to the network device.

For example, when receiving the first indication information, the terminal device reports the target training data set and the currently used encoder model to the network device. Wherein, the target training data set includes channel data corresponding to the changed channel scene, and for specific implementation, refer to relevant descriptions of the foregoing embodiments.

S404, the network device performs joint migration training on the encoder model currently used by the terminal device and the decoder model currently used by the network device according to the target training data set, to obtain an updated encoder model and decoder model.

S405. The terminal device downloads the updated encoder model from the network device, or the network device sends the updated encoder model to the terminal device. The updated model can be adapted to the changed channel scene, and the network device and the terminal device can perform CSI compression feedback based on the updated model when the channel scene does not change.

S406. The network device continues to monitor the channel quality index to determine whether the channel scene is changed. If the channel is changed, the above steps may be performed to implement online update of the model.

FIG. 10 is another schematic interaction diagram of a network device performing migration training provided by an embodiment of the present application. As shown in Figure 10, the following steps may be included:

S501. The terminal device performs scene monitoring.

For example, a terminal device may perform scene monitoring by monitoring channel quality indicators.

As an example, the terminal device may monitor the channel quality indicator continuously or periodically, and determine whether the channel scene is changed according to the change of the channel quality indicator. For specific implementation, refer to the relevant description of the foregoing embodiments.

S502. When it is determined that the channel scene is changed, the terminal device sends second indication information to the network device, which is used to indicate that the model is updated and/or the channel scene is changed.

S503. The terminal device sends the third indication information and the currently used encoder model to the network device.

Wherein, the third indication information is used to indicate the changed channel scenario.

In this embodiment, the target training data set used by the network device for migration training may be determined according to the pre-stored data set of the network device. For the specific determination method, refer to the relevant description in the previous embodiment, and for the sake of brevity, details are not repeated here.

Optionally, when it is determined that the channel scene changes, the terminal device may not send the second indication information to the network device, but directly send the third indication information and the current encoder model to the network device, and the network device may 3. Indicate the information and/or the encoder model reported by the terminal device, and determine that a model update is required.

S504. The network device performs joint migration training on the encoder model currently used by the terminal device and the decoder model currently used by the network device according to the target training data set, to obtain an updated encoder model and a decoder model.

S505. The terminal device downloads the updated encoder model from the network device, or the network device sends the updated encoder model to the terminal device.

The updated model can be adapted to the changed channel scene, and the network device and the terminal device can perform CSI compression feedback based on the updated model when the channel scene does not change.

S506, the terminal device continues to monitor the scene to determine whether the channel scene changes, and if the channel changes, the above steps can be performed to implement online update of the model.

FIG. 11 is a schematic interaction diagram of yet another example of migration training performed by a network device provided in an embodiment of the present application. As shown in Figure 11, the following steps may be included:

S601. The network device performs scene monitoring.

For example, a network device can perform scenario monitoring by monitoring channel quality indicators.

As an example, the network device may continuously or periodically monitor the channel quality indicator, and determine whether the channel scene is changed according to the change of the channel quality indicator.

S602. When it is determined that the channel scene is changed, the network device sends first indication information to the terminal device, which is used to indicate that the model is updated and/or the channel scene is changed.

S603. The terminal device sends the currently used encoder model to the network device.

In this embodiment, the target training data set used by the network device for migration training may be determined according to the pre-stored data set of the network device. For the specific determination method, refer to the relevant description in the previous embodiment, and for the sake of brevity, details are not repeated here.

S604. The network device performs joint migration training on the encoder model currently used by the terminal device and the decoder model currently used by the network device according to the target training data set, to obtain an updated encoder model and a decoder model.

S605. The terminal device downloads the updated encoder model from the network device, or the network device sends the updated encoder model to the terminal device.

The updated model can be adapted to the changed channel scene, and the network device and the terminal device can perform CSI compression feedback based on the updated model when the channel scene does not change.

S606, the network device continues to monitor the scene to determine whether the channel scene changes, and if the channel changes, the above steps can be performed to implement online update of the model.

To sum up, in the embodiment of this application, the network device can update the encoder model and decoder model online according to the data set corresponding to the changed channel scene according to the change of the wireless channel scene, and support the target training data set, encoding The air interface transmission of the decoder model and the decoder model enables the updated model to adapt to the changed channel environment, thereby achieving better CSI feedback and restoration performance.

FIG. 12 is a schematic interaction diagram of another wireless communication method 1000 provided according to an embodiment of the present application. As shown in FIG. 12, the method 1000 includes the following content:

S1010, the terminal device performs transfer training on the first encoding network and the first decoding network according to the target training data set, to obtain the second encoding network and the second decoding network.

It should be understood that the encoding network in the embodiment of the present application may also be called an encoder, and the decoding network may also be called a decoder. The encoder and decoder can constitute a CSI autoencoder, or in other words, a channel information feedback system. That is, the first encoding network may correspond to an encoder in the CSI autoencoder, and the first decoding network may correspond to a decoder in the CSI autoencoder.

In the embodiment of the present application, the encoding network may be deployed on terminal devices, and the decoding network may be deployed on network devices. The terminal device can encode the channel data through the encoding network to obtain the target bit stream, and further send the target bit stream to the network device, and the network device decodes the target bit stream through the decoding network to obtain the target channel data.

In some embodiments, the first encoding network and the first decoding network correspond to a first channel scenario, or in other words, a first channel environment. In other words, the first encoding network and the first decoding network are adapted to the first channel scenario.

It should be understood that adapting the first encoding network and the first decoding network to the first channel scenario may refer to:

In the first channel scenario, the terminal device encodes the channel data based on the first encoding network to obtain the target bit stream, and the network device decodes the target bit stream based on the first encoding network to obtain the target channel data, which can achieve relatively Excellent CSI feedback and recovery performance. In other words, in the first channel scenario, the autoencoder model based on the first encoding network and the first decoding network can achieve better CSI feedback and recovery performance.

When the channel scene changes, how to continue to use the first encoding network and the first decoding network will affect CSI feedback and restoration performance. Therefore, in the embodiment of the present application, when the channel scene changes, the terminal device can perform migration training on the currently used model based on the channel data set corresponding to the changed channel scene, so that the model after migration training can adapt to the changed channel scenarios to improve CSI feedback and recovery performance.

For example, when the channel scene is changed from the first channel scene to the second channel scene, the terminal device can perform migration training on the first encoding network and the first decoding network according to the target training data set to obtain the second encoding network and the second A decoding network, wherein the target training data set includes channel data corresponding to a second channel scenario.

Optionally, the method 1000 also includes:

S1020. The terminal device sends the model parameters of the second decoding network to the network device.

For example, after the model training is completed, the terminal device may send the trained model parameters of the second decoding network to the network device.

Optionally, the method 1000 also includes:

S1030, the terminal device may encode the channel data in the second channel scenario through the second encoding network to obtain the target bit stream;

S1040. The terminal device sends the target bit stream to the network device.

S1050, the network device may decode the target bit stream through the second decoding network to obtain target channel data.

That is to say, when the channel scene changes, the terminal device can update the currently used model based on the channel data set corresponding to the changed channel scene, so that the network device and the terminal device can use the model adapted to the changed channel scene. The model compresses and feeds back the channel data in the changed channel scenario, which is beneficial to improve the CSI feedback and recovery performance.

In some embodiments, the network device may send second information to the terminal device, where the second information is used to assist the terminal device in performing model migration training.

In this embodiment of the present application, the change of the channel scene may be determined by the network device, or may also be determined by the terminal device.

In some implementations, the network device can monitor the channel quality indicator to determine whether the channel scene changes. For example, the network device may continuously or periodically detect the channel quality indicator, and further determine whether the channel scene is changed according to the change of the channel quality indicator.

