CSI feedback method and apparatus, and device and readable storage medium

Abstract

The present application belongs to the technical field of communications. Disclosed are a CSI feedback method and apparatus, and a device and a readable storage medium. The method comprises: a network-side device performing training to obtain a target layer in an updated encoding AI unit; a terminal receiving first data from the network-side device; the terminal updating the target layer in the encoding AI unit on the basis of the first data, so as to obtain the updated encoding AI unit; the terminal encoding CSI by means of the update encoding AI unit, so as to obtain CSI feedback information; and the terminal sending the CSI feedback information to the network-side device, wherein the encoding AI unit is an AI unit for encoding the CSI by the terminal, and the first data includes information associated with the target layer in the update encoding AI unit.

Description

CSI feedback method, device, equipment, and readable storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese Patent Application No. 202311008078.3 filed in China on August 10, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present application belongs to the field of communication technology, and specifically relates to a CSI feedback method, apparatus, device, and readable storage medium. Background Art

[0004] Artificial Intelligence (AI) technology is applied to the CSI encoding process during the channel state information (CSI) feedback process. When the configuration related to the CSI feedback changes, and the changed configuration is a configuration that the AI ​​unit has not considered during the training process, the AI ​​unit used for CSI encoding also needs to be updated. The existing methods for updating the unit are: 1) Generally, the complete encoder needs to be re-transmitted to the terminal side (or the decoder needs to be re-transmitted to the network side); 2) The training process needs to be repeated to update the unit; 3) The relevant unit information (such as the input and output data of the unit, etc.) needs to be re-transmitted to update the unit. Generally speaking, the overhead of re-transmitting the unit is lower than the overhead of retraining the unit. Therefore, method 1) has an advantage over methods 2) and 3) in flexible unit updates. However, when the encoder structure is more complex or the parameter scale is large, the overhead of unit transmission is still high.

[0005] Summary of the Invention

[0006] The embodiments of the present application provide a CSI feedback method, apparatus, device, and readable storage medium, which can solve the problem of high overhead when updating AI units during the CSI feedback process.

[0007] In a first aspect, a CSI feedback method is provided, including:

[0008] The terminal receives first data from the network side device;

[0009] The terminal updates the target layer in the coding artificial intelligence (AI) unit according to the first data to obtain an updated coding AI unit;

[0010] The terminal encodes the CSI through the updated encoding AI unit to obtain CSI feedback information;

[0011] The terminal sends the CSI feedback information to the network side device;

[0012] The encoding AI unit is an AI unit used by the terminal to encode the CSI, and the first data includes information associated with a target layer in the updated encoding AI unit.

[0013] In a second aspect, a CSI feedback method is provided, including:

[0014] The network-side device training obtains the target layer in the updated coding AI unit;

[0015] The network side device sends first data to the terminal;

[0016] The network side device receives CSI feedback information from the terminal;

[0017] Among them, the first data is used by the terminal to update the target layer in the coding AI unit to obtain an updated coding AI unit, and the coding AI unit is an AI unit for the terminal to encode CSI, and the first data contains information associated with the target layer in the updated coding AI unit.

[0018] In a third aspect, a CSI feedback device is provided, including:

[0019] A first receiving module, configured for a terminal to receive first data from a network-side device;

[0020] An updating module, configured for the terminal to update a target layer in a coding AI unit according to the first data to obtain an updated coding AI unit;

[0021] A first processing module, configured for the terminal to encode the CSI through the updated encoding AI unit to obtain CSI feedback information;

[0022] A first sending module, configured for the terminal to send the CSI feedback information to the network side device;

[0023] The encoding AI unit is an AI unit used by the terminal to encode the CSI, and the first data includes information associated with a target layer in the updated encoding AI unit.

[0024] In a fourth aspect, a CSI feedback device is provided, including:

[0025] The training module is used to train network-side devices to update the target layer in the coding AI unit;

[0026] A second sending module, configured for the network side device to send first data to the terminal;

[0027] A second receiving module, the network side device receives CSI feedback information from the terminal;

[0028] Among them, the first data is used by the terminal to update the target layer in the coding AI unit to obtain an updated coding AI unit, and the coding AI unit is an AI unit for the terminal to encode CSI, and the first data contains information associated with the target layer in the updated coding AI unit.

[0029] In a fifth aspect, a terminal is provided, comprising a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0030] In a sixth aspect, a terminal is provided, comprising a processor and a communication interface, wherein the communication interface is used for the terminal to receive first data from a network side device; the processor is used for the terminal to update the target layer in the coding artificial intelligence AI unit according to the first data to obtain an updated coding AI unit; the terminal encodes the CSI through the updated coding AI unit to obtain CSI feedback information; the communication interface is used for the terminal to send the CSI feedback information to the network side device; wherein the coding AI unit is an AI unit for the terminal to encode the CSI, and the first data includes information associated with the target layer in the updated coding AI unit.

[0031] In the seventh aspect, a network side device is provided, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the second aspect are implemented.

[0032] In the eighth aspect, a network side device is provided, including a processor and a communication interface, wherein the processor is used for the network side device to train and obtain a target layer in an updated coding AI unit; the communication interface is used for the network side device to send first data to a terminal; the communication interface is used for the network side device to receive CSI feedback information from the terminal; wherein the first data is used by the terminal to update the target layer in the coding AI unit to obtain an updated coding AI unit, the coding AI unit is an AI unit for the terminal to encode CSI, and the first data contains information associated with the target layer in the updated coding AI unit.

[0033] In the ninth aspect, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented, or the steps of the method described in the second aspect are implemented.

[0034] In the tenth aspect, a wireless communication system is provided, comprising: a terminal and a network side device, wherein the terminal can be used to execute the steps of the method described in the first aspect, and the network side device can be used to execute the steps of the method described in the second aspect.

[0035] In the eleventh aspect, a chip is provided, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method as described in the first aspect, or to implement the method as described in the second aspect.

[0036] In the twelfth aspect, a computer program / program product is provided, which is stored in a storage medium and is executed by at least one processor to implement the method as described in the first aspect, or to implement the method as described in the second aspect.

[0037] In this embodiment of the present application, the terminal updates the target layer in the coding AI unit based on data received from the network, and then encodes the CSI by updating the coding AI unit to obtain CSI feedback information and feed it back to the network. This allows for a low-overhead update of the AI ​​unit, thereby improving the flexibility of CSI feedback processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] FIG1a is a block diagram of a wireless communication system applicable to embodiments of the present application;

[0039] Figure 1b is a schematic diagram of the structure of a neural network;

[0040] Figure 1c is a schematic diagram of the structure of a neuron;

[0041] FIG2 is a flow chart of a CSI feedback method according to an embodiment of the present application;

[0042] FIG3 is a second flow chart of the CSI feedback method provided in an embodiment of the present application;

[0043] FIG4a is a schematic diagram of one of the application embodiments provided in the embodiments of the present application;

[0044] FIG4 b is a second schematic diagram of an application embodiment provided by an embodiment of the present application;

[0045] FIG4c is a third schematic diagram of an application embodiment provided by an embodiment of the present application;

[0046] FIG5 is a schematic diagram of a structure of a CSI feedback device according to an embodiment of the present application;

[0047] FIG6 is a second structural diagram of a CSI feedback device provided in an embodiment of the present application;

[0048] FIG7 is a schematic structural diagram of a communication device provided in an embodiment of the present application;

[0049] FIG8 is a schematic diagram of the structure of a terminal provided in an embodiment of the present application;

[0050] FIG9 is a schematic diagram of a structure of a network side device according to an embodiment of the present application;

[0051] FIG10 is a second structural diagram of the network side device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] The following will be combined with the accompanying drawings in the embodiments of this application to clearly describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field are within the scope of protection of this application.

