Ai-based CSI compression method and apparatus, and terminal and network-side device

Abstract

The present application belongs to the technical field of communications. Disclosed are an AI-based CSI compression method and apparatus, and a terminal and a network-side device. The AI-based CSI compression method of the embodiments of the present application comprises: a terminal sending to a network-side device capability information of the terminal regarding an artificial intelligence (AI) unit, wherein the AI unit is configured to compress channel state information (CSI); the terminal receiving CSI configuration information sent by the network-side device on the basis of the capability information; and on the basis of the CSI configuration information, the terminal compressing the CSI by means of the AI unit.

Description

An AI-based CSI compression method, device, terminal, and network-side equipment

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese patent application filed with the China Patent Office on January 4, 2024, with application number 202410015933.1 and entitled “A CSI compression method, device, terminal and network side equipment based on AI”, the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present application belongs to the field of communication technology, and specifically relates to an AI-based CSI compression method, apparatus, terminal, and network-side equipment. Background Art

[0004] Information theory shows that accurate channel state information (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 eigen beamforming to maximize the strength of the received signal, or to suppress interference (such as inter-cell interference, interference between multiple users, etc.). Therefore, since the introduction of multi-input multi-output (MIMO) technology, CSI acquisition has always been a research hotspot.

[0005] Typically, the base station sends a Channel State Information Reference Signal (CSI-RS) on certain time-frequency resources in a certain time slot. The terminal performs channel estimation based on the CSI-RS, calculates the channel information for this 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.

[0006] To further reduce CSI feedback overhead, an evolved codebook solution is provided. This allows the terminal to report the PMI for each subband instead of reporting it based on a delay. Since channels in the delay domain are more concentrated, a PMI with less delay can approximate the PMI for all subbands. This means that the delay domain information is compressed before reporting.

[0007] Furthermore, to better compress channel information, CSI can be compressed using neural networks or machine learning methods, namely, using AI units to compress CSI. The terminal and network-side equipment need to exchange necessary information to implement CSI compression based on the AI ​​unit. However, the information exchange method between the terminal and network-side equipment when compressing CSI based on the AI ​​unit is currently not provided. Summary of the Invention

[0008] The embodiments of the present application provide an AI-based CSI compression method, apparatus, terminal, and network-side equipment, and provide an information interaction method when the terminal and the network-side equipment compress CSI based on the AI ​​unit.

[0009] In a first aspect, an AI-based CSI compression method is provided, the method comprising:

[0010] The terminal sends capability information of the artificial intelligence (AI) unit of the terminal to the network device, where the AI ​​unit is used to compress the channel state information (CSI).

[0011] The terminal receives CSI configuration information sent by the network side device according to the capability information;

[0012] The terminal compresses the CSI through the AI ​​unit according to the CSI configuration information.

[0013] In a second aspect, an AI-based CSI compression method is provided, the method comprising:

[0014] The network-side device receives capability information of the terminal regarding an artificial intelligence (AI) unit sent by the terminal, where the AI ​​unit is used to compress channel state information (CSI);

[0015] The network-side device determines, according to the capability information, CSI configuration information for instructing the terminal to compress the CSI;

[0016] The network side device sends the CSI configuration information to the terminal.

[0017] In a third aspect, an AI-based CSI compression device is provided, which is applied to a terminal. The device includes:

[0018] A first sending module is configured to send capability information of the terminal about an artificial intelligence (AI) unit to a network-side device, where the AI ​​unit is configured to compress channel state information (CSI);

[0019] A first receiving module is configured to receive CSI configuration information sent by the network side device according to the capability information;

[0020] A CSI compression module is configured to compress the CSI through the AI ​​unit according to the CSI configuration information.

[0021] In a fourth aspect, an AI-based CSI compression device is provided, which is applied to a network-side device. The device includes:

[0022] a second receiving module, configured to receive capability information of an artificial intelligence (AI) unit of the terminal sent by the terminal, wherein the AI ​​unit is configured to compress channel state information (CSI);

[0023] an information determining module, configured to determine, based on the capability information, CSI configuration information for instructing the terminal to compress the CSI;

[0024] The second sending module is configured to send the CSI configuration information to the terminal.

[0025] 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.

[0026] In a sixth aspect, a terminal is provided, comprising a processor and a communication interface;

[0027] Wherein, the communication interface is used for:

[0028] Sending capability information of the terminal about an artificial intelligence (AI) unit to a network device, where the AI ​​unit is used to compress channel state information (CSI);

[0029] receiving CSI configuration information sent by the network-side device according to the capability information;

[0030] The processor is configured to compress the CSI through the AI ​​unit according to the CSI configuration information.

[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 an eighth aspect, a network-side device is provided, including a processor and a communication interface;

[0033] The communication interface is used to: receive capability information of the terminal regarding an artificial intelligence (AI) unit sent by the terminal, and the AI ​​unit is used to compress channel state information (CSI);

[0034] The processor is configured to: determine, according to the capability information, CSI configuration information for instructing the terminal to compress the CSI;

[0035] The communication interface is further used to: send the CSI configuration information to the terminal.

[0036] 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.

[0037] In the tenth aspect, an AI-based CSI compression 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.

[0038] 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 steps of the method described in the first aspect, or to implement the steps of the method described in the second aspect.

[0039] 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 steps of the method described in the first aspect, or to implement the steps of the method described in the second aspect.

[0040] In a thirteenth aspect, an embodiment of the present application provides an AI-based CSI compression device, which is used to execute the steps of the AI-based CSI compression method as described in the first aspect or the second aspect.

[0041] In an embodiment of the present application, the terminal can send the terminal's capability information about the AI ​​unit to the network-side device, thereby receiving the CSI configuration information sent by the network-side device based on the capability information, and then compressing the CSI through the AI ​​unit based on the received CSI configuration information. It can be seen that in an embodiment of the present application, the terminal can exchange its capability information about the AI ​​unit used to compress CSI with the network-side device, so that the network-side device can configure how the terminal performs CSI compression based on the terminal's capability information about the AI ​​unit. Therefore, an embodiment of the present application provides a method for information exchange between the terminal and the network-side device when compressing CSI based on the AI ​​unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] FIG1 is a block diagram of a wireless communication system to which embodiments of the present application may be applied;

[0043] FIG2 is a schematic diagram of a neural network in an embodiment of the present application;

[0044] FIG3 is a schematic diagram of neurons in a neural network according to an embodiment of the present application;

[0045] FIG4 is a flowchart of an AI-based CSI compression method in an embodiment of the present application;

[0046] FIG5 is a schematic diagram of a packaged time-frequency-spatial CSI compression solution according to an embodiment of the present application;

[0047] FIG6 is a schematic diagram of a progressive time-frequency-spatial CSI compression scheme for a multi-slot sharing model according to an embodiment of the present application;

[0048] FIG7 is a schematic diagram of a progressive time-frequency-spatial CSI compression scheme for a dedicated model on multiple slots in an embodiment of the present application;

[0049] FIG8 is a schematic diagram of a slot interval pattern in the inference phase according to an embodiment of the present application;

[0050] FIG9 is a flowchart of another AI-based CSI compression method in an embodiment of the present application;

[0051] FIG10 is a structural block diagram of an AI-based CSI compression device in an embodiment of the present application;

[0052] FIG11 is a structural block diagram of another AI-based CSI compression device in an embodiment of the present application;

[0053] FIG12 is a structural block diagram of a communication device in an embodiment of the present application;

[0054] FIG13 is a block diagram of a terminal in an embodiment of the present application;

[0055] FIG14 is a structural block diagram of a network-side device in an embodiment of the present application. Specific embodiments

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] FIG1 is 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-mounted device (VUE), a ship-mounted 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, vehicle-mounted controller, vehicle-mounted module, vehicle-mounted component, vehicle-mounted chip or 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.

[0061] The network-side device 12 may include an access network device or a core network device, wherein the access network device may also be referred to as a 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 (WLAN) access point (AP), or a wireless fidelity (WiFi) node. 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.