As an example but not a limitation, the channel quality indicator may include at least one of the following: CSI, RSRP, RSRQ, RSSI, and SINR.

For example, the network device determines that the channel scene is changed when the change amount of the channel quality index is greater than the first threshold. As an example, the network device may determine that the channel scene is changed when the change amount of the RSRP is greater than the first RSRP threshold.

In some embodiments, different channel scenarios may correspond to corresponding channel quality indicator ranges, and the network device may determine the changed channel scenario according to the changed channel quality indicator and the channel quality indicator range.

It should be understood that the above judgment method of channel scene change is only an example, and the channel scene change in the embodiment of the present application may include any scene that causes model incompatibility, for example, the change amount of the channel quality index exceeds a certain threshold, or the terminal device Moving from one cell to another etc., the application is not limited thereto.

Optionally, in some embodiments of the present application, the method 200 further includes:

The network device sends first indication information to the terminal device, where the first indication information is used to indicate that a model is updated and/or a channel scene is changed, where the model update includes a model update of the encoding network and/or Model updates for the decoding network.

In some embodiments, the second information may include the first indication information.

It should be understood that the first indication information may be sent through any downlink message or signaling, for example, the first indication information is sent through DCI.

In other implementation manners, the terminal device may monitor the channel quality indicator to determine whether the channel scene changes. For example, the terminal device may continuously or periodically detect the channel quality indicator, and further determine whether the channel scene is changed according to the change of the channel quality indicator.

It should be understood that the manner in which the terminal device monitors the channel quality indicator may refer to the relevant implementation of the network device, and for the sake of brevity, details are not repeated here.

Optionally, in some embodiments of the present application, the method 200 further includes:

The terminal device sends second indication information to the network device, where the second indication information is used to indicate that a model is updated and/or a channel scene is changed, where the model update includes a model update of the coding network and/or Model updates for the decoding network.

It should be understood that the second indication information may be sent through any uplink message or signaling, for example, the second indication information is sent through UCI.

In some embodiments of the present application, the method 200 further includes:

The terminal device acquires the model parameters of the first decoding network from the network device.

For example, the terminal device acquires the decoder model currently used by the network device from the network device when receiving the first indication information sent by the network device.

For another example, when the terminal device determines that the channel scene changes by monitoring the channel quality index, it acquires the decoder model currently used by the network device from the network device.

For another example, the network device may send the currently used decoder model to the terminal device when it detects that the channel scene changes, or when receiving the second indication information sent by the terminal device.

In some embodiments, the second information includes model parameters of the first decoding network.

In some embodiments, when the network device detects that the channel scene changes, the first instruction information may not be sent to the terminal device, but the currently used encoder model may be directly sent to the terminal device, and the terminal device may use the coder model sent by the network device to The model determines that a model update is required.

It should be understood that, in this embodiment of the application, the data set on which the terminal device performs migration training can be obtained in real time from the terminal device, or it can also be pre-stored on the terminal device. The method of obtaining the data set in this application Not limited.

For example, the terminal device collects the target training data set when receiving the first indication information sent by the network device. For another example, the terminal device collects the target training data set when it determines that the channel scene changes by monitoring the channel quality index.

In some embodiments of the present application, the target training data set includes a CSI vector on at least one frequency domain unit within a first time period after the first channel scene is changed to the second channel scene.

It should be understood that the unit of the first duration may be a subframe, a time slot, a symbol, or other time units, which is not limited in the present application. As an example, the first duration includes N time slots, where N is a positive integer.

In some embodiments, the N is predefined or configured by the network device, for example, the network device can configure the parameter N through DCI.

It should be understood that the frequency domain unit may be a subband, a physical resource block (Physical Resource Block, PRB), or other frequency domain units, which are not limited in this application.

As an example, the at least one frequency domain unit includes K subbands, where K is a positive integer. In some embodiments, the K is predefined, or configured by the network device, for example, the network device can configure the parameter K through DCI.

As another example, the at least one frequency domain unit includes M PRBs, where M is a positive integer.

In some embodiments, the M is predefined or configured by the network device, for example, the network device can configure the parameter M through DCI.

Since the target training data set is used for model migration training, in order to meet the requirements of online update of the model, higher CSI feedback accuracy is required, and the CSI vector in the target training data set can be obtained based on the first codebook quantization, Wherein, the first codebook has higher precision, for example, the precision of the first codebook is higher than the precision of the Type 2 codebook.

In some embodiments, the first codebook may be a dedicated codebook for the data set for model migration training, that is, only use this codebook for feedback on the data set for model migration training.

In some embodiments, based on the Type 2 codebook, the terminal device may use higher-precision quantization bits to quantize the fed-back amplitude and phase to obtain the target training data set.

In some embodiments, the configuration of the first codebook is sent by the network device through DCI.

In other embodiments of the present application, the target training data set is determined according to at least one data set in multiple data sets prestored on the terminal device.

In some implementations, the terminal device may pre-store data sets corresponding to multiple channel scenarios, such as LoS scenarios, NLoS scenarios, indoor scenarios, outdoor scenarios, low-speed moving scenarios, high-speed moving scenarios and other scenarios.

Further, the terminal device may determine a target training data set from the multiple pre-stored data sets according to the changed channel scenario. As an example, the terminal device may determine samples in the data set corresponding to the changed channel scene as the target training data set. As another example, the target training data set includes samples in multiple data sets, where each data set corresponds to a corresponding weight, and the weight corresponding to each data set is based on the applicable channel scenario of the data set and the Correlation determination of the above-mentioned changed channel scene. For example, a data set corresponding to a channel scene with a large correlation with the changed channel scene can be given a larger weight, and a data set corresponding to a channel scene with a small correlation with the changed channel scene can be given a smaller weight . That is, the samples with high matching degree of channel scene are given higher weight, and the samples with low matching degree of channel scene are given lower weight.

It should be understood that the changed channel scenario may be known by the terminal device by monitoring the channel quality index, or it may be determined according to the fourth indication information sent by the network device, where the fourth indication information is used to indicate the changed channel scenario, wherein the network device can determine the changed channel scenario according to the changed channel quality index.

In summary, the second information sent by the network device to the terminal device for assisting the terminal device in performing model migration training may include at least one of the foregoing first indication information, the first decoding network, and the fourth indication information.

So far, the terminal device can know the encoder model currently used by the terminal device and the decoder model currently used by the network device, as well as the target training data set used for model migration training. Further, the terminal device can perform transfer training on the currently used encoder model and decoder model based on the target training data set. For example, by fine-tuning the model parameters, as an example, the parameters of some layers in the network structure are fixed, and only the parameters of other layers are adjusted. This application does not limit the specific adjustment method. Finally, the updated encoder model and decoder model converge on the target training data set.

It should be understood that in this embodiment of the application, the training rounds on the target training data set during migration training and the training loss function used for migration training, and the migration training parameters such as the training optimizer can be configured by the network device, or can be is predefined.

After the terminal device completes the online update of the model, the terminal device sends the updated model parameters of the decoder to the network device, that is, the terminal device can send the model parameters of the second decoding network obtained through migration training to the network device. Alternatively, after the terminal device completes the online model update, the network device may download the updated decoder model from the terminal device side.

It should be noted that the embodiment of the present application does not limit the network implementation of the encoder and decoder, and may include at least one network structure such as DNN, CNN, and recurrent neural network (eg, LSTM). For encoders and decoders with different network structures, the model can be updated according to the method described in this application.

As an example, the encoder and decoder are implemented using CNN, and the terminal device can encode the channel data as an image to be compressed through CNN to obtain the target bit stream. Correspondingly, the network device uses CNN to encode the target bit stream as The information obtained by encoding the image is used to decode the target bit stream to obtain target channel data.