[0053] The terms "first", "second", etc. in this application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the terms used in this way are interchangeable where appropriate, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first" and "second" are generally of the same type, and do not limit the number of objects, for example, the first object can be one or more. In addition, "or" in this application represents at least one of the connected objects. For example, "A or B" covers three options, namely, Option 1: including A but not including B; Option 2: including B but not including A; Option 3: including both A and B. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0054] The term "indication" in this application can be either a direct indication (or explicit indication) or an indirect indication (or implicit indication). A direct indication can be understood as the sender explicitly informing the receiver of specific information, the operation to be performed, or the requested result, etc. in the instruction sent; an indirect indication can be understood as the receiver determining the corresponding information based on the instruction sent by the sender, or making a judgment and determining the operation to be performed or the requested result, etc. based on the judgment result.

[0055] It is worth noting that the technology described in the embodiments of the present application is not limited to the Long Term Evolution (LTE) / LTE-Advanced (LTE-A) system, but can also be used in other wireless communication systems, such as Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Frequency Division Multiple Access (FDMA), Orthogonal Frequency Division Multiple Access (OFDMA), Single-carrier Frequency-Division Multiple Access (SC-FDMA) or other systems. The terms "system" and "network" in the embodiments of the present application are often used interchangeably, and the technology described can be used for the systems and radio technologies mentioned above, as well as for other systems and radio technologies. The following description describes a New Radio (NR) system for illustrative purposes, and NR terminology is used in most of the following description, but these technologies can also be applied to systems other than NR systems, such as 6th generation (6G) systems. th Generation, 6G) communication system.

[0056] FIG1a shows a block diagram of a wireless communication system applicable to an embodiment of the present application. The wireless communication system includes a terminal 11 and a network-side device 12. The terminal 11 may be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), a notebook computer, a personal digital assistant (PDA), a handheld computer, a netbook, an ultra-mobile personal computer (UMPC), a mobile internet device (MID), an augmented reality (AR), a virtual reality (VR) device, a robot, a wearable device (Wearable Device), an aircraft (flight vehicle), a vehicle user equipment (VUE), a ship-borne device, a pedestrian user equipment (PUE), a smart home (home appliances with wireless communication capabilities, such as refrigerators, televisions, washing machines, or furniture), a game console, a personal computer (PC), an ATM, or a self-service machine, or other terminal-side devices. Wearable devices include: smart watches, smart bracelets, smart headphones, smart glasses, smart jewelry (smart bracelets, smart bracelets, smart rings, smart necklaces, smart anklets, smart anklets, etc.), smart wristbands, smart clothing, etc. Among them, the vehicle-mounted device can also be called a vehicle-mounted terminal, a vehicle-mounted controller, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application. The network side device 12 may include an access network device or a core network device, wherein the access network device may also be called a radio access network (Radio Access Network, RAN) device, a radio access network function or a radio access network unit. The access network device may include a base station, a wireless local area network (Wireless Local Area Network, WLAN) access point (Access Point, AP) or a wireless fidelity (Wireless Fidelity, WiFi) node, etc.Among them, the base station can be referred to as Node B (NB), Evolved Node B (eNB), the next generation Node B (gNB), New Radio Node B (NR Node B), access point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home Node B (HNB), Home evolved Node B (home evolved Node B), Transmission Reception Point (TRP) or other appropriate terms in the relevant field. As long as the same technical effect is achieved, the base station is not limited to specific technical vocabulary. It should be noted that in the embodiment of the present application, only the base station in the NR system is used as an example for introduction, and the specific type of the base station is not limited.

[0057] The core network device may include but is not limited to at least one of the following: core network node, core network function, mobility management entity (MME), access mobility management function (AMF), session management function (SMF), user plane function (UPF), policy control function (PCF), policy and charging rules function unit (PCRF), edge application server discovery function (EASDF), unified data management (UDM), unified data storage (UDR), home user server (HSS), centralized network configuration (CNC), network storage function (NRF), network exposure function (NEF), local NEF (L-NEF), binding support function (BNSF), network access function (UE ... Function, BSF), application function (Application Function, AF), etc. It should be noted that in the embodiment of the present application, only the core network device in the NR system is introduced as an example, and the specific type of the core network device is not limited.

[0058] To better understand the technical solution of this application, some contents are first introduced:

[0059] CSI Compression Based on AI / Machine Learning (ML)

[0060] Information theory shows that accurate CSI is crucial to channel capacity. Especially for multi-antenna systems, the transmitter can optimize signal transmission based on CSI to better match the channel state. For example, the channel quality indicator (CQI) can be used to select an appropriate modulation and coding scheme (MCS) for link adaptation; the precoding matrix indicator (PMI) can be used to implement eigenbeamforming to maximize the strength of the received signal, or to suppress interference (such as inter-cell interference and interference between multiple users). Therefore, since the introduction of multi-input multi-output (MIMO) technology, CSI acquisition has been a research hotspot.

[0061] Typically, the base station sends a CSI reference signal (CSI-RS) on certain time-frequency resources in a certain slot. The terminal performs channel estimation based on the CSI-RS, calculates the channel information for this time slot, and feeds the PMI back to the base station using a codebook. The base station then combines the channel information based on the codebook information fed back by the terminal and uses this information for data precoding and multi-user scheduling before the next CSI report.

[0062] To further reduce CSI feedback overhead, the terminal can change the PMI reported for each subband to reporting it according to the delay. Since the channels in the delay domain are more concentrated, a PMI with less delay can approximately represent the PMI of all subbands. This means that the delay domain information is compressed before reporting. Similarly, to reduce overhead, the base station can pre-code the CSI-RS and send the encoded CSI-RS to the terminal. The terminal sees the channel corresponding to the encoded CSI-RS. The terminal only needs to select several ports with higher strength from the ports indicated by the network side and report the coefficients corresponding to these ports.

[0063] Furthermore, in order to better compress channel information, neural network or machine learning methods can be used.

[0064] Artificial intelligence is currently being widely used in various fields. There are many ways to implement AI modules, such as neural networks, decision trees, support vector machines, Bayesian classifiers, etc. This application uses neural networks as an example, but does not limit the specific type of AI module. A simple neural network structure diagram is shown in Figure 1b:

[0065] A neural network is composed of neurons, as shown in Figure 1c. Here, a1, a2, …, aK are inputs, w is the weight (multiplicative coefficient), b is the bias (additive coefficient), and σ(.) is the activation function. Common activation functions include sigmoid, tanh, and ReLU (Rectified Linear Unit).

[0066] Neural network parameters are optimized using optimization algorithms. An optimization algorithm is a type of algorithm that minimizes or maximizes an objective function (sometimes called a loss function). The objective function is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, a neural network model f(.) is constructed. With the model, the predicted output f(x) can be obtained based on the input x. The difference between the predicted value and the true value (f(x) - Y) can be calculated. This is the loss function. The appropriate values ​​w and b are found to minimize the loss function. The smaller the loss value, the closer the model is to the true value.

[0067] Currently, most common optimization algorithms are based on the back propagation (BP) algorithm. The basic idea of ​​the BP algorithm is that the learning process consists of two steps: forward propagation of signals and back propagation of errors. During forward propagation, input samples are passed from the input layer, processed layer by layer through each hidden layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, the error begins back propagation. Back propagation involves propagating the output error back through the hidden layers to the input layer layer by layer in some form, distributing the error to all units in each layer. This error signal is then generated for each unit in each layer, which serves as the basis for adjusting the weights of each unit. This process of adjusting the weights of each layer, through forward propagation of signals and back propagation of errors, is repeated over and over again. This process of continuous weight adjustment is the network's learning and training process. This process continues until the error in the network output is reduced to an acceptable level, or until a pre-set number of learning cycles is reached.