[0062] To facilitate understanding of the AI-based CSI compression method of the present application, the following related technologies are first introduced:

[0063] 1. Introduction to CSI Compression Based on Artificial Intelligence (AI) / Machine Learning (ML)

[0064] To reduce overhead, the base station can precode the CSI-RS in advance and send the encoded CSI-RS to each 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.

[0065] Furthermore, in order to better compress channel information, neural network or machine learning methods can be used.

[0066] Artificial intelligence is currently being widely used in various fields. There are many ways to implement AI units, such as neural networks, decision trees, support vector machines, and Bayesian classifiers. This application uses neural networks as an example, but does not limit the specific type of AI module. A schematic diagram of a simple neural network structure is shown in Figure 2.

[0067] In addition, the neural network is composed of neurons, and the schematic diagram of neurons is shown in Figure 3. In Figure 3, a1, a2, ... a K σ(.) represents the activation function. Common activation functions include sigmoid (which maps variables to between 0 and 1), tanh (a shift and contraction of sigmoid), and rectified linear unit (ReLU).

[0068] The parameters of a neural network can be optimized using a gradient optimization algorithm. A gradient optimization algorithm is a type of algorithm that minimizes or maximizes an objective function (sometimes also called a loss function), which 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(.) can be constructed. Based on the input x, the predicted output f(x) can be obtained, and the difference between the predicted value and the true value (f(x)-Y) can be calculated. This is the loss function. The optimization goal of the gradient optimization algorithm is to find the appropriate w (i.e., weight) and b (i.e., bias) to minimize the value of the aforementioned loss function. The smaller the loss value, the closer the model is to the true situation.

[0069] 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, and used as the basis for correcting the weights of each unit. This process of adjusting the weights of each layer, including forward propagation of signals and back propagation of errors, is repeated over and over again. This continuous adjustment of weights 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 has been completed.

[0070] In addition, common optimization algorithms include gradient descent, stochastic gradient descent (SGD), mini-batch gradient descent, momentum method (Momentum), Nesterov (the name of the inventor, specifically stochastic gradient descent with momentum), adaptive gradient descent (ADAptive GRADient descent, Adagrad), Adagrad's extended algorithm (Adadelta), root mean square error deceleration (root mean square prop, RMSprop), Adaptive Moment Estimation (Addam), etc.

[0071] When these optimization algorithms backpropagate errors, they all calculate the derivative / partial derivative of the current neuron based on the error / loss obtained by the loss function, add the influence of the learning rate, the previous gradient / derivative / partial derivative, etc., obtain the gradient, and pass the gradient to the previous layer.

[0072] Specifically, the terminal compresses and encodes the channel information, and the base station decodes the compressed content to recover the channel information. The base station's decoding network and the terminal's encoding network require joint training to achieve a reasonable match. A neural network is formed by the terminal's encoder and the base station's decoder, forming a joint neural network. Joint training is performed by the network. After training is complete, the base station sends the encoder network to the terminal. During inference, the terminal estimates the CSI-RS and calculates the channel information. This calculated channel information or the original estimated channel information is passed through the encoding network to obtain the encoding result. The encoding result is then sent to the base station. The base station receives the encoded result and inputs it into the decoding network to recover the channel information.

[0073] Scalability of AI / ML CSI Compression Models

[0074] Model scalability refers to the ability of a single model to adapt to multiple input / output configurations simultaneously. In CSI compression, model scalability primarily considers the following metrics: the number of subbands input to the encoder, the number of ports input to the encoder, and the length of the payload output by the encoder. The goal is for a CSI compression model to simultaneously support as many of the aforementioned 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). This reduces the overhead of replacing models and maintaining at least one of the multiple models simultaneously. Generally speaking, achieving a certain level of scalability in a CSI compression model requires considering multiple scenarios during the model training phase and incorporating specialized model structures (such as adaptation layers).

[0075] Theoretically, it's possible to consider all possible scalability requirements for CSI compression models during model training, enabling a single model to handle all configurations. However, given the difficulty of model training and the fact that certain network parameters are sometimes not known in advance during training, it's difficult to fully consider all possible scalability requirements for each model in practice. In such cases, retraining the model is still necessary to address this issue.

[0076] 3. Training Collaboration Types for AI / ML CSI Compression Models

[0077] 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 involve deploying the encoder on the UE and the decoder on the network (NW). The (sub-)models deployed on multiple nodes must be paired to function properly. Considering the aforementioned characteristics of the two-end model, 3GPP has identified several basic types of training collaboration:

[0078] (1) Joint training at single entity (also called type 1):

[0079] The training framework is designed to train a complete encoder and decoder model on a network node (UE or NW or a third-party server node, etc.), and then deploy the corresponding model module to the target node through methods such as model transfer (for example, the encoder part is transferred to the UE and the decoder part is transferred to the NW).

[0080] (2) Joint training at multiple entities (also known as type 2)

[0081] 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.

[0082] (3) Separate (or step-by-step) training on multiple nodes (also known as type 3)

[0083] This training framework involves first training a reference model on a node, then sending information about the reference model to the target node. The target node then uses this information to train its own model, ensuring that the node (sub-)models can be paired with each other. For example, the network (NW) first trains a complete encoder and decoder model, confirms that the resulting decoder is the one that will actually be used, and then sends information about the corresponding encoder (typically the encoder's input and output data) to the user end. The user end then uses this information to train its own encoder.

[0084] 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 needed for the NW to train a matching model (typically the input and output data of the model to be trained on the NW side). Conversely, NW-first training involves first training a complete model on the NW side, then sending the information needed for the UE to train a matching model (typically the input and output data of the model to be trained on the UE side).

[0085] The following, in conjunction with the accompanying drawings, describes in detail the AI-based CSI compression method provided in the embodiments of the present application through some embodiments and their application scenarios.

[0086] As shown in FIG4 , an embodiment of the present application provides an AI-based CSI compression method, which may include the following steps 401 to 403:

[0087] Step 401: The terminal sends capability information of the terminal regarding the artificial intelligence (AI) unit to the network side device.

[0088] The AI ​​unit is used to compress the channel state information CSI.

[0089] It should be noted that the AI ​​unit can be used to perform CSI compression in at least one of the time domain, frequency domain, and spatial domain on the CSI, that is, the AI ​​unit can perform at least one of CSI time domain compression, CSI frequency domain compression, and CSI spatial domain compression.

[0090] In addition, the AI ​​unit may also be referred to as an AI model, a machine learning (ML) model, an ML unit, an AI structure, an AI function, an AI characteristic, a neural network, a neural network function, a neural network function, etc.; or the AI ​​unit may also refer to a processing unit that can implement specific algorithms, formulas, processing procedures, capabilities, etc. related to AI, or the AI ​​unit may be a processing method, algorithm, function, module or unit for a specific data set, or the AI ​​unit may be a processing method, algorithm, function, module or unit running on AI / ML related hardware such as a graphics processing unit (GPU), a neural network processor (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 at least one of the input and output of the AI ​​unit / AI model.

[0091] Optionally, the identifier of the AI ​​unit 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, 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.

[0092] In addition, the capability information is used to indicate the terminal's support for the AI ​​unit, that is, the terminal's capability in performing CSI compression.

[0093] Step 402: The terminal receives CSI configuration information sent by the network-side device according to the capability information.

[0094] The CSI configuration information includes configuration information for performing CSI compression, that is, the CSI configuration information is used to instruct the terminal how to perform CSI compression.

[0095] In addition, the CSI configuration information may also be referred to as CSI association information, CSI triggering information, or CSI scheduling information, and is used to indicate how to perform CSI compression.

[0096] As can be seen from step 402, after receiving the capability information reported by the terminal, the network side device determines CSI configuration information according to the capability information to instruct the terminal how to perform CSI compression.

[0097] Step 403: The terminal compresses the CSI through the AI ​​unit according to the CSI configuration information.