As an example, the encoder and decoder are implemented using a cyclic neural network, and the terminal device can encode each CSI vector in the channel data as an element of the sequence through the cyclic neural network to obtain a target bit stream. Correspondingly, the network device decodes the target bit stream by using the target bit stream as the information obtained by encoding the sequence through the cyclic neural network to obtain the target channel data.

Hereinafter, with reference to FIG. 13 to FIG. 14 , the specific implementation of the model updating method according to the embodiment of the present application will be described.

FIG. 13 is a schematic interaction diagram of an example of migration training performed by a terminal device according to an embodiment of the present application. As shown in Figure 13, the following steps may be included:

S701. The terminal device monitors a channel quality index.

For example, the terminal device monitors the channel quality indicator continuously or periodically, and determines whether the channel scene is changed according to the change of the channel quality indicator.

S702. When it is determined that the channel scene is changed, the terminal device sends second indication information to the network device, which is used to indicate that the model is updated and/or the channel scene is changed.

S703. The terminal device acquires the decoder model currently used by the network device from the network device.

Optionally, in some embodiments, after receiving the second indication information sent by the terminal device, the network device may send the currently used decoder model to the terminal device.

S704, the terminal device performs joint migration training on the encoder model currently used by the terminal device and the decoder model currently used by the network device according to the target training data set, to obtain an updated encoder model and decoder model.

S705. The terminal device sends the updated decoder model to the network device.

For example, after the terminal device completes the model training, it can upload the updated decoder model to the network device.

The updated model can be adapted to the changed channel scene, and when the channel scene does not change, the network device and the terminal device can perform CSI compression feedback based on the updated model.

S706. The terminal device continues to monitor the channel quality index to determine whether the channel scene is changed. If the channel is changed, the above steps may be performed to implement online update of the model.

FIG. 14 is a schematic interaction diagram of an example of migration training performed by a terminal device according to an embodiment of the present application. As shown in Figure 14, the following steps may be included:

S801. The network device monitors the channel quality index.

For example, the network device monitors the channel quality indicator continuously or periodically, and determines whether the channel scene is changed according to the change of the channel quality indicator.

S802. In the case of determining that the channel scene is changed, the network device sends first indication information to the terminal device, which is used to indicate that the model is updated and/or the channel scene is changed.

S803. The terminal device acquires the currently used decoder model from the network device.

In some embodiments, when the network device determines that the channel scene changes, it may directly send the currently used decoder model to the terminal device without sending the first indication information, and the terminal device , it can be determined that a model update is required.

S804, the terminal device performs joint migration training on the encoder model currently used by the terminal device and the decoder model currently used by the network device according to the target training data set, to obtain an updated encoder model and decoder model.

S805. The terminal device sends the updated decoder model to the network device.

The updated model can be adapted to the changed channel scene, and when the channel scene does not change, the network device and the terminal device can perform CSI compression feedback based on the updated model.

S806. The network device continues to monitor the channel quality index to determine whether the channel scene is changed. If the channel is changed, the above steps may be performed to implement online update of the model.

To sum up, in the embodiment of this application, the terminal device can update the encoder model and decoder model online according to the data set corresponding to the changed channel scene according to the change of the wireless channel scene, and support the target training data set, encoding The air interface transmission of the decoder model and the decoder model enables the updated model to adapt to the changed channel environment, thereby achieving better CSI feedback and restoration performance.

The method embodiment of the present application is described in detail above in conjunction with FIG. 7 to FIG. 14 , and the device embodiment of the present application is described in detail below in conjunction with FIG. 15 to FIG. 20 . It should be understood that the device embodiment and the method embodiment correspond to each other, similar to The description can refer to the method embodiment.

Fig. 15 is a schematic block diagram of a network device according to an embodiment of the present application. The network device 1100 of Figure 15 includes:

The processing unit 1110 is configured to, when the channel scene is changed from the first channel scene to the second channel scene, according to the target training data set, the first encoding network deployed on the terminal device and the first decoding network deployed on the network device The network performs migration training to obtain a second encoding network and a second decoding network, wherein the target training data set includes channel data in the second channel scenario, and the first encoding network and the first decoding network are adapted to Configure the first channel scenario.

In some embodiments of the present application, the processing unit 1110 is further configured to:

Monitor the channel quality index, and determine whether the channel scene is changed according to the change amount of the channel quality index.

In some embodiments of the present application, the processing unit 1110 is further configured to:

If the change amount of the channel quality index is greater than the first threshold, it is determined that the channel scene is changed.

In some embodiments of the present application, the network device 1110 further includes:

A communication unit, configured to send first indication information to the terminal device, where the first indication information is used to indicate that a model is updated and/or a channel scene is changed, wherein the model update includes an encoding network model update and/or Or model updates for decoding networks.

In some embodiments of the present application, the first indication information is sent through downlink control information DCI.

In some embodiments of the present application, the network device 1100 further includes:

A communication unit, configured to receive second indication information sent by the terminal device, where the second indication information is used to indicate that a model is updated and/or a channel scene is changed, where the model update includes a model update of the coding network and /or model updates for the decoding network.

In some embodiments of the present application, the second indication information is sent through uplink control information UCI.

In some embodiments of the present application, the network device 1100 further includes:

A communication unit, configured to acquire the model parameters of the first encoding network from the terminal device.

In some embodiments of the present application, the network device 1100 further includes:

A communication unit, configured to receive the target training data set reported by the terminal device.

In some embodiments of the present application, the target training data set includes channel state information CSI vectors on at least one frequency domain unit within a first time period after the first channel scene is changed to the second channel scene.

In some embodiments of the present application, the CSI vector is obtained through quantization based on the first codebook, and the precision of the first codebook is higher than the precision of the type 2 codebook.

In some embodiments of the present application, the configuration of the first codebook is sent by the network device through downlink control information DCI.

In some embodiments of the present application, the first duration includes N time slots, and the at least one frequency domain unit includes K subbands or M PRBs, where N is a positive integer, and the K is a positive integer, and the M is a positive integer.

In some embodiments of the present application, the N is predefined or configured by the network device;

The K is predefined or configured by the network device;

The M is predefined or configured by the network device.

In some embodiments of the present application, the processing unit 1110 is further configured to:

According to the changed channel scene, the target training data set is determined in multiple prestored data sets, where each data set in the multiple data sets corresponds to a corresponding channel scene.

In some embodiments of the present application, the network device 1100 further includes:

A communication unit, configured to receive third indication information sent by the terminal device, where the third indication information is used to indicate a changed channel scenario.

In some embodiments of the present application, the target training data set only includes the data set corresponding to the second channel scene; or

The target training data set includes a plurality of data sets, wherein each data set corresponds to a corresponding weight, and the weight corresponding to each data set is based on the correlation between the channel scene applicable to the data set and the second channel scene gender certainty.

In some embodiments of the present application, the network device 1100 further includes:

A communication unit, configured to send the model parameters of the second encoding network obtained through migration training to the terminal device.

In some embodiments of the present application, the network device 1100 further includes:

A communication unit, configured to receive a target bit stream sent by the terminal device, where the target bit stream is obtained by the terminal device encoding channel data in the second channel scenario through the second encoding network.

In some embodiments of the present application, the processing unit 1110 is further configured to:

The target bit stream is decoded by the second decoding network to obtain target channel data.

Optionally, in some embodiments, the above-mentioned communication unit may be a communication interface or a transceiver, or an input-output interface of a communication chip or a system-on-chip. The aforementioned processing unit may be one or more processors.