[0068] Common optimization algorithms include gradient descent, stochastic gradient descent (SGD), mini-batch gradient descent, momentum, Nesterov (name of the inventor, specifically stochastic gradient descent with momentum), adaptive gradient descent (Adagrad), Adadelta, root mean square prop (RMSprop), and adaptive momentum estimation (Adam). During error backpropagation, these optimization algorithms calculate the derivative / partial derivative of the current neuron based on the error / loss obtained from the loss function, add the learning rate, previous gradients / derivatives / partial derivatives, and other factors to obtain the gradient, which is then passed to the previous layer.

[0069] Specifically, the channel information is compressed and encoded at the terminal, and the compressed content is decoded at the base station to restore the channel information. In AI-based CSI compression, the decoding network deployed at the base station and the encoding network deployed at the terminal need to work together to effectively restore the original channel information. If the decoding network and the encoding network do not match, the accuracy of the entire CSI feedback will be significantly reduced. To ensure that the decoding network and the encoding network match each other, several training methods, or so-called training collaboration types, can be used. For a detailed introduction to training collaboration types, please see the corresponding section below.

[0070] After the terminal and network have deployed the corresponding models respectively, the terminal estimates the CSI-RS and calculates the channel information. The calculated channel information or the original estimated channel information is encoded through the coding network to obtain the encoding result, and the encoding result is sent to the base station. The base station receives the encoded result and inputs it into the decoding network to recover the channel information.

[0071] AI unit / AI model

[0072] The AI ​​unit / AI model described in this application may also be referred to as an AI unit, an AI model, an ML model, an ML unit, an AI structure, an AI function, an AI feature, a machine learning model, a neural network, a neural network function, a neural network function, etc., or the AI ​​unit / AI model may also refer to a processing unit that can implement specific algorithms, formulas, processing flows, capabilities, etc. related to AI, or the AI ​​unit / AI model may be a processing method, algorithm, function, module or unit for a specific data set, or the AI ​​unit / AI model may be a processing method, algorithm, function, module or unit running on AI / ML related hardware such as a graphics processing unit (GPU), a network processing unit (NPU), a tensor processing unit (TPU), an application-specific integrated circuit (ASIC), etc., and this application does not make specific limitations on this. Optionally, the specific data set includes the input and / or output of the AI ​​unit / AI model.

[0073] Optionally, the identifier of the AI ​​unit / AI model may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific data set associated with the AI ​​unit / AI model, or an identifier of a specific scenario, environment, channel feature, or device related to the AI / ML, or an identifier of a function, feature, capability, or module related to the AI / ML. This application does not specifically limit this.

[0074] Scalability of AI / ML CSI Compression Models

[0075] Model scalability refers to the ability of a single model to adapt to multiple input / output configurations simultaneously. In CSI compression, model scalability mainly considers the following indicators: the number of subbands of the (encoder) input, the number of ports of the (encoder) input, and the payload length of the (encoder) output. In other words, it is hoped that a CSI compression model can simultaneously support as many of the above configuration combinations as possible (for example, a model can support both 64-bit and 116-bit payloads. When the network needs to switch the payload from 64-bit to 116-bit, it can rely on the existing model implementation without retraining the model), thereby reducing the cost of replacing models and / or maintaining multiple models at the same time. Generally speaking, in order to achieve a certain amount of scalability of the CSI compression model, it is necessary to consider multiple situations during the model training phase and combine it with a special model structure to achieve this.

[0076] Theoretically, it's possible to consider all possible scalability aspects of the CSI compression model during model training. However, this approach significantly increases the difficulty of model training and complicates the model structure. Even then, it's impossible to utilize the existing compression model when receiving parameter configurations not known during training.

[0077] Training collaboration types for AI / ML CSI compression models

[0078] The CSI compression use case is a typical two-end model use case, meaning the complete CSI compression model needs to be deployed on different network nodes. Currently, most scenarios considered deploy the encoder on the user equipment (UE) side and the decoder on the network (NW). The (sub-)models deployed on multiple nodes need to be paired with each other to function properly. Considering the above characteristics of the two-end model, 3GPP has identified several basic types of training collaboration:

[0079] 1) Joint training at single entity (also called type 1)

[0080] This training framework involves training a complete encoder-decoder model on a network node (e.g., a UE, NW, or a third-party server node), and then deploying the corresponding model module to the target node through methods such as model transfer (e.g., transferring the encoder portion to the UE, or the decoder portion to the NW). The node that completes the training can be the UE (or a server on the UE side), the NW (or a server on the NW side), or a third-party server node.

[0081] 2) Joint training at multiple entities (also known as type 2)

[0082] This training framework involves multiple nodes participating in the training process, with each node independently calculating the forward and backpropagation information required for local model training and updating its own model parameters. Because the training process requires forward and backpropagation of the entire model (including the encoder and decoder), the corresponding forward and backpropagation information must be transferred between participating nodes. After training is complete, the model no longer needs to be transferred between nodes.

[0083] 3) Separate training on multiple nodes (also known as type 3)

[0084] This training framework involves first training a reference model on a specific node, then sending the relevant information about the reference model to the target node. Finally, the target node trains the model it needs based on this information, ensuring that the (sub-)models of each node can be paired and used together. For example, the NW first trains a complete encoder-decoder model and determines that the resulting decoder is the decoder that will actually be used. The corresponding encoder information (typically the encoder's input and output data) is then sent to the UE, which then trains its own encoder based on this information. This training framework can be further divided into two scenarios: UE-first training and NW-first training. UE-first training involves first training a complete model on the UE side, then sending the information required for the NW to train a matching model (typically the input and output data of the model to be trained on the NW side). Finally, the NW trains the required model (typically the decoding model) on its own side. In contrast, NW-first training means training the complete model on the NW side first, then sending the information required for UE to train a matching model (generally the input and output data of the model to be trained on the UE side) to the UE side, and finally the UE trains the required model (generally the coding model) on its side.

[0085] When CSI compression requires a payload configuration that was not considered in the previous model training, it is often necessary to update the model to meet this requirement. In the existing AI CSI compression training collaboration types, the method of updating the model is: 1) For type1, it is generally necessary to re-deliver the complete encoder to the UE side (or re-deliver the decoder to the NW side); 2) For type2, it is necessary to re-train the model; 3) For type3, it is necessary to re-deliver the relevant model information (such as the input and output data of the model) to update the model. Generally speaking, the overhead of re-delivering the model is lower than the overhead of retraining the model, so type1 has an advantage in flexible model updates compared to type2 and type3. However, when the structure of the encoder is more complex or the parameter scale is large, the overhead of model transmission is still high, which brings obvious challenges to model updates.

[0086] The CSI feedback method provided in the embodiments of the present application is described in detail below with reference to some embodiments and their application scenarios in conjunction with the accompanying drawings.

[0087] Referring to FIG. 2 , an embodiment of the present application provides a CSI feedback method, which is performed by a terminal and includes:

[0088] Step 201: The terminal receives first data from a network-side device;

[0089] Step 202: The terminal updates the target layer in the coding AI unit according to the first data to obtain an updated coding AI unit;

[0090] Step 203: The terminal encodes the CSI by updating the encoding AI unit to obtain CSI feedback information;

[0091] Step 204: The terminal sends CSI feedback information to the network side device;

[0092] The coding AI unit is an AI unit used by the terminal to encode the CSI, and the first data includes information associated with updating the target layer in the coding AI unit.

[0093] In this embodiment of the present application, the terminal updates the target layer in the coding AI unit based on data received from the network, and then encodes the CSI by updating the coding AI unit to obtain CSI feedback information and feed it back to the network. This allows for a low-overhead update of the AI ​​unit, thereby improving the flexibility of CSI feedback processing.

[0094] It should be noted that the encoding AI unit for encoding the CSI in the terminal involved in the embodiments of the present application, and the decoding AI unit for decoding the CSI feedback information in the network side device involved below, can be understood as the AI ​​units currently used by the terminal side and the network side. During the CSI feedback process, the terminal side encodes the CSI through the encoding AI unit to achieve CSI compression to obtain CSI feedback information. After the terminal side sends the CSI feedback information to the network side, the network side decodes the CSI feedback information through the decoding AI unit to obtain specific CSI.