[0098] As previously mentioned, the AI ​​unit can be used to perform CSI compression on at least one of the time, frequency, and spatial domains. Specifically, the AI ​​unit can perform at least one of the following: CSI time-domain compression, CSI frequency-domain compression, and CSI spatial-domain compression. In step 403, the terminal determines which one or more of the time, frequency, and spatial domains of CSI compression are performed, depending on the AI ​​unit's functionality and the CSI configuration information.

[0099] It should be noted that time domain compression is time domain joint compression, that is, CSI on multiple time units (such as slots) are combined for compression, thereby further reducing the overhead of CSI reporting or improving CSI reporting accuracy.

[0100] In addition, time-domain joint compression, that is, the reporting method of CSI on multiple time units, is divided into two types: packaged reporting and progressive reporting. Packaged reporting is to report CSI on multiple time units (such as time slots) at one time (as shown in Figure 5 below), while progressive reporting is to report CSI on each time unit (such as time slot) in sequence in an autoregressive manner (as shown in Figure 6 above).

[0101] Furthermore, traditional Type II codebook-based CSI reporting supports both aperiodic (AP) and semi-persistent modes. For the AP mode, from a system perspective, since the network device (e.g., base station) only needs the most recent CSI for a scheduling operation, the terminal only needs to trigger a single CSI report to represent the most recent measurement result. There is no need to continuously report CSI at multiple times. Even with this progressive CSI reporting method in the AP mode, there is still a problem of being unable to control the time of the last CSI transmission. If the network device may have scheduled the terminal long ago, the time-domain correlation between the two CSI reports to be reported will be weak, making it difficult to implement time-domain compression schemes. Therefore, persistent scheduling (e.g., using the SP mode) can create scenarios where time-domain CSI compression is appropriate. That is, progressive time-domain CSI compression primarily relies on the semi-persistent (SP) CSI reporting mechanism.

[0102] As can be seen from the above steps 401 to 403, in the embodiment of the present application, the terminal can send the terminal's capability information about the AI ​​unit to the network-side device, thereby receiving the CSI configuration information sent by the network-side device based on the capability information, and then compressing the CSI through the AI ​​unit based on the received CSI configuration information. It can be seen that in the embodiment of the present application, the terminal can exchange its capability information about the AI ​​unit used to compress CSI with the network-side device, so that the network-side device can configure how the terminal performs CSI compression based on the terminal's capability information about the AI ​​unit. Therefore, the embodiment of the present application provides a method for information interaction between the terminal and the network-side device when compressing CSI based on the AI ​​unit.

[0103] Optionally, the capability information includes at least one of the following items A-1 to A-4:

[0104] Item A-1: ​​Whether the terminal supports AI-based CSI compression;

[0105] Item A-2: the type of the AI ​​unit supported by the terminal;

[0106] The type of the AI ​​unit may include at least one of the following types:

[0107] Type 1 (also called a dedicated model): Different encoders and decoders are used to compress CSI in different time units. As shown in Figure 7, ENC0 and DEC0 are dedicated to reporting CSI in slot 0, and so on. ENC represents the encoder and DEC represents the decoder.

[0108] The second type (also called the shared model) uses the same encoder and decoder to compress CSI in different time units. As shown in Figure 6, a set of ENC0 and DEC0 is applied to CSI reporting in all slots.

[0109] The third type: In some time units, different encoders and decoders are used to compress the CSI in different time units; in another part of the time units, the same encoder and decoder are used to compress the CSI in different time units. For example, the AI ​​unit can compress the CSI of 4 time units, among which the same encoder and decoder can be used to compress the CSI of the first two time units, and different encoders and decoders can be used to compress the CSI of the last two time units.

[0110] Item A-3: First indication information, the first indication information is used to indicate the length of the target time window supported by the terminal, and the target time window includes at least one time unit (e.g., a time slot) for the AI ​​unit to perform CSI compression; that is, it can be understood that the target time window is used to indicate the maximum number of time units processed when the AI ​​unit performs an inference (i.e., performs CSI compression).

[0111] It should be noted that a key feature of the dedicated model is its clear concept of time windows, which defines the maximum number of CSIs that can be processed at different moments. (Generally speaking, the time window must be determined during model training, and the length of the time window during inference must remain consistent with that during training.) Once the number of CSIs to be reported exceeds the maximum number of CSIs that the model can handle in the time domain, inference must be restarted from slot 0. (This means that this solution has difficulty processing CSI reports for slots exceeding the window length during a single inference process, as shown in Figure 7. This may be because no set of ENC and DEC models can process the intermediate information stream (internal information) output by ENC3 and DEC3.) Furthermore, during inference, the position of the current slot within the time window must be aligned with the model used (i.e., ENC1 and DEC1 can only process CSI from the second slot within the window and not from the third slot). Otherwise, model performance degradation due to misalignment is likely to occur.

[0112] Although the dedicated model has the above limitations, its performance ceiling is relatively high, especially when processing CSI in slots later in the time window. The shared model has weaker requirements on the time window because it shares the same set of ENC0 and DEC0 relationships. Its reasoning process can continue in the time domain without significant performance loss (for example, a model trained using CSI in four consecutive slots can be used to compress CSI in six or more consecutive slots because ENC0 and DEC0 can always identify the intermediate information flow). Therefore, this solution has less demand for information exchange. Although the shared model solution is easy to implement, it is limited by the parameter limitations of the contribution model, and its performance is generally weaker than the dedicated model solution, especially for slots later in the time sequence.

[0113] It is understandable that although the shared model has weaker requirements for the time window, a corresponding time window may exist when the shared model is used for CSI compression; similarly, when the third type of AI unit mentioned above performs CSI compression, the corresponding time window may also be stored.

[0114] Optionally, the first indication information includes at least one of the following items B-1 to B-3:

[0115] Item B-1: The maximum length of the target time window supported by the terminal; that is, the available target time window length indicated in the capability information reported by the terminal is the maximum length, on this basis, the network side device can schedule any number of CSI reports that are less than, equal to, or less than the maximum length.

[0116] Item B-2: The set of target time window lengths supported by the terminal; that is, the capability information reported by the terminal can indicate the set of target time window lengths supported by the terminal, and the network side device can select the number of CSIs to be reported from the set when subsequently scheduling CSI reporting.

[0117] Item B-3: Identification information of the target time window length configuration supported by the terminal; that is, during the terminal capability reporting stage, the terminal can not only directly report the supported target time window length, but also report the supported typical target time window length configuration. For example, some typical target time window configurations are stipulated in the protocol, and the identification (such as index or serial number) of these typical configurations can be directly reported when reporting.

[0118] Item A-4: CSI reporting interval mode supported by the terminal, such as equal interval mode or unequal interval mode; here, the interval refers to the time unit between CSI reports;

[0119] Among them, even if the data used by the AI ​​unit in the model training phase is CSI at continuous equal intervals, the AI ​​unit can still have a certain generalization ability and expand to non-equally spaced CSI work, as shown in Figure 8. That is, the CSI data on continuous slots used in the training phase of Figure 8, and the non-equally spaced CSI data used in the inference phase (the CSI on slot 1 is missing). The above-mentioned slot pattern expansion may have a certain performance loss. For example, as the time interval between adjacent slots in the pattern increases, the CSI recovery accuracy will decrease (it can be understood that CSI with longer time intervals can provide less information to each other), but the experimental results show that as long as the interval is not too large (for example, one or two slots), the degree of decline in recovery accuracy is still within an acceptable range.

[0120] It can be seen from item A-4 that a CSI report can be performed once in a time unit within a target time window, where the time units within a target time window may include time units with equal intervals in the time domain (for example, time slots 0, 1, 2, 3), and may also include time units with unequal intervals (for example, time slots 0, 2, 3, 4).