It should be understood that the network device 1100 according to the embodiment of the present application may correspond to the network device in the method embodiment of the present application, and the above-mentioned and other operations and/or functions of each unit in the network device 1100 are to realize the For the sake of brevity, the corresponding flow of the network device in the shown method 200 is not repeated here.

Fig. 16 shows a schematic block diagram of a terminal device 1200 according to an embodiment of the present application. As shown in Figure 16, the terminal device 1200 includes:

The processing unit 1210 is configured to, when the channel scene is changed from the first channel scene to the second channel scene, according to the target training data set, the first encoding network deployed on the terminal device and the first decoding network deployed on the network device The network performs migration training to obtain a second encoding network and a second decoding network, wherein the target training data set includes channel data in the second channel scenario, and the first encoding network and the first decoding network are adapted to Configure the first channel scenario.

In some embodiments of the present application, the processing unit 1210 is further configured to:

Monitor the channel quality index, and determine whether the channel scene is changed according to the change amount of the channel quality index.

In some embodiments of the present application, the processing unit 1210 is further configured to:

If the change amount of the channel quality index is greater than the second threshold, it is determined that the channel scene is changed.

In some embodiments of the present application, the terminal device 1200 further includes:

A communication unit, configured to send second indication information to the network device, where the second indication information is used to indicate that a model is updated and/or a channel scene is changed, where the model update includes an encoding network model update and/or Or model updates for decoding networks.

In some embodiments of the present application, the second indication information is sent through uplink control information UCI.

In some embodiments of the present application, the terminal device 1200 further includes:

A communication unit, configured to receive the first instruction information sent by the network device, for instructing to update the model and/or change the channel scene, wherein the update of the model includes an update of the model of the encoding network and/or a model of the decoding network renew.

In some embodiments of the present application, the first indication information is sent through downlink control information DCI.

In some embodiments of the present application, the target training data set includes channel state information CSI vectors on at least one frequency domain unit within a first time period after the first channel scene is changed to the second channel scene.

In some embodiments of the present application, the CSI vector is obtained through quantization based on the first codebook, and the precision of the first codebook is higher than the precision of the type 2 codebook.

In some embodiments of the present application, the configuration of the first codebook is sent by the network device through downlink control information DCI.

In some embodiments of the present application, the first duration includes N time slots, and the at least one frequency domain unit includes K subbands or M PRBs, where N is a positive integer, and the K is a positive integer, and the M is a positive integer.

In some embodiments of the present application, the N is predefined or configured by the network device;

The K is predefined or configured by the network device;

The M is predefined or configured by the network device.

In some embodiments of the present application, the terminal device 1200 further includes:

A communication unit, configured to acquire model parameters of the first decoding network from the network device.

In some embodiments of the present application, the terminal device 1200 further includes:

A communication unit, configured to send the model parameters of the second decoding network obtained through migration training to the network device.

In some embodiments of the present application, the processing unit 1200 is also used to:

The channel data in the second channel scenario is encoded by the second encoding network to obtain a target bit stream.

In some embodiments of the present application, the terminal device 1200 further includes: a communication unit, configured to send the target bit stream to the network device.

Optionally, in some embodiments, the above-mentioned communication unit may be a communication interface or a transceiver, or an input-output interface of a communication chip or a system-on-chip. The aforementioned processing unit may be one or more processors.

It should be understood that the terminal device 1200 according to the embodiment of the present application may correspond to the terminal device in the method embodiment of the present application, and the above-mentioned and other operations and/or functions of each unit in the terminal device 1200 are to realize the For the sake of brevity, the corresponding process of the terminal device in the shown method 200 will not be repeated here.

Fig. 17 shows a schematic block diagram of a terminal device 1300 according to an embodiment of the present application. As shown in Figure 17, the terminal device 1300 includes:

The communication unit 1310 is configured to send first information to the network device, where the first information is used for the network device to update the first encoding network deployed on the terminal device and the network device on the network device when the channel scene changes. The deployed first decoding network performs migration training to obtain a second encoding network and a second decoding network, wherein the first encoding network and the first decoding network adapt to the channel scene before the change, and the second encoding network Adapting to the changed channel scenario with the second decoding network.

In some embodiments of the present application, the terminal device 1310 further includes: a processing unit configured to monitor a channel quality indicator, and determine whether a channel scene is changed according to a change amount of the channel quality indicator.

In some embodiments of the present application, the processing unit is also used for:

If the change amount of the channel quality index is greater than the second threshold, it is determined that the channel scene is changed.

In some embodiments of the present application, the first information includes second indication information, and the second indication information is used to indicate that the model is updated and/or the channel scene is changed, wherein the model update includes the encoding network Model update and/or model update of the decoding network.

In some embodiments of the present application, the second indication information is sent through uplink control information UCI.

In some embodiments of the present application, the communication unit 1310 is also used to:

receiving first instruction information sent by the network device, where the first instruction information is used to indicate model update and/or channel scene change, where the model update includes model update of the encoding network and/or model update of the decoding network Model updates.

In some embodiments of the present application, the first indication information is sent through downlink control information DCI.

In some embodiments of the present application, the first information includes model parameters of the first encoding network.

In some embodiments of the present application, the first information includes a target training data set, and the target training data set includes channel data in a changed channel scenario.

In some embodiments of the present application, the target training data set includes channel state information CSI vectors on at least one frequency domain unit within the first time period after the channel scene is changed.

In some embodiments of the present application, the CSI vector is obtained by quantization based on the first codebook, and the precision of the first codebook is higher than that of the type 2 codebook.

In some embodiments of the present application, the configuration of the first codebook is sent by the network device through DCI.

In some embodiments of the present application, the first duration includes N time slots, and the at least one frequency domain unit includes K subbands or M PRBs, where N is a positive integer, and the K is a positive integer, and the M is a positive integer.

In some embodiments of the present application, the N is predefined or configured by the network device;

The K is predefined or configured by the network device;

The M is predefined or configured by the network device.

In some embodiments of the present application, the first information includes third indication information, and the third indication information is used to indicate a changed channel scenario.

In some embodiments of the present application, the communication unit 1310 is also used to:

Acquiring the model parameters of the second coding network obtained through migration training from the network device.

In some embodiments of the present application, the terminal device 1300 further includes: a processing unit, configured to use the second encoding network to encode the channel data in the second channel scenario to obtain a target bit stream.

In some embodiments of the present application, the communication unit 1310 is also used to:

sending the target bitstream to the network device.

Optionally, in some embodiments, the above-mentioned communication unit may be a communication interface or a transceiver, or an input-output interface of a communication chip or a system-on-chip. The aforementioned processing unit may be one or more processors.

It should be understood that the terminal device 1300 according to the embodiment of the present application may correspond to the terminal device in the method embodiment of the present application, and the above-mentioned and other operations and/or functions of each unit in the terminal device 1300 are to realize the For the sake of brevity, the corresponding process of the terminal device in the shown method 1000 is not repeated here.

Fig. 18 shows a schematic block diagram of a network device 1800 according to an embodiment of the present application. As shown in Figure 18, the network device 1800 includes:

The communication unit 1810 is configured to send second information to the terminal device, where the second information is used for the terminal device to update the first encoding network deployed on the terminal device and the network when the channel scene changes. The first decoding network deployed on the device performs migration training to obtain a second encoding network and a second decoding network, wherein the first encoding network and the first decoding network adapt to the channel scene before the change, and the second The encoding network and the second decoding network are adapted to the changed channel scenario.

In some embodiments of the present application, the network device 1800 further includes: a processing unit configured to monitor a channel quality indicator, and determine whether a channel scene is changed according to a change amount of the channel quality indicator.