[0095] In one possible implementation, the encoding AI unit and the decoding AI unit are pre-trained AI units.

[0096] It is understandable that the pre-training of the above-mentioned encoding AI unit and decoding AI unit can be implemented using several types of training collaboration of the AI / ML CSI compression model described above, so that the AI ​​units on both sides can match.

[0097] It should be noted that, for the sake of convenience of description, in the description of the technical solution of this application, the encoding AI unit can be called an encoder, and the decoding AI unit can be called a decoder accordingly.

[0098] In one possible implementation, the target layer satisfies at least one of the following:

[0099] (1) The output of the target layer is the output of the encoding AI unit;

[0100] (2) The input of the target layer is the output of other parts of the encoding AI unit except the target layer.

[0101] In an embodiment of the present application, referring to FIG4a , the structure of the coding AI unit is improved, and the coding AI unit is divided into a target layer and a part other than the target layer, and the target layer is specifically located at the tail of the coding AI unit, that is, the output of the target layer is the output of the coding AI unit, and the input of the target layer is the output of other parts.

[0102] Optionally, the target layer is called the adaptation layer, and the rest is called the fixed layer. When the CSI feedback-related configuration changes and the AI ​​unit needs to be updated, the target layer in the coding AI unit is updated, achieving a low-overhead AI unit update, thereby improving the flexibility of CSI feedback processing.

[0103] In one possible implementation, the target layer is used to determine the payload of CSI feedback. Specifically, the target layer output is quantized to obtain the payload, where the length of the payload is related to the length of the target layer output. The target layer output is generally a sequence of floating-point numbers, and quantization is specifically the process of mapping the floating-point format sequence into a binary sequence, ultimately obtaining the binary sequence, i.e., the payload.

[0104] In an embodiment of the present application, a change in the configuration related to CSI feedback, specifically a change in the payload of the CSI feedback, is taken as an example for explanation. Accordingly, the target layer is used to determine the payload of the CSI feedback. In this way, when the payload of the CSI feedback changes and the AI ​​unit needs to be updated, the target layer can be updated to apply the changed payload.

[0105] Specifically, in the technical solution of the present application, the decoder is consistent with the conventional AI CSI compression model, and the encoder is further divided into two parts: a fixed layer and an adaptation layer. The frozen layer generally refers to the part from the input of the encoder to a certain intermediate node, while the adaptation layer refers to the part from the end of the frozen layer to the encoded output. When the model is initially deployed, it is still deployed to each target node using the conventional method (i.e., the three types of training collaboration mentioned in the background part). When the model needs to be updated to change the supported payload, only the adaptation layer is updated, and the frozen layer remains unchanged. Generally speaking, the adaptation layer part will be designed to be relatively simple (for example, only a few layers of multilayer perceptron (MLP) structure are used and the parameter scale is small), so the overhead of updating the adaptation layer (compared to updating the entire encoder part) is also low, thereby obtaining the benefit of reducing the model update overhead.

[0106] Optionally, the structure of the target layer is defined by a protocol or determined by negotiation between the UE and the NW, and may specifically satisfy at least one of the following:

[0107] (1) The number of target layers is 1, 2, or several layers;

[0108] (2) The structure of the target layer is a multilayer perceptron (MLP), a convolutional neural network (CNN), etc.

[0109] In one possible implementation, the first data includes at least one of the following:

[0110] (1) At least part of the model structure information of the target layer; for example, the number of layers included in the model, the number of neurons included in each layer, etc. The embodiments of the present application do not limit the specific content of the model structure information.

[0111] (2) At least part of the model parameters of the target layer; for example, the functions, weights, and other parameters used in neurons. The embodiments of the present application do not limit the specific content of the model parameters.

[0112] The above (1) and (2) are aimed at the network side directly providing the model structure information or model parameters of the target layer in the updated AI unit to the terminal, and the terminal directly updates the target layer based on these information parameters; specifically, the network side can provide the model structure information and model parameters of the target layer at the same time, or the network side can also only provide the model parameters of the target layer.

[0113] (3) A first training data set, wherein the first training data set is used for terminal training to obtain a target layer in an updated coding AI unit.

[0114] The above (3) is aimed at providing training data to the terminal on the network side. The training data is specifically based on the relevant training data of the target layer in the updated coding AI unit obtained through training on the network side. The network side provides the training data to the terminal, and the terminal itself trains to obtain the specific parameters of the target layer in the updated coding AI unit.

[0115] In a possible implementation, before the terminal receives the first data from the network-side device, the method further includes:

[0116] The terminal sends the second data to the network side device;

[0117] Among them, the second data is used for network side device training to obtain the target layer in the updated coding AI unit.

[0118] In an embodiment of the present application, the target layer in the coding AI unit is updated for training on the network side, and specifically the network side performs training based on the data set reported by the UE side.

[0119] In one possible implementation, the second data includes at least one of the following:

[0120] (1) A second training data set, which includes the input of the target layer in the encoding AI unit and the target output of the decoding AI unit. The decoding AI unit is an AI unit that decodes the CSI feedback information on the network side device. The input of the target layer corresponds to the output of the decoding AI unit in a one-to-one manner.

[0121] It should be noted that the second training data set is a different training data set from the above-mentioned first training data set. On the one hand, the CSI feedback configurations targeted by the two may be different. For example, when the payload supported by CSI feedback needs to be changed from 64 bits to 116 bits, the second training data set pre-provided by the terminal to the network side is a training data set with a payload of 64 bits, and the network side is trained based on a payload of 116 bits during the training process, so that the first training data set provided by the network side to the terminal is a training data set with a payload of 116 bits; on the other hand, the range, dimension, and amount of data contained may also be different. The second training data set provided by the terminal to the network side is the input of the target layer and the output of the decoding AI unit, while the first training data set provided by the network side to the terminal is the input of the target layer and the output of the target layer, and there is no necessary connection between the amount of data contained in the first data set and the amount of data contained in the second data set.

[0122] (2) Encode all model information of the AI ​​unit;

[0123] (3) Encode model information of other parts of the AI ​​unit except the target layer.

[0124] The above (2) and (3) are aimed at the terminal directly providing specific model-related information of the encoding AI unit to the network side.

[0125] In one possible implementation, the method further includes at least one of the following:

[0126] (1) The terminal periodically sends information about the coding AI unit currently used by the terminal to the network device according to the configuration of the network device;

[0127] (2) The terminal sends, to the network device, information related to the coding AI unit currently used by the terminal according to the trigger instruction of the network device based on the first event;

[0128] (3) The terminal proactively sends information about the encoding AI unit currently used by the terminal to the network-side device based on the second event;

[0129] (4) The terminal sends the input and output of the currently used coding AI unit to the network side device;

[0130] The first event and the second event are associated with CSI feedback.

[0131] In an embodiment of the present application, the network side needs to monitor whether the encoder model on the terminal side has changed. Once a change occurs, the terminal side needs to re-report the data set or model used for training the adaptation layer on the network side. The specific monitoring method can be as described in (1) to (4) above. In this way, the network side can promptly learn that the encoder model on the terminal side has changed, and perform model training updates based on the data set or model used for training the adaptation layer on the network side that has been re-reported by the terminal side, ensuring that the models used on the terminal side and the network side match each other.