[0121] Optionally, the CSI configuration information includes at least one of the following items C-1 to C-6:

[0122] Item C-1: Number of CSI reports (i.e., the number of CSI reports performed by terminals scheduled by the network-side device);

[0123] Item C-2: The type of the AI ​​unit used; wherein the type of the AI ​​unit includes at least one of the first type, the second type, and the third type described above, which will not be repeated here;

[0124] Item C-3: CSI grouping information, where the CSI grouping information indicates grouping of CSI to be reported according to the length of a target time window, where the target time window includes at least one time unit for performing CSI compression by the AI ​​unit;

[0125] If the number of CSI reports that the terminal needs to perform exceeds the length of the target time window, the CSI reported by the terminal needs to be grouped so that the CSI reports in a group are within a target time window (ie, one group corresponds to one target time window).

[0126] That is, it can be understood as follows: CSI grouping information is used to indicate which CSIs are reported within the same target time window. When the number of CSIs to be scheduled belongs to one of the target time window lengths supported by the terminal capability, or the number of CSIs to be scheduled is less than the maximum target time window length supported by the terminal capability, the network-side device can directly schedule the reporting of this number of CSIs. When the number of CSIs to be scheduled does not belong to the target time window length supported by the terminal capability (or is greater than the length of the maximum target time window supported), the network-side device can further indicate the grouping information of the scheduled CSI.

[0127] For example, if the network device schedules 10 CSI reports, and the terminal capability supports time domain compression of up to 4 CSIs as a group, the network device can indicate that the first 4 CSIs are a group, the next 4 CSIs are another group, and the last 2 CSIs are a group; or the first 2 CSIs are a group, the next 4 are a group, and the last 4 are a group.

[0128] Item C-4: Second indication information, the second indication information is used to indicate whether the position of the reported CSI in the group to which it belongs needs to be carried when the CSI is reported; wherein, one group corresponds to one target time window, and therefore the second indication information can also be understood as: used to indicate whether the position of the reported CSI in the corresponding target time window needs to be carried when the CSI is reported.

[0129] Item C-5: The payload length of at least part of the CSI report within the target time window, where the payload length is the length of the uplink control information UCI resources occupied by the CSI report; that is, the network-side device may indicate to the terminal the payload length of part of the CSI report within a target time window, or may indicate the payload length of all CSI reports within a target time window; wherein, when the network side indicates the payload length of part of the CSI report within a target time window, the payload length of the remaining CSI reports within the target time window may be calculated according to the arrangement rules agreed in advance by the protocol.

[0130] Item C-6: Target resources for measuring CSI within the target time window.

[0131] It should be noted that the terminal measures CSI in a time unit within the target time window and reports the CSI. Accordingly, the network side device may also indicate to the terminal the target resource for measuring CSI within the target time window.

[0132] Optionally, the CSI grouping information in the above item C-3 includes at least one item from the following D-1 to D-3:

[0133] Item D-1: identification information of the group to which each CSI to be reported belongs;

[0134] For example, the network device schedules 10 CSI reports, and the terminal capability supports time domain compression of up to 4 CSIs as a group. If the network device indicates that the 1st to 4th CSIs are a group, the 5th to 8th CSIs are a group, and the 9th to 10th CSIs are a group, then specifically, the network device can indicate the identification information of the group to which each CSI belongs, for example, the first CSI belongs to the first group, the second CSI belongs to the first group, the third CSI belongs to the first group, the fourth CSI belongs to the first group, and so on.

[0135] Item D-2: The location of the first CSI report in each group within the required CSI reports, and the number of CSI reports included in each group; this allows the terminal to determine the group to which each CSI belongs;

[0136] For example, if the network-side device schedules 10 CSI reports, and the terminal capability supports time-domain compression of up to 4 CSIs as a group, if the network-side device indicates that the 1st to 4th CSIs are a group, the 5th to 8th CSIs are a group, and the 9th to 10th CSIs are a group, then specifically, the network-side device can indicate the position of the first CSI in each group among the 10 CSIs, and the number of CSIs included in each group, that is, the first CSI in the first group is the first of the 10 CSIs, the first CSI in the second group is the fifth of the 10 CSIs, and the first CSI in the third group is the ninth of the 10 CSIs. The first group includes 4 CSIs, the second group includes 4 CSIs, and the third group includes 2 CSIs.

[0137] It should be noted that if a group includes a CSI (ie, the length of a target time window is 1), the CSI (ie, the CSI within the target time window) is not compressed in the time domain (eg, only compressed in the space-frequency domain).

[0138] Item D-3: target CSI that needs to be jointly compressed in the time domain, and identification information of the group to which the target CSI belongs.

[0139] As can be seen from item D-3, the network-side device can only indicate the CSI and grouping that require time-domain joint compression, and the remaining CSI will not be subjected to time-domain joint compression by default. When no grouping indication is given, time-domain joint compression is not performed on all by default.

[0140] Optionally, in the above item C-5, the payload length of at least part of the CSI reported within the target time window satisfies at least one of the following items E-1 to E-5:

[0141] Item E-1: The payload length of each CSI report within the target time window is the same (i.e., the payload length of each CSI report within the same target time window is the same). In this case, the network device only needs to indicate one payload length for each target time window. That is, if the network device indicates a payload length of X for each target time window, then the payload length of each CSI report within the target time window is X.

[0142] Item E-2: The payload length of the first CSI report within the target time window is greater than the payload length of the CSI report subsequent to the first CSI report within the target time window (i.e., the payload length of the first CSI report within the same target time window is greater than the payload length of the CSI report subsequent to the first CSI report);

[0143] Item E-3: The payload lengths of the CSI reports after the first CSI report within the target time window are arranged in the form of an arithmetic progression (i.e., the payload lengths of the CSI reports after the first report within the same target time window are arranged in the form of an arithmetic progression); in this case, for the CSI reports after the first CSI report within a target time window, the network-side device may indicate a payload length and a payload difference between adjacent CSI reports, wherein the payload length is the payload length of the second CSI report within the target time window, and thus the payload lengths of the third and subsequent CSI reports within the target time window may be determined based on the payload difference between the adjacent CSI reports;

[0144] In item E-3, for example, there may be a certain correlation between the payload lengths of the CSI reports within a target time window. For example, the feedback payload length of the first CSI in the target time window is higher, followed by the second, until the last CSI payload in the window is the lowest value reported in the window.

[0145] Item E-4: The payload lengths of the CSI reports after the first CSI report within the target time window are the same (i.e., the payload lengths of the CSI reports after the first CSI report within the same target time window are the same); in this case, the network-side device only needs to indicate one payload length for the CSI reports after the first CSI report.

[0146] Item E-5: When the payload lengths of each CSI report within the target time window are different, the payload lengths of each CSI report within the target time window implicitly indicate the position of each CSI report within the target time window within the target time window; for example, a target time window length is 4 time slots, and the payloads of the CSI reports in each time slot are 100, 80, 64, and 50, then the CSI report with a payload of 100 is located in the first time slot within the time window, the CSI report with a payload of 80 is located in the second time slot within the time window, the CSI report with a payload of 64 is located in the third time slot within the time window, and the CSI report with a payload of 50 is located in the fourth time slot within the time window. In this case, the network-side device can configure the terminal to report CSI without carrying the position of the reported CSI in the group to which it belongs (i.e., the position of the reported CSI within the corresponding target time window).

[0147] Optionally, the target resources in the above item C-6 meet at least one of the following items F-1 to F-4:

[0148] Item F-1: the resources used for each CSI measurement within the target time window are the same (i.e., the resources used for CSI measurement in different time units within the same target time window are the same);

[0149] Item F-2: The number of resources used for CSI measurement in the first time unit in the target time window is the largest (i.e., the number of resources used for CSI measurement in the first time unit in the same target time window is greater than the number of resources used for CSI measurement in time units subsequent to the first time unit);

[0150] Item F-3: The number of resources used for CSI measurement in each time unit after the first time unit in the target time window is the same (i.e., the number of resources used for CSI measurement in each time unit after the first time unit in the same target time window is the same);

[0151] Item F-4: The number of resources used for measuring CSI in each time unit after the first time unit in the target time window decreases sequentially in the order of the time domain (i.e., the number of resources used for measuring CSI in the i-th time unit in the same target time window is greater than the number of resources used for measuring CSI in the i+1-th time unit, where i is an integer greater than 2).