In some embodiments of the present application, the second information includes first indication information, and the first indication information is used to indicate that a model is updated and/or a channel scene is changed, wherein the model update includes a model of the coding network Update and/or model updates for the decoding network.

In some embodiments of the present application, the first indication information is sent through downlink control information DCI.

In some embodiments of the present application, the second information includes model parameters of the first decoding network.

In some embodiments of the present application, the communication unit 1810 is also used to:

Acquire model parameters of the second decoding network from the terminal device.

In some embodiments of the present application, the network device 1800 further includes: a processing unit, configured to decode the target bit stream through the second decoding network to obtain target channel data, wherein the target bit stream is the It is obtained by the terminal device by encoding the channel data in the second channel scenario through the second encoding network.

Optionally, in some embodiments, the above-mentioned communication unit may be a communication interface or a transceiver, or an input-output interface of a communication chip or a system-on-chip. The aforementioned processing unit may be one or more processors.

It should be understood that the network device 1800 according to the embodiment of the present application may correspond to the network device in the method embodiment of the present application, and the above-mentioned and other operations and/or functions of each unit in the network device 1800 are to realize the For the sake of brevity, the corresponding flow of the network device in the shown method 1000 is not repeated here.

FIG. 19 is a schematic structural diagram of a communication device 1400 provided by an embodiment of the present application. The communication device 1400 shown in FIG. 19 includes a processor 1410, and the processor 1410 can call and run a computer program from a memory, so as to implement the method in the embodiment of the present application.

Optionally, as shown in FIG. 19 , the communication device 1400 may further include a memory 1420 . Wherein, the processor 1410 can invoke and run a computer program from the memory 1420, so as to implement the method in the embodiment of the present application.

Wherein, the memory 1420 may be an independent device independent of the processor 1410 , or may be integrated in the processor 1410 .

Optionally, as shown in FIG. 19, the communication device 1400 may further include a transceiver 1430, and the processor 1410 may control the transceiver 1430 to communicate with other devices, specifically, to send information or data to other devices, or to receive other Information or data sent by the device. Wherein, the transceiver 1430 may include a transmitter and a receiver. The transceiver 1430 may further include antennas, and the number of antennas may be one or more.

Optionally, the communication device 1400 may specifically be the network device of the embodiment of the present application, and the communication device 1400 may implement the corresponding processes implemented by the network device in each method of the embodiment of the present application. For the sake of brevity, details are not repeated here. .

Optionally, the communication device 1400 may specifically be the mobile terminal/terminal device of the embodiment of the present application, and the communication device 1400 may implement the corresponding processes implemented by the mobile terminal/terminal device in each method of the embodiment of the present application, for the sake of brevity , which will not be repeated here.

FIG. 20 is a schematic structural diagram of a chip according to an embodiment of the present application. The chip 1500 shown in FIG. 20 includes a processor 1510, and the processor 1510 can call and run a computer program from a memory, so as to implement the method in the embodiment of the present application.

Optionally, as shown in FIG. 20 , the chip 1500 may further include a memory 1520 . Wherein, the processor 1510 can invoke and run a computer program from the memory 1520, so as to implement the method in the embodiment of the present application.

Wherein, the memory 1520 may be an independent device independent of the processor 1510 , or may be integrated in the processor 1510 .

Optionally, the chip 1500 may also include an input interface 1530 . Wherein, the processor 1510 can control the input interface 1530 to communicate with other devices or chips, specifically, can obtain information or data sent by other devices or chips.

Optionally, the chip 1500 may also include an output interface 1540 . Wherein, the processor 1510 can control the output interface 1540 to communicate with other devices or chips, specifically, can output information or data to other devices or chips.

Optionally, the chip can be applied to the network device in the embodiment of the present application, and the chip can implement the corresponding processes implemented by the network device in the methods of the embodiment of the present application. For the sake of brevity, details are not repeated here.

Optionally, the chip can be applied to the mobile terminal/terminal device in the embodiments of the present application, and the chip can implement the corresponding processes implemented by the mobile terminal/terminal device in the various methods of the embodiments of the present application. For the sake of brevity, here No longer.

It should be understood that the chip mentioned in the embodiment of the present application may also be called a system-on-chip, a system-on-chip, a system-on-a-chip, or a system-on-a-chip.

Fig. 21 is a schematic block diagram of a communication system 900 provided by an embodiment of the present application. As shown in FIG. 21 , the communication system 900 includes a terminal device 910 and a network device 920 .

Wherein, the terminal device 910 can be used to realize the corresponding functions realized by the terminal device in the above method, and the network device 920 can be used to realize the corresponding functions realized by the network device in the above method. .

It should be understood that the processor in the embodiment of the present application may be an integrated circuit chip, which has a signal processing capability. In the implementation process, each step of the above-mentioned method embodiments may be completed by an integrated logic circuit of hardware in a processor or instructions in the form of software. The above-mentioned processor can be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), an off-the-shelf programmable gate array (Field Programmable Gate Array, FPGA) or other available Program logic devices, discrete gate or transistor logic devices, discrete hardware components. Various methods, steps, and logic block diagrams disclosed in the embodiments of the present application may be implemented or executed. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, register. The storage medium is located in the memory, and the processor reads the information in the memory, and completes the steps of the above method in combination with its hardware.

It can be understood that the memory in the embodiments of the present application may be a volatile memory or a nonvolatile memory, or may include both volatile and nonvolatile memories. Among them, the non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electronically programmable Erase Programmable Read-Only Memory (Electrically EPROM, EEPROM) or Flash. The volatile memory can be Random Access Memory (RAM), which acts as external cache memory. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (Static RAM, SRAM), Dynamic Random Access Memory (Dynamic RAM, DRAM), Synchronous Dynamic Random Access Memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous connection dynamic random access memory (Synchlink DRAM, SLDRAM ) and Direct Memory Bus Random Access Memory (Direct Rambus RAM, DR RAM). It should be noted that the memory of the systems and methods described herein is intended to include, but not be limited to, these and any other suitable types of memory.

It should be understood that the above-mentioned memory is illustrative but not restrictive. For example, the memory in the embodiment of the present application may also be a static random access memory (static RAM, SRAM), a dynamic random access memory (dynamic RAM, DRAM), Synchronous dynamic random access memory (synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (double data rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), synchronous connection Dynamic random access memory (synch link DRAM, SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DR RAM), etc. That is, the memory in the embodiments of the present application is intended to include, but not be limited to, these and any other suitable types of memory.

The embodiment of the present application also provides a computer-readable storage medium for storing computer programs.

Optionally, the computer-readable storage medium can be applied to the network device in the embodiments of the present application, and the computer program enables the computer to execute the corresponding processes implemented by the network device in the methods of the embodiments of the present application. For brevity, here No longer.

Optionally, the computer-readable storage medium can be applied to the mobile terminal/terminal device in the embodiments of the present application, and the computer program enables the computer to execute the corresponding processes implemented by the mobile terminal/terminal device in the various methods of the embodiments of the present application , for the sake of brevity, it is not repeated here.

The embodiment of the present application also provides a computer program product, including computer program instructions.

Optionally, the computer program product may be applied to the network device in the embodiment of the present application, and the computer program instructions cause the computer to execute the corresponding process implemented by the network device in each method of the embodiment of the present application. For the sake of brevity, the Let me repeat.

Optionally, the computer program product can be applied to the mobile terminal/terminal device in the embodiments of the present application, and the computer program instructions cause the computer to execute the corresponding processes implemented by the mobile terminal/terminal device in the methods of the embodiments of the present application, For the sake of brevity, details are not repeated here.

The embodiment of the present application also provides a computer program.

Optionally, the computer program can be applied to the network device in the embodiment of the present application. When the computer program is run on the computer, the computer executes the corresponding process implemented by the network device in each method of the embodiment of the present application. For the sake of brevity , which will not be repeated here.