[0132] It should be noted that the first event may include at least one of the following:

[0133] (a) The performance of the CSI feedback unit monitored by the network side is lower than the target indicator;

[0134] (b) The network side monitors changes in the wireless environment, and the degree of change exceeds the target indicator or the applicable scope or applicable conditions / scenarios of the AI ​​unit currently used for CSI feedback;

[0135] (c) The network side monitors the AI ​​computing power, and the currently available AI computing power or the remaining AI computing power is lower than the usage condition of the AI ​​unit currently used for CSI feedback;

[0136] The second event may include at least one of the following:

[0137] (a) The UE monitors the performance of the CSI feedback unit, which is lower than the target indicator;

[0138] (b) The UE monitors changes in the wireless environment, and the degree of change exceeds the target indicator or the applicable scope or applicable conditions / scenarios of the AI ​​unit currently used for CSI feedback;

[0139] (c) The UE monitors the AI ​​computing power, and the currently available AI computing power or the remaining AI computing power is lower than the usage condition of the AI ​​unit currently used for CSI feedback;

[0140] In one possible implementation, the method further includes any one of the following:

[0141] (1) During the terminal capability reporting phase, the terminal sends first information to the network device based on an instruction from the network device, where the first information is used to indicate whether the terminal supports the coding AI unit containing the target layer;

[0142] (2) During the terminal capability reporting phase, the terminal actively sends first information to the network-side device, where the first information is used to indicate whether the terminal supports the coding AI unit containing the target layer.

[0143] Referring to FIG3 , an embodiment of the present application provides a CSI feedback method, which is performed by a network-side device and includes:

[0144] Step 301: The network-side device trains to obtain the target layer in the updated coding AI unit;

[0145] Step 302: The network-side device sends first data to the terminal;

[0146] Step 303: The network-side device receives CSI feedback information from the terminal;

[0147] Among them, the first data is used by the terminal to update the target layer in the coding AI unit to obtain an updated coding AI unit. The coding AI unit is an AI unit for the terminal to encode CSI, and the first data contains information associated with the target layer in the updated coding AI unit.

[0148] It should be noted that the network side solution is the counterpart of the terminal side solution, and the corresponding content can be understood by referring to the corresponding content on the terminal side.

[0149] In this embodiment of the present application, the terminal updates the target layer in the coding AI unit based on data received from the network, and then encodes the CSI by updating the coding AI unit to obtain CSI feedback information and feed it back to the network. This allows for a low-overhead update of the AI ​​unit, thereby improving the flexibility of CSI feedback processing.

[0150] In one possible implementation, the target layer satisfies at least one of the following:

[0151] The output of the target layer is the output of the encoding AI unit;

[0152] The input of the target layer is the output of other parts of the encoding AI unit except the target layer.

[0153] In a possible implementation, the target layer is used to determine the payload of the CSI feedback.

[0154] In one possible implementation, the first data includes at least one of the following:

[0155] At least part of the model structure information of the target layer;

[0156] at least some of the model parameters of the target layer;

[0157] The first training data set is used for terminal training to obtain a target layer in the updated coding AI unit.

[0158] In one possible implementation, the network-side device trains and updates the target layer in the coding AI unit, including:

[0159] The network side device receives second data from the terminal;

[0160] The network side device trains and updates the target layer in the coding AI unit based on the second data.

[0161] In one possible implementation, the second data includes at least one of the following:

[0162] A second training data set includes the input of the target layer in the encoding AI unit and the output of the decoding AI unit. The decoding AI unit is an AI unit on the network side device that decodes CSI feedback information. The input of the target layer corresponds to the output of the decoding AI unit in a one-to-one manner.

[0163] Encodes all model information of the AI ​​unit;

[0164] Encodes model information of other parts of the AI ​​unit except the target layer.

[0165] In one possible implementation, the method further includes:

[0166] The network-side device determines whether the coding AI unit used by the terminal has changed based on at least one of the following:

[0167] The terminal periodically sends information about the encoding AI unit currently used by the terminal to the network device according to the configuration of the network device;

[0168] The terminal sends, to the network side device, information related to the encoding AI unit currently used by the terminal according to the triggering instruction of the network side device based on the first event;

[0169] The terminal proactively sends to the network device information related to the encoding AI unit currently used by the terminal based on the second event;

[0170] The input and output of the currently used coding AI unit sent by the terminal to the network side device;

[0171] The first event and the second event are associated with CSI feedback.

[0172] In one possible implementation, the method further includes any one of the following:

[0173] In the terminal capability reporting phase, the network side device instructs the terminal to send first information to the network side device, where the first information is used to indicate whether the terminal supports the coding AI unit including the target layer;

[0174] During the terminal capability reporting phase, the network side device receives first information actively sent by the terminal, where the first information is used to indicate whether the terminal supports the coding AI unit containing the target layer.

[0175] The technical solution of the present application is described below in conjunction with specific application embodiments. It should be noted that in the following embodiments, the training collaboration type of the AI / ML CSI compression model is taken as an example of separate (or step-by-step) training on multiple nodes, where Example 1 corresponds to the case of UE-first separate training, and Example 2 corresponds to the case of NW-first separate training.

[0176] Example 1: This example considers the process of adopting the technical solution of this application in the case of UE-first training. The flowchart is shown in Figure 4b:

[0177] The specific steps are as follows:

[0178] 1. Data collection on the UE side.

[0179] 2. The UE trains the local encoder and decoder. The encoder structure consists of a frozen layer and an adaptation layer. The specific design of the adaptation layer can be implemented according to: i) protocol specifications; ii) negotiation between the NW and UE. For example, the MLP layer at the end of the encoder can be set as the adaptation layer.

[0180] 3. The UE side reports the data set used for training the decoder and adaptation layer on the NW side to the NW, where the data set includes the one-to-one correspondence between the input of the adaptation layer and the output of the decoder.

[0181] 4. The NW trains the adaptation layer and decoder based on the dataset reported by the UE (the feedback payload used during training can be different from the payload used by the UE to train the local encoder in step 2, such as the 64-bit payload in Figure 4b). After training is complete, the NW sends the adaptation layer to the UE using model transfer, which can be either sending only the model parameters or the entire model.

[0182] 5. The UE uses the local frozen layer and the adaptation layer received from the NW side to form a complete encoder for CSI compression feedback.

[0183] 6. When the NW side needs to change the CSI feedback payload, it will retrain the adaptation layer and decoder based on the new payload in step 3 (e.g., 116 bits in Figure 4b), and send the updated adaptation layer to the UE side in the same way as step 4.

[0184] 7. The UE receives the updated adaptation layer and combines it with the local frozen layer to form a complete encoder, thereby providing CSI feedback according to the updated payload.

[0185] Example 2: This example considers the process of adopting the technical solution of this application in the case of NW-first training. The flow chart is shown in FIG4c :

[0186] The specific steps are:

[0187] 1. Data collection is performed on the NW side.

[0188] 2. The NW side trains the local encoder and decoder. The encoder structure consists of a frozen layer and an adaptation layer. The specific design of the adaptation layer can be implemented according to: i) protocol specifications; ii) negotiation between the NW and UE. For example, the MLP layer at the end of the encoder can be set as the adaptation layer.

[0189] 3. The NW sends a first dataset to the UE, which includes the encoder input (also the frozen layer input), frozen layer output, and corresponding decoder output, trained in step 2. These three data sets have a one-to-one correspondence. This can be sent as a triplet of (encoder input, encoder output, decoder output), or as a binary tuple (e.g., encoder input, frozen layer output) followed by a mapping between the remaining data (e.g., decoder output) and the binary.

[0190] 4. The UE side trains the frozen layer of the local encoder based on the first data set (actually based on the frozen layer input and frozen layer output in the first data set).

[0191] 5. The UE reports a second data set to the network, including the frozen layer output and decoder output obtained in step 4. There is a one-to-one correspondence between the two, which can be obtained based on the first data set (for example, the encoder input in the first data set is passed through the frozen layer model in step 4 to obtain the frozen layer output, and then the decoder output corresponding to the encoder input is found based on the first data set). Note that the frozen layer output here is different from the frozen layer output in the first data set. The frozen layer output in this step is obtained based on the frozen layer model trained in step 4.