[0152] It can be seen from the above items F-1 to F-4 that the resources for measuring CSI in different time units within the same target time window can be the same or different.

[0153] As shown in FIG9 , an embodiment of the present application further provides an AI-based CSI compression method, which may include the following steps 901 to 903:

[0154] Step 901: The network-side device receives capability information of the terminal regarding the artificial intelligence (AI) unit sent by the terminal.

[0155] The AI ​​unit is used to compress the channel state information CSI.

[0156] It should be noted that the AI ​​unit can be used to perform CSI compression in at least one of the time domain, frequency domain, and spatial domain on the CSI, that is, the AI ​​unit can perform at least one of CSI time domain compression, CSI frequency domain compression, and CSI spatial domain compression.

[0157] In addition, the capability information is used to indicate the terminal's support for the AI ​​unit, that is, the terminal's capability in performing CSI compression.

[0158] Step 902: The network-side device determines, based on the capability information, CSI configuration information for instructing the terminal to compress the CSI.

[0159] The CSI configuration information includes configuration information for performing CSI compression, that is, the CSI configuration information is used to instruct the terminal how to perform CSI compression.

[0160] In addition, the CSI configuration information may also be referred to as CSI association information, CSI triggering information, or CSI scheduling information, and is used to indicate how to perform CSI compression.

[0161] As can be seen from step 902, after receiving the capability information reported by the terminal, the network side device determines CSI configuration information according to the capability information to instruct the terminal how to perform CSI compression.

[0162] Step 903: The network-side device sends the CSI configuration information to the terminal.

[0163] After receiving the CSI configuration information, the terminal compresses the CSI through the AI ​​unit according to the CSI configuration information.

[0164] In addition, the AI ​​unit can be used to perform CSI compression in at least one of the time, frequency, and spatial domains. Specifically, the AI ​​unit can perform at least one of the following: time-domain compression, frequency-domain compression, and spatial-domain compression. The specific time-domain, frequency-domain, and spatial-domain CSI compression performed by the terminal depends on the AI ​​unit's functionality and CSI configuration information.

[0165] As can be seen from the above steps 901 to 903, in the embodiment of the present application, the network-side device is able to receive the terminal's capability information about the AI ​​unit sent by the terminal, thereby determining the CSI configuration information based on the capability information, and then sending the CSI configuration information to the terminal, so that the terminal can compress the CSI through the AI ​​unit based on the received CSI configuration information. It can be seen that in the embodiment of the present application, the terminal can exchange its capability information about the AI ​​unit used to compress the CSI with the network-side device, so that the network-side device can configure how the terminal performs CSI compression based on the terminal's capability information about the AI ​​unit. Therefore, the embodiment of the present application provides a method for information exchange between the terminal and the network-side device when compressing CSI based on the AI ​​unit.

[0166] Optionally, the capability information includes at least one of the following items A-1 to A-4:

[0167] Item A-1: ​​Whether the terminal supports AI-based CSI compression;

[0168] Item A-2: the type of the AI ​​unit supported by the terminal;

[0169] Item A-3: First indication information, where the first indication information is used to indicate the length of a target time window supported by the terminal, where the target time window includes at least one time unit (e.g., a time slot) for performing CSI compression by the AI ​​unit.

[0170] Item A-4: The CSI reporting interval mode supported by the terminal.

[0171] It is understood that the relevant explanations of items A-1 to A-4 here can be found in the above text and will not be repeated here.

[0172] Optionally, the first indication information includes at least one of the following items B-1 to B-3:

[0173] Item B-1: the maximum length of the target time window supported by the terminal;

[0174] Item B-2: a set of lengths of the target time window supported by the terminal;

[0175] Item B-3: Identification information of the length configuration of the target time window supported by the terminal.

[0176] It is understood that the relevant explanations of items B-1 to B-3 here can be found in the previous text and will not be repeated here.

[0177] Optionally, the CSI configuration information includes at least one of the following items C-1 to C-6:

[0178] Item C-1: CSI reporting quantity;

[0179] Item C-2: Type of AI unit used;

[0180] Item C-3: CSI grouping information, where the CSI grouping information indicates grouping of CSI to be reported according to the length of a target time window, where the target time window includes at least one time unit for performing CSI compression by the AI ​​unit;

[0181] Item C-4: second indication information, which is used to indicate whether the position of the reported CSI in the group to which it belongs needs to be carried when the CSI is reported;

[0182] Item C-5: payload length of at least part of the CSI report within the target time window, where the payload length is the length of uplink control information (UCI) resources occupied by the CSI report;

[0183] Item C-6: Target resources for measuring CSI within the target time window.

[0184] It is understood that the relevant explanations of items C-1 to C-6 here can be found in the previous text and will not be repeated here.

[0185] Optionally, the CSI grouping information in the above item C-3 includes at least one item from the following D-1 to D-3:

[0186] Item D-1: identification information of the group to which each CSI to be reported belongs;

[0187] Item D-2: The location of the first CSI report in each group within the CSI that needs to be reported, and the number of CSI reports included in each group;

[0188] Item D-3: target CSI that needs to be jointly compressed in the time domain, and identification information of the group to which the target CSI belongs.

[0189] It is understood that the relevant explanations of items D-1 to D-3 here can be found in the previous text and will not be repeated here.

[0190] Optionally, in the above item C-5, the payload length of at least part of the CSI reported within the target time window satisfies at least one of the following items E-1 to E-5:

[0191] Item E-1: The payload length of each CSI report within the target time window is the same;

[0192] Item E-2: The length of the payload of the first CSI report within the target time window is greater than the length of the payload of the CSI report after the first CSI report within the target time window;

[0193] Item E-3: payload lengths of CSI reports after the first CSI report within the target time window, arranged in an arithmetic progression;

[0194] Item E-4: The payload lengths of CSI reports after the first CSI report within the target time window are the same;

[0195] Item E-5: When the payload lengths of each CSI report within the target time window are different, the payload lengths of each CSI report within the target time window implicitly indicate the position of each CSI report within the target time window within the target time window.

[0196] It is understood that the relevant explanations of items E-1 to E-5 here can be found in the above text and will not be repeated here.

[0197] Optionally, the target resources in the above item C-6 meet at least one of the following items F-1 to F-4:

[0198] Item F-1: The resources for each CSI measurement within the target time window are the same;

[0199] F-2: The resources used for measuring CSI in the first time unit of the target time window are the most;

[0200] Item F-3: The number of resources used for measuring CSI in each time unit after the first time unit in the target time window is the same;

[0201] Item F-4: The number of resources used for measuring CSI in each time unit after the first time unit in the target time window decreases in sequence according to the time domain.

[0202] It will be understood that the relevant explanations of items F-1 to F-4 here can be found in the previous text and will not be repeated here.

[0203] In summary, the current CSI compression is mainly limited to CSI compression in the space-frequency domain. In fact, there is also an obvious correlation (referred to as time domain correlation) between different (but temporally close) slots. Increasing the use of time domain CSI correlation on the basis of the space-frequency domain can significantly improve the performance of CSI compression. In an embodiment of the present application, progressive time-frequency space-domain CSI compression can be used: under this compression framework, CSI reporting still occurs once per slot, but the CSI report on a certain slot will refer to the CSI content that has been reported in the previous slot, thereby improving the reporting accuracy. In addition, in an embodiment of the present application, additional information (compared to traditional space-frequency domain CSI compression) that needs to be interacted or determined between the network side device and the terminal when performing CSI compression in the above manner is also provided.

[0204] The AI-based CSI compression method provided in the embodiments of the present application may be executed by an AI-based CSI compression device. In the embodiments of the present application, an AI-based CSI compression device executing the AI-based CSI compression method is used as an example to illustrate the AI-based CSI compression method and device provided in the embodiments of the present application.