Optionally, the computer program can be applied to the mobile terminal/terminal device in the embodiment of the present application. When the computer program is run on the computer, the computer executes each method in the embodiment of the present application to be implemented by the mobile terminal/terminal device For the sake of brevity, the corresponding process will not be repeated here.

Those skilled in the art can appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be regarded as exceeding the scope of the present application.

Those skilled in the art can clearly understand that for the convenience and brevity of the description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

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

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

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

If the functions described above are realized in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disc and other media that can store program codes. .

The above is only a specific implementation of the application, but the scope of protection of the application is not limited thereto. Anyone familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the application. Should be covered within the protection scope of this application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims (130)

A method for wireless communication, comprising: When the channel scene is changed from the first channel scene to the second channel scene, the network device performs migration training on the first encoding network deployed on the terminal device and the first decoding network deployed on the network device according to the target training data set , to obtain a second encoding network and a second decoding network, wherein the target training data set includes channel data in the second channel scenario, and the first encoding network and the first decoding network are adapted to the first One channel scene. The method according to claim 1, further comprising: The network device monitors channel quality indicators; According to the change amount of the channel quality index, it is determined whether the channel scene is changed. The method according to claim 2, wherein the determining whether the channel scene is changed according to the variation of the channel quality index comprises: The network device determines that the channel scene is changed when the change amount of the channel quality index is greater than the first threshold. The method according to any one of claims 1-3, further comprising: The network device sends first indication information to the terminal device, where the first indication information is used to indicate that a model is updated and/or a channel scene is changed, where the model update includes a model update of the encoding network and/or Model updates for the decoding network. The method according to claim 4, wherein the first indication information is sent through downlink control information (DCI). The method according to claim 1, further comprising: The network device receives second instruction information sent by the terminal device, where the second instruction information is used to indicate model update and/or channel scene change, where the model update includes model update of the coding network and/or Or model updates for decoding networks. The method according to claim 6, wherein the second indication information is sent through uplink control information (UCI). The method according to any one of claims 1-7, further comprising: The network device acquires the model parameters of the first coding network from the terminal device. The method according to any one of claims 1-8, further comprising: The network device receives the target training data set reported by the terminal device. The method according to claim 9, wherein the target training data set includes the channel state on at least one frequency domain unit within the first duration after the first channel scene is changed to the second channel scene Information CSI vector. The method according to claim 10, wherein the CSI vector is obtained by quantization based on a first codebook, and the precision of the first codebook is higher than the precision of the Type 2 codebook. The method according to claim 11, wherein the configuration of the first codebook is sent by the network device through downlink control information (DCI). The method according to any one of claims 10-12, wherein the first duration includes N time slots, and the at least one frequency domain unit includes K subbands or M physical resource blocks (PRBs), where , the N is a positive integer, the K is a positive integer, and the M is a positive integer. The method according to claim 13, characterized in that, The N is predefined or configured by the network device; The K is predefined or configured by the network device; The M is predefined or configured by the network device. The method according to any one of claims 1-8, further comprising: According to the changed channel scene, the target training data set is determined in multiple prestored data sets, where each data set in the multiple data sets corresponds to a corresponding channel scene. The method according to claim 15, further comprising: The network device receives third indication information sent by the terminal device, where the third indication information is used to indicate a changed channel scenario. The method according to claim 15 or 16, wherein the target training data set only includes a data set corresponding to the second channel scene; or The target training data set includes a plurality of data sets, wherein each data set corresponds to a corresponding weight, and the weight corresponding to each data set is based on the correlation between the channel scene applicable to the data set and the second channel scene gender certainty. The method according to any one of claims 1-17, further comprising: The network device sends the model parameters of the second encoding network obtained through migration training to the terminal device. The method according to any one of claims 1-18, further comprising: The network device receives the target bit stream sent by the terminal device, where the target bit stream is obtained by the terminal device encoding channel data in the second channel scenario through the second encoding network. The method according to claim 19, further comprising: The network device decodes the target bit stream through the second decoding network to obtain target channel data. A method for wireless communication, comprising: When the channel scene is changed from the first channel scene to the second channel scene, the terminal device performs migration training on the first encoding network deployed on the terminal device and the first decoding network deployed on the network device according to the target training data set , to obtain a second encoding network and a second decoding network, wherein the target training data set includes channel data in the second channel scenario, and the first encoding network and the first decoding network are adapted to the first One channel scene. The method according to claim 21, further comprising: The terminal device monitors channel quality indicators; According to the change amount of the channel quality index, it is determined whether the channel scene is changed. The method according to claim 22, wherein the determining whether the channel scene is changed according to the variation of the channel quality index comprises: The terminal device determines that the channel scene is changed when the change amount of the channel quality index is greater than the second threshold. The method according to any one of claims 21-23, further comprising: Sending second indication information to the network device, where the second indication information is used to indicate that a model is updated and/or a channel scene is changed, where the model update includes an encoding network model update and/or a decoding network model renew. The method according to claim 24, wherein the second indication information is sent through uplink control information (UCI). The method according to claim 21, further comprising: The terminal device receives the first indication information sent by the network device, which is used to indicate model update and/or channel scene change, wherein the model update includes a model update of the encoding network and/or a model update of the decoding network . The method according to claim 26, wherein the first indication information is sent through downlink control information (DCI). The method according to any one of claims 21-27, wherein the target training data set includes at least one frequency within the first duration after the first channel scene is changed to the second channel scene. Channel state information CSI vector on the domain unit. The method according to claim 28, wherein the CSI vector is obtained by quantization based on a first codebook, and the precision of the first codebook is higher than the precision of the type 2 codebook. The method according to claim 29, wherein the configuration of the first codebook is sent by the network device through downlink control information (DCI). The method according to any one of claims 28-30, wherein the first duration includes N time slots, and the at least one frequency domain unit includes K subbands or M physical resource blocks (PRBs), where , the N is a positive integer, the K is a positive integer, and the M is a positive integer. The method of claim 31 wherein, The N is predefined or configured by the network device; The K is predefined or configured by the network device; The M is predefined or configured by the network device. The method according to any one of claims 21-32, further comprising: Obtain model parameters of the first decoding network from the network device. The method according to any one of claims 21-32, further comprising: and sending the model parameters of the second decoding network obtained through migration training to the network device. The method according to any one of claims 21-34, further comprising: The terminal device encodes the channel data in the second channel scenario through the second encoding network to obtain a target bit stream; The terminal device sends the target bit stream to the network device. A method for wireless communication, comprising: The terminal device sends first information to the network device, where the first information is used by the network device to update the first encoding network deployed on the terminal device and the first encoding network deployed on the network device when the channel scene changes. The decoding network performs migration training to obtain a second encoding network and a second decoding network, wherein the first encoding network and the first decoding network adapt to the channel scene before the change, and the second encoding network and the first The second decoding network adapts to the changed channel scenario. The method of claim 36, further comprising: The terminal device monitors channel quality indicators; According to the change amount of the channel quality index, it is determined whether the channel scene is changed. The method according to claim 37, wherein the determining whether the channel scene is changed according to the variation of the channel quality index comprises: The terminal device determines that the channel scene is changed when the change amount of the channel quality index is greater than the second threshold. The method according to any one of claims 36-38, wherein the first information includes second indication information, and the second indication information is used to indicate model update and/or channel scene change, Wherein, the model update includes the model update of the encoding network and/or the model update of the decoding network. The method according to claim 