[0192] 6. The NW side updates the adaptation layer and decoder according to the second data set (for example, by fine-tuning), and transmits the updated adaptation layer to the UE side.

[0193] 7. The UE side combines the updated adaptation layer in step 6 with the local frozen layer to form a complete encoder for CSI compression feedback.

[0194] 8. When the NW side needs to change the CSI feedback payload, it will retrain the adaptation layer and decoder based on the new payload in step 3 (e.g., 116 bits in Figure 4b), and send the updated adaptation layer to the UE side in the same way as step 4.

[0195] 9. The UE receives the updated adaptation layer and combines it with the local frozen layer to form a complete encoder, thereby providing CSI feedback according to the updated payload.

[0196] The CSI feedback method provided in the embodiment of the present application may be performed by a CSI feedback device. In the embodiment of the present application, the CSI feedback device performing the CSI feedback method is taken as an example to illustrate the CSI feedback device provided in the embodiment of the present application.

[0197] Referring to FIG5 , an embodiment of the present application provides a CSI feedback device, which can be applied to a terminal and includes:

[0198] A first receiving module 501 is configured for a terminal to receive first data from a network-side device;

[0199] An updating module 502, configured for the terminal to update a target layer in a coding AI unit according to the first data to obtain an updated coding AI unit;

[0200] A first processing module 503 is configured for the terminal to encode the CSI through the updated encoding AI unit to obtain CSI feedback information;

[0201] A first sending module 504 is configured for the terminal to send the CSI feedback information to the network side device;

[0202] The encoding AI unit is an AI unit used by the terminal to encode the CSI, and the first data includes information associated with a target layer in the updated encoding AI unit.

[0203] Optionally, the target layer satisfies at least one of the following:

[0204] The output of the target layer is the output of the encoding AI unit;

[0205] The input of the target layer is the output of other parts of the encoding AI unit except the target layer.

[0206] Optionally, the target layer is used to determine a payload of CSI feedback.

[0207] Optionally, the first data includes at least one of the following:

[0208] At least part of the model structure information of the target layer;

[0209] at least some model parameters of the target layer;

[0210] A first training data set, where the first training data set is used for the terminal training to obtain a target layer in the updated coding AI unit.

[0211] Optionally, the device further comprises:

[0212] a third sending module, configured to, before the terminal receives the first data from the network side device, send the second data by the terminal to the network side device;

[0213] The second data is used for training the network side device to obtain the target layer in the updated coding AI unit.

[0214] Optionally, the second data includes at least one of the following:

[0215] a second training data set, the second training data set including an input of a target layer in the encoding AI unit and an output of a decoding AI unit, the decoding AI unit being an AI unit of the network-side device that decodes CSI feedback information, the input of the target layer corresponding to the output of the decoding AI unit in a one-to-one manner;

[0216] All model information of the encoding AI unit;

[0217] Model information of other parts of the encoding AI unit except the target layer.

[0218] Optionally, the encoding AI unit and the decoding AI unit are pre-trained AI units.

[0219] Optionally, the device further comprises:

[0220] The fourth sending module is configured to:

[0221] The terminal periodically sends relevant information of the encoding AI unit currently used by the terminal to the network side device according to the configuration of the network side device;

[0222] The terminal sends, to the network side device, information related to the encoding AI unit currently used by the terminal according to the trigger instruction of the network side device based on the first event;

[0223] The terminal actively sends, based on the second event, to the network-side device, information related to the encoding AI unit currently used by the terminal;

[0224] The terminal sends the input of the currently used coding AI unit and the output of the currently used coding AI unit to the network side device;

[0225] The first event and the second event are associated with CSI feedback.

[0226] Optionally, the device further comprises:

[0227] The fifth sending module is configured to:

[0228] In the terminal capability reporting phase, the terminal sends first information to the network side device based on an instruction of the network side device, where the first information is used to indicate whether the terminal supports the coding AI unit including the target layer;

[0229] During the terminal capability reporting phase, the terminal actively sends first information to the network side device, where the first information is used to indicate whether the terminal supports the coding AI unit containing the target layer.

[0230] 6 , an embodiment of the present application provides a CSI feedback device, which can be applied to a network-side device. The device includes:

[0231] Training module 601, used for training the network side device to obtain the target layer in the updated coding AI unit;

[0232] A second sending module 602 is configured for the network side device to send first data to the terminal;

[0233] A second receiving module 603, wherein the network side device receives CSI feedback information from the terminal;

[0234] Among them, the first data is used by the terminal to update the target layer in the coding AI unit to obtain an updated coding AI unit, and the coding AI unit is an AI unit for the terminal to encode CSI, and the first data contains information associated with the target layer in the updated coding AI unit.

[0235] Optionally, the target layer satisfies at least one of the following:

[0236] The output of the target layer is the output of the encoding AI unit;

[0237] The input of the target layer is the output of other parts of the encoding AI unit except the target layer.

[0238] Optionally, the target layer is used to determine the payload of CSI feedback.

[0239] Optionally, the first data includes at least one of the following:

[0240] At least part of the model structure information of the target layer;

[0241] at least some model parameters of the target layer;

[0242] A first training data set, where the first training data set is used for the terminal training to obtain a target layer in the updated coding AI unit.

[0243] Optionally, the training module is specifically used to:

[0244] The network side device receives second data from the terminal;

[0245] The network side device trains and updates the target layer in the encoding AI unit based on the second data.

[0246] Optionally, the second data includes at least one of the following:

[0247] a second training data set, the second training data set including an input of a target layer in the encoding AI unit and an output of a decoding AI unit, the decoding AI unit being an AI unit of the network-side device that decodes CSI feedback information, the input of the target layer corresponding to the output of the decoding AI unit in a one-to-one manner;

[0248] All model information of the encoding AI unit;

[0249] Model information of other parts of the encoding AI unit except the target layer.

[0250] Optionally, the device further comprises:

[0251] A determination module, configured for the network-side device to determine whether the coding AI unit used by the terminal has changed according to at least one of the following:

[0252] The terminal periodically sends to the network side device information related to the encoding AI unit currently used by the terminal according to the configuration of the network side device;

[0253] The terminal sends, to the network side device, information related to the encoding AI unit currently used by the terminal according to the triggering instruction of the network side device based on the first event;

[0254] The terminal actively sends, based on the second event, to the network-side device, information related to the encoding AI unit currently used by the terminal;

[0255] The input of the currently used coding AI unit and the output of the currently used coding AI unit sent by the terminal to the network side device;

[0256] The first event and the second event are associated with CSI feedback.

[0257] Optionally, the device further comprises

[0258] The second processing module is used for any of the following:

[0259] In the terminal capability reporting phase, the network side device instructs the terminal to send first information to the network side device, where the first information is used to indicate whether the terminal supports the coding AI unit including the target layer;

[0260] During the terminal capability reporting phase, the network side device receives first information actively sent by the terminal, where the first information is used to indicate whether the terminal supports the coding AI unit including the target layer.

[0261] The CSI feedback device in the embodiments of the present application can be an electronic device, such as an electronic device with an operating system, or a component of an electronic device, such as an integrated circuit or chip. The electronic device can be a terminal or other device other than a terminal. For example, the terminal can include but is not limited to the types of terminal 11 listed above, and other devices can include servers, network attached storage (NAS), etc., which are not specifically limited in the embodiments of the present application.

[0262] The CSI feedback device provided in the embodiment of the present application can implement each process implemented in the method embodiments of Figures 2 to 4c and achieve the same technical effect. To avoid repetition, it will not be described here.