[0205] The embodiment of the present application further provides an AI-based CSI compression device, which is applied to a terminal. As shown in FIG10 , the AI-based CSI compression device 100 includes the following modules:

[0206] A first sending module 1001 is configured to send capability information of an artificial intelligence (AI) unit of the terminal to a network device, where the AI ​​unit is configured to compress channel state information (CSI);

[0207] A first receiving module 1002 is configured to receive CSI configuration information sent by the network side device according to the capability information;

[0208] The CSI compression module 1002 is configured to compress the CSI through the AI ​​unit according to the CSI configuration information.

[0209] Optionally, the capability information includes at least one of the following:

[0210] Whether the terminal supports AI-based CSI compression;

[0211] The type of the AI ​​unit supported by the terminal;

[0212] First indication information, where the first indication information is used to indicate a length of a target time window supported by the terminal, where the target time window includes at least one time unit for performing CSI compression by the AI ​​unit;

[0213] The CSI reporting interval mode supported by the terminal.

[0214] Optionally, the first indication information includes at least one of the following:

[0215] The maximum length of the target time window supported by the terminal;

[0216] a set of lengths of the target time window supported by the terminal;

[0217] Identification information of the target time window length configuration supported by the terminal.

[0218] Optionally, the CSI configuration information includes at least one of the following:

[0219] CSI reporting quantity;

[0220] the type of AI unit used;

[0221] CSI grouping information, where the CSI grouping information is used to indicate grouping of CSI to be reported according to a length of a target time window, where the target time window includes at least one time unit in which the AI ​​unit performs CSI compression;

[0222] Second indication information, where the second indication information is used to indicate whether the position of the reported CSI in the group to which it belongs needs to be carried when reporting the CSI;

[0223] The payload length of at least part of the CSI report within the target time window, where the payload length is the length of uplink control information (UCI) resources occupied by the CSI report;

[0224] The target resource for measuring CSI within the target time window.

[0225] Optionally, the CSI grouping information includes at least one of the following:

[0226] Identification information of the group to which each CSI to be reported belongs;

[0227] The location of the first CSI report in each group within the CSI that needs to be reported, and the number of CSI reports included in each group;

[0228] Target CSI that needs to be jointly compressed in the time domain, and identification information of the group to which the target CSI belongs.

[0229] Optionally, a payload length of at least part of the CSI reported within the target time window satisfies at least one of the following:

[0230] The payload length of each CSI report within the target time window is the same;

[0231] The length of the payload of the first CSI report within the target time window is greater than the length of the payload of the CSI report after the first CSI report within the target time window;

[0232] The payload lengths of the CSI reports after the first CSI report within the target time window are arranged in an arithmetic progression;

[0233] The payload lengths of CSI reports after the first CSI report within the target time window are the same;

[0234] In the case where the payload lengths of the CSI reports within the target time window are different, the payload lengths of the CSI reports within the target time window implicitly indicate the positions of the CSI reports within the target time window.

[0235] Optionally, the target resource meets at least one of the following conditions:

[0236] The resources for each CSI measurement within the target time window are the same;

[0237] The resources used for measuring CSI in the first time unit in the target time window are the largest;

[0238] The number of resources used for measuring CSI in each time unit after the first time unit in the target time window is the same;

[0239] The number of resources used for measuring CSI in each time unit after the first time unit in the target time window decreases sequentially according to the order of time domain.

[0240] The AI-based CSI compression 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 within an electronic device, such as an integrated circuit or chip. The electronic device can be a terminal; illustratively, the terminal can include, but is not limited to, the types of terminal 11 listed above, and is not specifically limited in the embodiments of the present application.

[0241] The AI-based CSI compression device provided in the embodiment of the present application can implement the various processes implemented in the method embodiment of Figure 4 and achieve the same technical effects. To avoid repetition, it will not be described here.

[0242] The embodiments of the present application further provide an AI-based CSI compression device, which is applied to a network-side device. As shown in FIG11 , the AI-based CSI compression device 110 includes the following modules:

[0243] A second receiving module 1101 is configured to receive capability information of an artificial intelligence (AI) unit of the terminal sent by the terminal, where the AI ​​unit is configured to compress channel state information (CSI);

[0244] An information determining module 1102 is configured to determine, based on the capability information, CSI configuration information for instructing the terminal to compress the CSI;

[0245] The second sending module 1103 is configured to send the CSI configuration information to the terminal.

[0246] Optionally, the capability information includes at least one of the following:

[0247] Whether the terminal supports AI-based CSI compression;

[0248] The type of the AI ​​unit supported by the terminal;

[0249] First indication information, where the first indication information is used to indicate a length of a target time window supported by the terminal, where the target time window includes at least one time unit for performing CSI compression by the AI ​​unit;

[0250] The CSI reporting interval mode supported by the terminal.

[0251] Optionally, the first indication information includes at least one of the following:

[0252] The maximum length of the target time window supported by the terminal;

[0253] a set of lengths of the target time window supported by the terminal;

[0254] Identification information of the target time window length configuration supported by the terminal.

[0255] Optionally, the CSI configuration information includes at least one of the following:

[0256] CSI reporting quantity;

[0257] the type of AI unit used;

[0258] CSI grouping information, where the CSI grouping information is used to indicate grouping of CSI to be reported according to a length of a target time window, where the target time window includes at least one time unit in which the AI ​​unit performs CSI compression;

[0259] Second indication information, where the second indication information is used to indicate whether the position of the reported CSI in the group to which it belongs needs to be carried when reporting the CSI;

[0260] The payload length of at least part of the CSI report within the target time window, where the payload length is the length of uplink control information (UCI) resources occupied by the CSI report;

[0261] The target resource for measuring CSI within the target time window.

[0262] Optionally, the CSI grouping information includes at least one of the following:

[0263] Identification information of the group to which each CSI to be reported belongs;

[0264] The location of the first CSI report in each group within the CSI that needs to be reported, and the number of CSI reports included in each group;

[0265] Target CSI that needs to be jointly compressed in the time domain, and identification information of the group to which the target CSI belongs.

[0266] Optionally, a payload length of at least part of the CSI reported within the target time window satisfies at least one of the following:

[0267] The payload length of each CSI report within the target time window is the same;

[0268] The length of the payload of the first CSI report within the target time window is greater than the length of the payload of the CSI report after the first CSI report within the target time window;

[0269] The payload lengths of the CSI reports after the first CSI report within the target time window are arranged in an arithmetic progression;

[0270] The payload lengths of CSI reports after the first CSI report within the target time window are the same;

[0271] In the case where the payload lengths of the CSI reports within the target time window are different, the payload lengths of the CSI reports within the target time window implicitly indicate the positions of the CSI reports within the target time window.

[0272] Optionally, the target resource meets at least one of the following conditions:

[0273] The resources for each CSI measurement within the target time window are the same;

[0274] The resources used for measuring CSI in the first time unit in the target time window are the largest;

[0275] The number of resources used for measuring CSI in each time unit after the first time unit in the target time window is the same;

[0276] The number of resources used for measuring CSI in each time unit after the first time unit in the target time window decreases sequentially according to the order of time domain.

[0277] The AI-based CSI compression 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 within an electronic device, such as an integrated circuit or chip. The electronic device can be a network-side device; exemplary network-side devices may include, but are not limited to, the types of network-side devices 12 listed above, and are not specifically limited in the embodiments of the present application.

[0278] The AI-based CSI compression device provided in the embodiment of the present application can implement the various processes implemented in the method embodiment of Figure 9 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0279] As shown in Figure 12, an embodiment of the present application further provides a communication device 1200, including a processor 1201 and a memory 1202. The memory 1202 stores a program or instruction that can be run on the processor 1201. For example, when the communication device 1200 is a terminal, the program or instruction, when executed by the processor 1201, implements the various steps of the above-mentioned embodiment of the AI-based CSI compression method applied to the terminal, and can achieve the same technical effect. When the communication device 1200 is a network-side device, the program or instruction, when executed by the processor 1201, implements the various steps of the above-mentioned embodiment of the AI-based CSI compression method applied to the network-side device, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0280] The present application also provides a terminal 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 shown in FIG4 . This terminal embodiment corresponds to the aforementioned terminal-side method embodiment, and each implementation process and implementation method of the aforementioned method embodiment is applicable to this terminal embodiment and can achieve the same technical effects. Specifically, FIG13 is a schematic diagram of the hardware structure of a terminal implementing an embodiment of the present application.