39, wherein the second indication information is sent through uplink control information (UCI). The method of claim 36, further comprising: The terminal device receives the first instruction information sent by the network device, the first instruction information is used to indicate that a model update is performed and/or a channel scene is changed, wherein the model update includes an encoding network model update and/or Or model updates for decoding networks. The method according to claim 41, wherein the first indication information is sent through downlink control information (DCI). The method according to any one of claims 36-42, wherein the first information includes model parameters of the first coding network. The method according to any one of claims 36-43, wherein the first information includes a target training data set, and the target training data set includes channel data in a changed channel scenario. The method according to claim 44, wherein the target training data set includes channel state information (CSI) vectors on at least one frequency domain unit within the first time period after the channel scene is changed. The method according to claim 45, wherein the CSI vector is obtained by quantization based on a first codebook, and the precision of the first codebook is higher than that of the type 2 codebook. The method according to claim 46, wherein the configuration of the first codebook is sent by the network device through DCI. The method according to any one of claims 45-47, wherein the first duration includes N time slots, and the at least one frequency domain unit includes K subbands or M physical resource blocks (PRBs), where , the N is a positive integer, the K is a positive integer, and the M is a positive integer. The method of claim 48, wherein, The N is predefined or configured by the network device; The K is predefined or configured by the network device; The M is predefined or configured by the network device. The method according to any one of claims 36-43, wherein the first information includes third indication information, and the third indication information is used to indicate the changed channel scenario. The method according to any one of claims 36-50, further comprising: The terminal device acquires, from the network device, the model parameters of the second encoding network obtained through migration training. The method according to any one of claims 36-51, further comprising: The terminal device encodes the channel data in the changed channel scenario through the second encoding network to obtain a target bit stream. The method according to claim 52, further comprising: The terminal device sends the target bit stream to the network device. A method for wireless communication, comprising: The network device sends second information to the terminal device, where the second information is used for the terminal device to update the first encoding network deployed on the terminal device and the first encoding network deployed on the network device when the channel scene changes. The first decoding network performs migration training to obtain a second encoding network and a second decoding network, wherein the first encoding network and the first decoding network adapt to the channel scene before the change, and the second encoding network and the The second decoding network adapts to the changed channel scenario. The method according to claim 54, wherein the second information includes first indication information, and the first indication information is used to indicate model update and/or channel scene change, wherein the model update Include model updates for the encoding network and/or model updates for the decoding network. The method according to claim 55, wherein the first indication information is sent through downlink control information (DCI). The method according to any one of claims 54-56, further comprising: The network device monitors channel quality indicators; According to the change amount of the channel quality index, it is determined whether the channel scene is changed. The method according to any one of claims 54-57, wherein the second information includes model parameters of the first decoding network. The method according to any one of claims 54-58, further comprising: The network device acquires the model parameters of the second decoding network from the terminal device. The method according to any one of claims 54-59, further comprising: The network device decodes the target bit stream through the second decoding network to obtain target channel data, where the target bit stream is the target bit stream obtained by the terminal device under the second channel scenario through the second encoding network obtained by encoding the channel data. A network device, characterized in that it includes: A processing unit, configured to perform an operation on the first encoding network deployed on the terminal device and the first decoding network deployed on the network device according to the target training data set when the channel scenario is changed from the first channel scenario to the second channel scenario Migration training is performed to obtain a second encoding network and a second decoding network, wherein the target training data set includes channel data in the second channel scenario, and the first encoding network and the first decoding network are adapted The first channel scenario. The network device according to claim 61, wherein the processing unit is further used for: Monitor the channel quality index, and determine whether the channel scene is changed according to the change amount of the channel quality index. The network device according to claim 62, wherein the processing unit is further used for: If the change amount of the channel quality index is greater than the first threshold, it is determined that the channel scene is changed. The network device according to any one of claims 61-63, wherein the network device further comprises: A communication unit, configured to send first indication information to the terminal device, where the first indication information is used to indicate that a model is updated and/or a channel scene is changed, wherein the model update includes an encoding network model update and/or Or model updates for decoding networks. The network device according to claim 64, wherein the first indication information is sent through downlink control information (DCI). The network device according to claim 61, wherein the network device further comprises: A communication unit, configured to receive second indication information sent by the terminal device, where the second indication information is used to indicate that a model is updated and/or a channel scene is changed, where the model update includes a model update of the coding network and /or model updates for the decoding network. The network device according to claim 66, wherein the second indication information is sent through uplink control information (UCI). The network device according to any one of claims 61-67, wherein the network device further comprises: A communication unit, configured to acquire the model parameters of the first encoding network from the terminal device. The network device according to any one of claims 61-68, wherein the network device further comprises: A communication unit, configured to receive the target training data set reported by the terminal device. The network device according to claim 69, wherein the target training data set includes channels on at least one frequency domain unit within the first duration after the first channel scene is changed to the second channel scene Status information CSI vector. The network device according to claim 70, wherein the CSI vector is obtained by quantization based on a first codebook, and the precision of the first codebook is higher than the precision of the Type 2 codebook. The network device according to claim 71, wherein the configuration of the first codebook is sent by the network device through downlink control information DCI. The network device according to any one of claims 70-72, wherein the first duration includes N time slots, and the at least one frequency domain unit includes K subbands or M physical resource blocks (PRBs), Wherein, the N is a positive integer, the K is a positive integer, and the M is a positive integer. The network device according to claim 73, wherein, The N is predefined or configured by the network device; The K is predefined or configured by the network device; The M is predefined or configured by the network device. The network device according to any one of claims 61-68, wherein the processing unit is further configured to: According to the changed channel scene, the target training data set is determined in multiple prestored data sets, where each data set in the multiple data sets corresponds to a corresponding channel scene. The network device according to claim 75, wherein the network device further comprises: A communication unit, configured to receive third indication information sent by the terminal device, where the third indication information is used to indicate a changed channel scenario. The network device according to claim 75 or 76, wherein the target training data set only includes a data set corresponding to the second channel scenario; or The target training data set includes a plurality of data sets, wherein each data set corresponds to a corresponding weight, and the weight corresponding to each data set is based on the correlation between the channel scene applicable to the data set and the second channel scene gender certainty. The network device according to any one of claims 61-77, wherein the network device further comprises: a communication unit, configured to send the model parameters of the second encoding network obtained through migration training to the Terminal Equipment. The network device according to any one of claims 61-78, wherein the network device further comprises: a communication unit, configured to receive the target bit stream sent by the terminal device, the target bit stream being the obtained by the terminal device encoding the channel data in the second channel scenario through the second encoding network. The network device according to claim 79, wherein the processing unit is further used for: The target bit stream is decoded by the second decoding network to obtain target channel data. A terminal device, characterized in that it includes: a processing unit, configured to perform an operation on the first encoding network deployed on the terminal device and the first decoding network deployed on the network device according to the target training data set when the channel scene is changed from the first channel scene to the second channel scene Migration training is performed to obtain a second encoding network and a second decoding network, wherein the target training data set includes channel data in the second channel scenario, and the first encoding network and the first decoding network are adapted The first channel scenario. The terminal device according to claim 81, wherein the processing unit is further used for: Monitor the channel quality index, and determine whether the channel scene is changed according to the change amount of the channel quality index. The terminal device according to claim 82, wherein the processing unit is further used for: If the change amount of the channel quality index is greater than the second threshold, it is determined that the channel scene is changed. The terminal device according to any one of claims 81-83, wherein the terminal device further comprises: A communication unit, configured to send second indication information to the network device, where the second indication information is used to indicate that a model is updated and/or a channel scene is changed, where the model update includes an encoding network model update and/or Or model updates for decoding networks. The