[0263] As shown in Figure 7, an embodiment of the present application further provides a communication device 700, including a processor 701 and a memory 702. The memory 702 stores a program or instruction that can be run on the processor 701. For example, when the communication device 700 is a terminal, the program or instruction, when executed by the processor 701, implements the various steps of the above-mentioned method embodiment and can achieve the same technical effect. When the communication device 700 is a network-side device, the program or instruction, when executed by the processor 701, implements the various steps of the above-mentioned method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0264] The present application also provides a terminal comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps in the above-described method embodiment. This terminal embodiment corresponds to the above-described terminal-side method embodiment, and each implementation process and implementation method of the above-described method embodiment can be applied to this terminal embodiment and achieve the same technical effect. Specifically, Figure 8 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of the present application.

[0265] The terminal 800 includes but is not limited to: a radio frequency unit 801, a network module 802, an audio output unit 803, an input unit 804, a sensor 805, a display unit 806, a user input unit 807, an interface unit 808, a memory 809 and at least some of the components of the processor 810.

[0266] Those skilled in the art will appreciate that the terminal 800 may also include a power supply (such as a battery) to power various components. The power supply may be logically connected to the processor 810 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The terminal structure shown in FIG8 does not limit the terminal. The terminal may include more or fewer components than shown, or may combine certain components, or have different component arrangements, which will not be described in detail here.

[0267] It should be understood that in an embodiment of the present application, the input unit 804 may include a graphics processing unit (GPU) 8041 and a microphone 8042, and the graphics processor 8041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 806 may include a display panel 8061, and the display panel 8061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 807 includes a touch panel 8071 and at least one of other input devices 8072. The touch panel 8071 is also called a touch screen. The touch panel 8071 may include two parts: a touch detection device and a touch controller. Other input devices 8072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0268] In the embodiment of the present application, after receiving downlink data from a network-side device, the radio frequency unit 801 may transmit the data to the processor 810 for processing. Furthermore, the radio frequency unit 801 may send uplink data to the network-side device. Typically, the radio frequency unit 801 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and the like.

[0269] The memory 809 can be used to store software programs or instructions and various data. The memory 809 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, applications or instructions required for at least one function (such as a sound playback function, an image playback function, etc.). In addition, the memory 809 may include a volatile memory or a non-volatile memory. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAM bus random access memory (DRRAM). The memory 809 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0270] Processor 810 may include one or more processing units. Optionally, processor 810 integrates an application processor and a modem processor. The application processor primarily handles operations related to the operating system, user interface, and application programs, while the modem processor primarily processes wireless communication signals, such as a baseband processor. It is understood that the modem processor may not be integrated into processor 810.

[0271] The radio frequency unit 801 is used for the terminal to receive first data from the network side device;

[0272] Processor 810 is configured to update, by the terminal, a target layer in a coding AI unit according to the first data to obtain an updated coding AI unit;

[0273] Processor 810, configured for the terminal to encode the CSI through the updated encoding AI unit to obtain CSI feedback information;

[0274] The radio frequency unit 801 is configured for the terminal to send the CSI feedback information to the network side device;

[0275] The encoding AI unit is an AI unit used by the terminal to encode the CSI, and the first data includes information associated with a target layer in the updated encoding AI unit.

[0276] Optionally, the target layer satisfies at least one of the following:

[0277] The output of the target layer is the output of the encoding AI unit;

[0278] The input of the target layer is the output of other parts of the encoding AI unit except the target layer.

[0279] Optionally, the target layer is used to determine a payload of CSI feedback.

[0280] Optionally, the first data includes at least one of the following:

[0281] At least part of the model structure information of the target layer;

[0282] at least some model parameters of the target layer;

[0283] A first training data set, where the first training data set is used for the terminal training to obtain a target layer in the updated coding AI unit.

[0284] Optionally, the device further comprises:

[0285] The radio frequency unit 801 is configured to send, by the terminal, second data to the network side device before the terminal receives first data from the network side device;

[0286] The second data is used for training the network side device to obtain the target layer in the updated coding AI unit.

[0287] Optionally, the second data includes at least one of the following:

[0288] a second training data set, the second training data set including an input of a target layer in the encoding AI unit and an output of a decoding AI unit, the decoding AI unit being an AI unit of the network-side device that decodes CSI feedback information, the input of the target layer corresponding to the output of the decoding AI unit in a one-to-one manner;

[0289] All model information of the encoding AI unit;

[0290] Model information of other parts of the encoding AI unit except the target layer.

[0291] Optionally, the encoding AI unit and the decoding AI unit are pre-trained AI units.

[0292] Optionally, the device further comprises:

[0293] The radio frequency unit 801 is configured to:

[0294] The terminal periodically sends relevant information of the encoding AI unit currently used by the terminal to the network side device according to the configuration of the network side device;

[0295] The terminal sends, to the network side device, information related to the encoding AI unit currently used by the terminal according to the trigger instruction of the network side device based on the first event;

[0296] The terminal actively sends, based on the second event, to the network-side device, information related to the encoding AI unit currently used by the terminal;

[0297] The terminal sends the input of the currently used coding AI unit and the output of the currently used coding AI unit to the network side device;

[0298] The first event and the second event are associated with CSI feedback.

[0299] Optionally, the device further comprises:

[0300] The radio frequency unit 801 is configured to:

[0301] In the terminal capability reporting phase, the terminal sends first information to the network side device based on an instruction of the network side device, where the first information is used to indicate whether the terminal supports the coding AI unit including the target layer;

[0302] During the terminal capability reporting phase, the terminal actively sends first information to the network side device, where the first information is used to indicate whether the terminal supports the coding AI unit containing the target layer.

[0303] It can be understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the method embodiment and achieve the same or corresponding technical effects. To avoid repetition, it will not be described here.

[0304] The present application also provides a network-side device, including a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to execute a program or instruction to implement the steps of the method embodiment. This network-side device embodiment corresponds to the aforementioned network-side device method embodiment, and each implementation process and implementation method of the aforementioned method embodiment are applicable to this network-side device embodiment and can achieve the same technical effects.

[0305] Specifically, embodiments of the present application also provide a network-side device. As shown in Figure 9, the network-side device 900 includes an antenna 91, a radio frequency device 92, a baseband device 93, a processor 94, and a memory 95. Antenna 91 is connected to radio frequency device 92. In the uplink direction, radio frequency device 92 receives information via antenna 91 and sends the received information to baseband device 93 for processing. In the downlink direction, baseband device 93 processes the information to be transmitted and sends it to radio frequency device 92. Radio frequency device 92 processes the received information and then sends it through antenna 91.

[0306] The method executed by the network-side device in the above embodiment may be implemented in the baseband device 93 , which includes a baseband processor.

[0307] The baseband device 93 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 9, one of the chips is, for example, a baseband processor, which is connected to the memory 95 through a bus interface to call the program in the memory 95 and execute the network device operations shown in the above method embodiment.

[0308] The network side device may further include a network interface 96, which is, for example, a Common Public Radio Interface (CPRI).

[0309] Specifically, the network side device 900 of the embodiment of the present application also includes: instructions or programs stored in the memory 95 and executable on the processor 94. The processor 94 calls the instructions or programs in the memory 95 to execute the methods of execution of each module shown in FIG6 and achieve the same technical effect. To avoid repetition, it will not be described here.

[0310] Specifically, the embodiment of the present application further provides a network side device. As shown in FIG10 , the network side device 1000 includes: a processor 1001, a network interface 1002, and a memory 1003. The network interface 1002 is, for example, a common public radio interface (CPRI).

[0311] Specifically, the network side device 1000 of the embodiment of the present application also includes: instructions or programs stored in the memory 1003 and executable on the processor 1001. The processor 1001 calls the instructions or programs in the memory 1003 to execute the method of execution of each module shown in Figure 6 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0312] An embodiment of the present application further provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, the various processes of the above-mentioned CSI feedback method embodiment are implemented and can achieve the same technical effect. To avoid repetition, they are not further described here.

[0313] The processor is the processor in the terminal described in the above embodiment. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk. In some examples, the readable storage medium may be a non-transitory readable storage medium.