[0281] The terminal 1300 includes but is not limited to: a radio frequency unit 1301, a network module 1302, an audio output unit 1303, an input unit 1304, a sensor 1305, a display unit 1306, a user input unit 1307, an interface unit 1308, a memory 1309 and at least some of the components of the processor 1310.

[0282] Those skilled in the art will appreciate that the terminal 1300 may also include a power supply (such as a battery) to power various components. The power supply may be logically connected to the processor 1310 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The terminal structure shown in FIG13 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.

[0283] It should be understood that in an embodiment of the present application, the input unit 1304 may include a graphics processing unit (GPU) 13041 and a microphone 13042, and the graphics processor 13041 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 1306 may include a display panel 13061, and the display panel 13061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 1307 includes a touch panel 13071 and at least one of the other input devices 13072. The touch panel 13071 is also called a touch screen. The touch panel 13071 may include two parts: a touch detection device and a touch controller. Other input devices 13072 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.

[0284] In the embodiment of the present application, after receiving downlink data from a network-side device, the RF unit 1301 may transmit the data to the processor 1310 for processing. Furthermore, the RF unit 1301 may send uplink data to the network-side device. Typically, the RF unit 1301 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and the like.

[0285] The memory 1309 can be used to store software programs or instructions and various data. The memory 1309 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 1309 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. Volatile memory can 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 1309 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0286] Processor 1310 may include one or more processing units. Optionally, processor 1310 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 1310.

[0287] The radio frequency unit 1301 is used for:

[0288] Sending capability information of the terminal about an artificial intelligence (AI) unit to a network device, where the AI ​​unit is used to compress channel state information (CSI);

[0289] receiving CSI configuration information sent by the network-side device according to the capability information;

[0290] The processor 1310 is configured to: compress the CSI through the AI ​​unit according to the CSI configuration information.

[0291] Optionally, the capability information includes at least one of the following:

[0292] Whether the terminal supports AI-based CSI compression;

[0293] The type of the AI ​​unit supported by the terminal;

[0294] First indication information, where the first indication information is used to indicate a length of a target time window supported by the terminal, where the target time window includes at least one time unit for performing CSI compression by the AI ​​unit;

[0295] The CSI reporting interval mode supported by the terminal.

[0296] Optionally, the first indication information includes at least one of the following:

[0297] The maximum length of the target time window supported by the terminal;

[0298] A set of lengths of the target time window supported by the terminal;

[0299] Identification information of the target time window length configuration supported by the terminal.

[0300] Optionally, the CSI configuration information includes at least one of the following:

[0301] CSI reporting quantity;

[0302] the type of AI unit used;

[0303] CSI grouping information, where the CSI grouping information is used to indicate grouping of CSI to be reported according to a length of a target time window, where the target time window includes at least one time unit in which the AI ​​unit performs CSI compression;

[0304] Second indication information, where the second indication information is used to indicate whether the position of the reported CSI in the group to which it belongs needs to be carried when reporting the CSI;

[0305] The payload length of at least part of the CSI report within the target time window, where the payload length is the length of uplink control information (UCI) resources occupied by the CSI report;

[0306] The target resource for measuring CSI within the target time window.

[0307] Optionally, the CSI grouping information includes at least one of the following:

[0308] Identification information of the group to which each CSI to be reported belongs;

[0309] The location of the first CSI report in each group within the CSI that needs to be reported, and the number of CSI reports included in each group;

[0310] Target CSI that needs to be jointly compressed in the time domain, and identification information of the group to which the target CSI belongs.

[0311] Optionally, a payload length of at least part of the CSI reported within the target time window satisfies at least one of the following:

[0312] The payload length of each CSI report within the target time window is the same;

[0313] The length of the payload of the first CSI report within the target time window is greater than the length of the payload of the CSI report after the first CSI report within the target time window;

[0314] The payload lengths of the CSI reports after the first CSI report within the target time window are arranged in an arithmetic progression;

[0315] The payload lengths of CSI reports after the first CSI report within the target time window are the same;

[0316] In the case where the payload lengths of the CSI reports within the target time window are different, the payload lengths of the CSI reports within the target time window implicitly indicate the positions of the CSI reports within the target time window.

[0317] Optionally, the target resource meets at least one of the following conditions:

[0318] The resources for each CSI measurement within the target time window are the same;

[0319] The resources used for measuring CSI in the first time unit in the target time window are the largest;

[0320] The number of resources used for measuring CSI in each time unit after the first time unit in the target time window is the same;

[0321] The number of resources used for measuring CSI in each time unit after the first time unit in the target time window decreases sequentially according to the order of time domain.

[0322] 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.

[0323] 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 shown in FIG9 . 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.

[0324] Specifically, embodiments of the present application also provide a network-side device. As shown in Figure 14, network-side device 1400 includes an antenna 141, a radio frequency device 142, a baseband device 143, a processor 144, and a memory 145. Antenna 141 is connected to radio frequency device 142. In the uplink direction, radio frequency device 142 receives information via antenna 141 and sends the received information to baseband device 143 for processing. In the downlink direction, baseband device 143 processes the information to be transmitted and sends it to radio frequency device 142. Radio frequency device 142 processes the received information and then sends it through antenna 141.

[0325] The method executed by the network-side device in the above embodiment may be implemented in the baseband device 143 , which includes a baseband processor.

[0326] The baseband device 143 may include, for example, at least one baseband board, on which multiple chips are arranged, as shown in Figure 14, one of the chips is, for example, a baseband processor, which is connected to the memory 145 through a bus interface to call the program in the memory 145 to execute the network device operations shown in the above method embodiment.

[0327] The network side device may further include a network interface 146 , which is, for example, a Common Public Radio Interface (CPRI).

[0328] Specifically, the network side device 1400 of an embodiment of the present invention also includes: instructions or programs stored in the memory 145 and executable on the processor 144. The processor 144 calls the instructions or programs in the memory 145 to execute the methods executed by the modules shown in FIG11 and achieve the same technical effect. To avoid repetition, they will not be elaborated here.

[0329] An embodiment of the present application also 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 AI-based CSI compression method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0330] 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.

[0331] 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 used to run programs or instructions to implement the various processes of the above-mentioned AI-based CSI compression method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0332] 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.

[0333] 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 AI-based CSI compression method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0334] An embodiment of the present application also provides an AI-based CSI compression system, including: a terminal and a network-side device, wherein the terminal can be used to execute the steps of the AI-based CSI compression method applied to the terminal as above, and the network-side device can be used to execute the steps of the method applied to the network-side device as above.

[0335] 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.

[0336] 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.

[0337] 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. An AI-based CSI compression method, wherein, The method comprises: The terminal sends capability information of the terminal about an artificial intelligence AI unit to the network side device, where the AI ​​unit is used to compress the channel state information CSI; The terminal receives CSI configuration information sent by the network side device according to the capability information; The terminal compresses the CSI through the AI ​​unit according to the CSI configuration information.

2. The method according to claim 1, wherein, The capability information includes at least one of the following: Whether the terminal supports AI-based CSI compression; The type of the AI ​​unit supported by the terminal; first indication information, where the first indication information is used to indicate a length of a target time window supported by the terminal, where the target time window includes at least one time unit for the AI ​​unit to perform CSI compression; The CSI reporting interval mode supported by the terminal.

3. The method according to claim 2, wherein, The first indication information includes at least one of the following: The maximum length of the target time window supported by the terminal; a set of lengths of the target time window supported by the terminal; Identification information of the length configuration of the target time window supported by the terminal.