terminal device according to claim 84, wherein the second indication information is sent through uplink control information (UCI). The terminal device according to claim 81, wherein the terminal device further comprises: A communication unit, configured to receive the first instruction information sent by the network device, for instructing to update the model and/or change the channel scene, wherein the update of the model includes an update of the model of the encoding network and/or a model of the decoding network renew. The terminal device according to claim 86, wherein the first indication information is sent through downlink control information (DCI). The terminal device according to any one of claims 81-87, wherein the target training data set includes at least one of the first time period after the first channel scene is changed to the second channel scene Channel state information CSI vector on the frequency domain unit. The terminal device according to claim 88, wherein the CSI vector is obtained by quantization based on a first codebook, and the precision of the first codebook is higher than the precision of the Type 2 codebook. The terminal device according to claim 89, wherein the configuration of the first codebook is sent by the network device through downlink control information DCI. The terminal device according to any one of claims 88-90, wherein the first duration includes N time slots, and the at least one frequency domain unit includes K subbands or M physical resource blocks (PRBs), Wherein, the N is a positive integer, the K is a positive integer, and the M is a positive integer. The terminal device according to claim 91, characterized in that, The N is predefined or configured by the network device; The K is predefined or configured by the network device; The M is predefined or configured by the network device. The terminal device according to any one of claims 81-92, wherein the terminal device further comprises: A communication unit, configured to acquire model parameters of the first decoding network from the network device. The terminal device according to any one of claims 81-93, wherein the terminal device further comprises: a communication unit, configured to send the model parameters of the second decoding network obtained through migration training to the Internet equipment. The terminal device according to any one of claims 81-94, wherein the processing unit is further configured to: encode the channel data in the second channel scenario through the second encoding network to obtain target bitstream; The terminal device further includes: a communication unit, configured to send the target bit stream to the network device. A terminal device, characterized in that it includes: A communication unit, configured to send first information to a network device, where the first information is used by the network device to update the first encoding network deployed on the terminal device and the first encoding network deployed on the network device when the channel scene changes. Migration training is performed on the first decoding network to obtain a second encoding network and a second decoding network, wherein the first encoding network and the first decoding network adapt to the channel scene before the change, and the second encoding network and The second decoding network adapts to the changed channel scenario. The terminal device according to claim 96, wherein the terminal device further comprises: The processing unit is configured to monitor the channel quality index, and determine whether the channel scene is changed according to the change amount of the channel quality index. The terminal device according to claim 97, wherein the processing unit is further used for: If the change amount of the channel quality index is greater than the second threshold, it is determined that the channel scene is changed. The terminal device according to any one of claims 96-98, wherein the first information includes second indication information, and the second indication information is used to indicate model update and/or channel scene change , wherein the model update includes model update of the encoding network and/or model update of the decoding network. The terminal device according to claim 99, wherein the second indication information is sent through uplink control information (UCI). The terminal device according to claim 96, wherein the communication unit is further used for: receiving first instruction information sent by the network device, where the first instruction information is used to indicate model update and/or channel scene change, where the model update includes model update of the encoding network and/or model update of the decoding network Model updates. The terminal device according to claim 101, wherein the first indication information is sent through downlink control information (DCI). The terminal device according to any one of claims 96-102, wherein the first information includes model parameters of the first encoding network. The terminal device according to any one of claims 96-103, wherein the first information includes a target training data set, and the target training data set includes channel data in a changed channel scenario. The terminal device according to claim 104, wherein the target training data set includes channel state information (CSI) vectors on at least one frequency domain unit within the first time period after the channel scene is changed. The terminal device according to claim 105, wherein the CSI vector is obtained by quantization based on a first codebook, and the accuracy of the first codebook is higher than that of a Type 2 codebook. The terminal device according to claim 106, wherein the configuration of the first codebook is sent by the network device through DCI. The terminal device according to any one of claims 105-107, wherein the first duration includes N time slots, and the at least one frequency domain unit includes K subbands or M physical resource blocks (PRBs), Wherein, the N is a positive integer, the K is a positive integer, and the M is a positive integer. The terminal device according to claim 108, wherein, The N is predefined or configured by the network device; The K is predefined or configured by the network device; The M is predefined or configured by the network device. The terminal device according to any one of claims 96-109, wherein the first information includes third indication information, and the third indication information is used to indicate a changed channel scenario. The terminal device according to any one of claims 96-110, wherein the communication unit is further used for: Acquiring the model parameters of the second coding network obtained through migration training from the network device. The terminal device according to any one of claims 96-111, wherein the terminal device further comprises: A processing unit, configured to encode the channel data in the second channel scenario through the second encoding network to obtain a target bit stream. The terminal device according to claim 112, wherein the communication unit is further used for: sending the target bitstream to the network device. A network device, characterized in that it includes: A communication unit, configured to send second information to the terminal device, where the second information is used for the terminal device to update the first encoding network deployed on the terminal device and the network device when the channel scene changes. Migration training is performed on the first decoding network deployed on the network to obtain a second encoding network and a second decoding network, wherein the first encoding network and the first decoding network are adapted to the channel scene before the change, and the second encoding network The network and the second decoding network adapt to the changed channel scenario. The method according to claim 114, wherein the network device further comprises: The processing unit is configured to monitor the channel quality index, and determine whether the channel scene is changed according to the change amount of the channel quality index. The method according to claim 114 or 115, wherein the second information includes first indication information, and the first indication information is used to indicate model update and/or channel scene change, wherein the Model updating includes model updating of the encoding network and/or model updating of the decoding network. The method according to claim 116, wherein the first indication information is sent through downlink control information (DCI). The method according to any one of claims 114-117, wherein the second information includes model parameters of the first decoding network. The method according to any one of claims 114-118, wherein the communication unit is further used for: Acquire model parameters of the second decoding network from the terminal device. The method according to any one of claims 114-119, wherein the network device further comprises: A processing unit, configured to decode the target bit stream through the second decoding network to obtain target channel data, wherein the target bit stream is the second channel scenario that the terminal device uses through the second encoding network The following channel data are encoded. A network device, characterized by comprising: a processor and a memory, the memory is used to store a computer program, the processor is used to invoke and run the computer program stored in the memory, and execute any of the following claims 1 to 20 A method as claimed in any one of claims 54 to 60. A chip, characterized in that it includes: a processor, configured to call and run a computer program from a memory, so that a device equipped with the chip executes the method according to any one of claims 1 to 20, or as described in The method as claimed in any one of claims 54 to 60. A computer-readable storage medium, characterized in that it is used to store a computer program, the computer program causes the computer to perform the method according to any one of claims 1 to 20, or any one of claims 54 to 60 method described in the item. A computer program product, characterized in that it includes computer program instructions, the computer program instructions cause the computer to perform the method as described in any one of claims 1 to 20, or as described in any one of claims 54 to 60 Methods. A computer program, characterized in that the computer program causes a computer to execute the method according to any one of claims 1-20, or the method according to any one of claims 54-60. A terminal device, characterized in that it includes: a processor and a memory, the memory is used to store computer programs, the processor is used to call and run the computer programs stored in the memory, and execute any one of claims 21 to 35 A method according to one, or a method according to any one of claims 36-53. A chip, characterized by comprising: a processor, configured to call and run a computer program from a memory, so that a device equipped with the chip executes the method according to any one of claims 21 to 35, or as described in The method of any one of claims 36-53. A computer-readable storage medium, characterized in that it is used to store a computer program, the computer program causes the computer to perform the method according to any one of claims 21 to 35, or any one of claims 36-53 method described in the item. A computer program product, characterized in that it includes computer program instructions, the computer program instructions cause the computer to perform the method as described in any one of claims 21 to 35, or as described in any one of claims 36-53 Methods. A computer program, characterized in that the computer program causes a computer to execute the method according to any one of claims 21 to 35, or the method according to any one of claims 36-53.

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