[0314] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to run a program or instruction to implement the various processes of the above-mentioned CSI feedback method embodiment, and can achieve the same technical effect. To avoid repetition, these are not described here.

[0315] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0316] An embodiment of the present application further provides a computer program / program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the above-mentioned CSI feedback method embodiment and can achieve the same technical effects. To avoid repetition, they are not described here.

[0317] An embodiment of the present application also provides a wireless communication system, including: a terminal and a network side device, wherein the terminal can be used to execute the steps of the terminal side method described above, and the network side device can be used to execute the steps of the network side method described above.

[0318] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the opposite order according to the functions involved. For example, the described method may be performed in an order different from that described, and various steps may also be added, omitted or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0319] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of a computer software product plus a necessary general-purpose hardware platform, or of course, by hardware. The computer software product is stored in a storage medium (such as ROM, RAM, magnetic disk, optical disk, etc.) and includes a number of instructions for enabling a terminal or network-side device to execute the methods described in each embodiment of the present application.

[0320] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of this application, ordinary technicians in this field can also make many forms of implementation methods without departing from the purpose of this application and the scope of protection of the claims. These implementation methods are all within the protection of this application.

Claims

1. A channel state information (CSI) feedback method, comprising: The terminal receives first data from the network side device; The terminal updates the target layer in the coding artificial intelligence AI unit according to the first data to obtain an updated coding AI unit; The terminal encodes the CSI through the update encoding AI unit to obtain CSI feedback information; The terminal sends the CSI feedback information to the network side device; The coding AI unit is an AI unit for encoding CSI by the terminal, and the first data includes information associated with a target layer in the updated coding AI unit.

2. The method according to claim 1, wherein: The target layer satisfies at least one of the following: The output of the target layer is the output of the encoding AI unit; The input of the target layer is the output of other parts of the encoding AI unit except the target layer.

3. The method according to claim 1, wherein: The target layer is used to determine the effective load payload of the CSI feedback.

4. The method according to claim 1, wherein: The first data includes at least one of the following: At least part of the model structure information of the target layer; at least some model parameters of the target layer; A first training data set, wherein the first training data set is used for the terminal training to obtain a target layer in the update coding AI unit.

5. The method according to claim 1, wherein: Before the terminal receives the first data from the network side device, the method further includes: The terminal sends second data to the network side device; Among them, the second data is used for training the network side device to obtain the target layer in the updated coding AI unit.

6. The method according to claim 5, wherein: The second data includes at least one of the following: A second training data set, wherein the second training data set includes an input of a target layer in the encoding AI unit and an output of a decoding AI unit, wherein the decoding AI unit is an AI unit that performs decoding processing on the CSI feedback information by the network side device, and the input of the target layer corresponds to the output of the decoding AI unit in a one-to-one manner; All model information of the encoding AI unit; Model information of other parts in the encoding AI unit except the target layer.

7. The method according to claim 6, wherein: The encoding AI unit and the decoding AI unit are pre-trained AI units.

8. The method according to claim 1, further comprising at least one of the following: The terminal periodically sends the network side device a message of the terminal currently using the network side device according to the configuration of the network side device. Information about the coded AI unit used; The terminal sends, to the network side device, relevant information of the encoding AI unit currently used by the terminal according to the trigger indication of the network side device based on the first event; The terminal actively sends, based on the second event, to the network side device, relevant information of the encoding AI unit currently used by the terminal; The terminal sends the input of the currently used coding AI unit and the output of the currently used coding AI unit to the network side device; The first event and the second event are associated with CSI feedback.

9. The method according to claim 1, further comprising any one of the following: In the terminal capability reporting stage, the terminal sends first information to the network side device based on the instruction of the network side device, where the first information is used to indicate whether the terminal supports the coding AI unit including the target layer; During the terminal capability reporting stage, the terminal actively sends first information to the network side device, where the first information is used to indicate whether the terminal supports the coding AI unit containing the target layer.

10. A CSI feedback method, comprising: The network-side device training obtains the target layer in the updated coding AI unit; The network side device sends first data to the terminal; The network side device receives CSI feedback information from the terminal; Among them, the first data is used by the terminal to update the target layer in the coding AI unit to obtain an updated coding AI unit, and the coding AI unit is an AI unit for the terminal to encode CSI, and the first data contains information associated with the target layer in the updated coding AI unit.

11. The method according to claim 10, wherein: The target layer satisfies at least one of the following: The output of the target layer is the output of the encoding AI unit; The input of the target layer is the output of other parts of the encoding AI unit except the target layer.

12. The method according to claim 10, wherein: The target layer is used to determine the payload of the CSI feedback.

13. The method according to claim 10, wherein: The first data includes at least one of the following: At least part of the model structure information of the target layer; at least some model parameters of the target layer; A first training data set, wherein the first training data set is used for the terminal training to obtain a target layer in the update coding AI unit.

14. The method according to claim 10, wherein: The network side device trains and updates the target layer in the coding AI unit, including: The network side device receives second data from the terminal; The network side device trains and updates the target layer in the coding AI unit based on the second data.

15. The method according to claim 14, wherein: The second data includes at least one of the following: A second training data set includes the input and decoding of the target layer in the encoding AI unit The output of the AI ​​unit, the decoding AI unit is an AI unit of the network side device that decodes the CSI feedback information, and the input of the target layer corresponds to the output of the decoding AI unit one by one; All model information of the encoding AI unit; Model information of other parts in the encoding AI unit except the target layer.

16. The method according to claim 10, further comprising: The network side device determines whether the coding AI unit used by the terminal changes according to at least one of the following: The terminal periodically sends to the network side device, according to the configuration of the network side device, information related to the coding AI unit currently used by the terminal; The terminal sends, to the network side device, information related to the encoding AI unit currently used by the terminal according to the trigger indication of the network side device based on the first event; The terminal actively sends the relevant information of the encoding AI unit currently used by the terminal to the network side device based on the second event; The input of the currently used coding AI unit and the output of the currently used coding AI unit sent by the terminal to the network side device; The first event and the second event are associated with CSI feedback.

17. The method according to claim 10, further comprising any one of the following: In the terminal capability reporting stage, the network side device instructs the terminal to send first information to the network side device, where the first information is used to indicate whether the terminal supports the coding AI unit including the target layer; During the terminal capability reporting phase, the network side device receives first information actively sent by the terminal, where the first information is used to indicate whether the terminal supports the coding AI unit containing the target layer.

18. A CSI feedback device, comprising: A first receiving module, used for a terminal to receive first data from a network side device; An updating module, configured for the terminal to update a target layer in a coding AI unit according to the first data to obtain an updated coding AI unit; A first processing module, configured for the terminal to encode the CSI through the update encoding AI unit to obtain CSI feedback information; A first sending module, configured for the terminal to send the CSI feedback information to the network side device; The coding AI unit is an AI unit for encoding CSI by the terminal, and the first data includes information associated with a target layer in the updated coding AI unit.

19. A CSI feedback device, comprising: The training module is used for training network-side devices to obtain the target layer in the updated coding AI unit; A second sending module, used for the network side device to send first data to the terminal; A second receiving module, the network side device receives CSI feedback information from the terminal; The first data is used by the terminal to update the target layer in the coding AI unit to obtain an updated coding AI unit, and the coding AI unit is an AI unit for the terminal to encode the CSI. The first data includes Information associated with the target layer in the update encoding AI unit.

20. A terminal, comprising a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the CSI feedback method according to any one of claims 1 to 9 are implemented.

21. A network side device, comprising a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the CSI feedback method according to any one of claims 10 to 18 are implemented.

22. A readable storage medium storing a program or instruction, wherein the program or instruction, when executed by a processor, implements the steps of the CSI feedback method according to any one of claims 1 to 9, or implements the steps of the CSI feedback method according to any one of claims 10 to 18.

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