4. The method according to any one of claims 1 to 3, wherein The CSI configuration information includes at least one of the following: Number of CSI reports; the type of said AI unit used; CSI grouping information, where the CSI grouping information is used to indicate grouping of CSI to be reported according to the length of a target time window, where the target time window includes at least one time unit for CSI compression by the AI ​​unit; second indication information, where the second indication information is used to indicate whether, when reporting the CSI, it is necessary to carry a position of the reported CSI in the group to which it belongs; The payload length of at least part of the CSI report within the target time window, where the payload length is the uplink control information UCI resource length occupied by the CSI report; The target resource for measuring CSI in the target time window.

5. The method according to claim 4, wherein The CSI grouping information includes at least one of the following: Identification information of the group to which each CSI to be reported belongs; The location information of the first CSI report in each group in the CSI reports that need to be reported, and the number of CSI reports included in each group; The target CSI that needs to be jointly compressed in the time domain, and the identification information of the group to which the target CSI belongs.

6. The method according to claim 4 or 5, wherein The payload length of at least part of the CSI reported within the target time window satisfies at least one of the following: The payload length of each CSI report within the target time window is the same; The length of the payload of the first CSI report within the target time window is greater than the length of the payload of the CSI report after the first CSI report within the target time window; The payload lengths of the CSI reports after the first CSI report within the target time window are arranged in an arithmetic progression; The payload lengths of CSI reports after the first CSI report within the target time window are the same; In the case where the payload lengths of each CSI report within the target time window are different, the payload lengths of each CSI report within the target time window implicitly indicate the position of each CSI report within the target time window.

7. The method according to any one of claims 4 to 6, wherein The target resource meets at least one of the following: The resources for measuring CSI each time within the target time window are the same; The resources for measuring CSI in the first time unit within the target time window are the most; The number of resources for measuring CSI in each time unit after the first time unit within the target time window is the same; The quantity of resources for measuring CSI in each time unit after the first time unit within the target time window decreases sequentially in the time domain order.

8. An AI-based CSI compression method, wherein, The method includes: The network device receives the capability information of the terminal sent by the terminal about the artificial intelligence (AI) unit, where the AI unit is used to compress channel state information (CSI); The network device determines CSI configuration information for instructing the terminal to compress CSI according to the capability information; The network device sends the CSI configuration information to the terminal.

9. The method according to claim 8, wherein, The capability information includes at least one of the following: Whether the terminal supports AI-based CSI compression; The type of the AI unit supported by the terminal; First indication information, which is used to indicate the length of the target time window supported by the terminal, and the target time window includes at least one time unit for the AI unit to compress CSI; The interval mode of CSI reporting supported by the terminal.

10. The method according to claim 9, wherein, The first indication information includes at least one of the following: The maximum length of the target time window supported by the terminal; The set of lengths of the target time window supported by the terminal; The identification information of the length configuration of the target time window supported by the terminal.

11. The method according to any one of claims 8 to 10, wherein, The CSI configuration information includes at least one of the following: The CSI reporting quantity; The type of the AI unit used; CSI grouping information, which is used to indicate the grouping of the CSI to be reported according to the length of the target time window, and the target time window includes at least one time unit for the AI unit to compress CSI; Second indication information, which is used to indicate whether the position of the reported CSI in its belonging group needs to be carried when reporting CSI; The load length of at least part of the CSI reporting within the target time window, and the load length is the length of the uplink control information (UCI) resource occupied by CSI reporting; The target resources for measuring CSI within the target time window.

12. The method according to claim 11, wherein, The CSI grouping information includes at least one of the following: The identification information of the group to which each CSI to be reported belongs; The position information of the first CSI reporting in each group in the CSI to be reported, and the number of CSI reports included in each group; The target CSI that needs to perform time domain joint compression, and the identification information of the group to which the target CSI belongs.

13. The method according to claim 11 or 12, wherein, The load length of at least part of the CSI reporting within the target time window meets at least one of the following: The load length of each CSI reporting within the target time window is the same; The length of the load of the first CSI reporting within the target time window is greater than the load length of the CSI reporting after the first CSI reporting within the target time window; The payload lengths of the CSI reports after the first CSI report within the target time window are arranged in an arithmetic progression; The payload lengths of the CSI reports after the first CSI report within the target time window are the same; In the case where the payload lengths of the CSI reports within the target time window are different, the payload lengths of the CSI reports within the target time window implicitly indicate the positions of the CSI reports within the target time window.

14. The method according to any one of claims 11 to 13, wherein The target resource meets at least one of the following: The resources for measuring CSI are the same each time within the target time window; The resources for measuring CSI are the most in the first time unit within the target time window; The number of resources for measuring CSI in each time unit after the first time unit within the target time window is the same; The quantity of resources for measuring CSI in each time unit after the first time unit within the target time window decreases sequentially in the time domain order.

15. An AI-based CSI compression device, wherein, Applied to a terminal, the device includes: A first sending module, configured to send, to a network-side device, the capability information of the terminal about an artificial intelligence (AI) unit, where the AI unit is used to compress channel state information (CSI); A first receiving module, configured to receive the CSI configuration information sent by the network-side device according to the capability information; A CSI compression module, configured to compress CSI through the AI unit according to the CSI configuration information.

16. The apparatus according to claim 15, wherein, The capability information includes at least one of the following: Whether the terminal supports AI-based CSI compression; The type of the AI unit supported by the terminal; First indication information, where the first indication information is used to indicate the length of the target time window supported by the terminal, and the target time window includes at least one time unit for the AI unit to perform CSI compression; The interval mode of CSI reports supported by the terminal.

17. The device according to claim 15 or 16, wherein, The CSI configuration information includes at least one of the following: The CSI report quantity; The type of the AI unit used; CSI grouping information, where the CSI grouping information is used to indicate the grouping of the CSI to be reported according to the length of the target time window, and the target time window includes at least one time unit for the AI unit to perform CSI compression; Second indication information, where the second indication information is used to indicate whether the position of the reported CSI in its belonging group needs to be carried when reporting CSI; The payload lengths of at least some of the CSI reports within the target time window, where the payload length is the uplink control information (UCI) resource length occupied by the CSI report; The target resources for measuring CSI within the target time window.

18. An AI-based CSI compression device, wherein, Applied to a network-side device, the device includes: A second receiving module, configured to receive the capability information of the terminal about an artificial intelligence (AI) unit sent by the terminal, where the AI unit is used to compress channel state information (CSI); An information determination module, configured to determine, according to the capability information, the CSI configuration information for instructing the terminal to compress CSI; A second sending module, configured to send the CSI configuration information to the terminal.

19. The device according to claim 18, wherein, The capability information includes at least one of the following: Whether the terminal supports AI-based CSI compression; The type of the AI ​​unit supported by the terminal; first indication information, where the first indication information is used to indicate a length of a target time window supported by the terminal, where the target time window includes at least one time unit for the AI ​​unit to perform CSI compression; The CSI reporting interval mode supported by the terminal.

20. The device according to claim 18 or 19, wherein The CSI configuration information includes at least one of the following: Number of CSI reports; the type of said AI unit used; CSI grouping information, where the CSI grouping information is used to indicate grouping of CSI to be reported according to the length of a target time window, where the target time window includes at least one time unit for CSI compression by the AI ​​unit; second indication information, where the second indication information is used to indicate whether, when reporting the CSI, it is necessary to carry a position of the reported CSI in the group to which it belongs; The payload length of at least part of the CSI report within the target time window, where the payload length is the uplink control information UCI resource length occupied by the CSI report; The target resource for measuring CSI in the target time window.

21. A terminal, wherein, It includes 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 AI-based CSI compression method as described in any one of claims 1 to 7 are implemented.

22. A network-side device, wherein, It includes 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 AI-based CSI compression method as described in any one of claims 8 to 14 are implemented.

23. A readable storage medium, wherein, The readable storage medium stores a program or instruction, and when the program or instruction is executed by the processor, it implements the AI-based CSI compression method as described in any one of claims 1 to 7, or implements the steps of the AI-based CSI compression method as described in any one of claims 8 to 14.

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