Information transmission method and apparatus, and communication device

Abstract

The present application relates to the technical field of communications, and discloses an information transmission method and apparatus, and a communication device. The information transmission method in embodiments of the present application comprises: a communication device receives or sends first information related to a first object, wherein the first object comprises at least one of the following: a payload, at least one layer, and at least one rank; and the first information is used for determining a first artificial intelligence (AI) model or for configuring AI-based channel state information (CSI) reporting information, and the first AI model is used for generating the CSI reporting information or for decoding the CSI reporting information.

Description

Information transmission method, device and communication equipment

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese Patent Application No. 2024100159577 filed on January 4, 2024, and the contents of the above-mentioned Chinese patent application disclosure are hereby incorporated by reference in their entirety as a part of this application. Technical Field

[0003] The present application belongs to the field of communication technology, and specifically relates to an information transmission method, apparatus and communication equipment. Background Art

[0004] Currently, in mobile communication networks, tasks can be performed or services can be provided based on artificial intelligence (AI). For example, terminals can encode channel state information (CSI) based on pre-trained AI models, and network-side devices can decode CSI based on pre-trained AI models. However, in related technologies, there are no relevant solutions for how to perform AI-based CSI processing or reporting, which can easily lead to AI-based CSI processing or reporting failing to achieve the expected results. Summary of the Invention

[0005] The embodiments of the present application provide an information transmission method, apparatus, and communication equipment, which can provide an AI-based CSI processing method, that is, determining an AI model for generating or decoding CSI reporting information based on first information related to at least one of payload, layer, and rank, or configuring AI-based CSI reporting information, which is conducive to improving the AI-based CSI processing effect.

[0006] In a first aspect, a method for transmitting information is provided, the method comprising:

[0007] The communication device receives or sends first information related to the first object;

[0008] The first object includes at least one of the following: a payload, at least one layer, and at least one rank; the first information is used to determine a first artificial intelligence (AI) model or to configure AI-based channel state information (CSI) reporting information, and the first AI model is used to generate the CSI reporting information or to decode the CSI reporting information.

[0009] In a second aspect, an information transmission device is provided, the device comprising:

[0010] a transceiver module, configured to receive or send first information related to a first object;

[0011] Among them, the first object includes at least one of the following: payload, at least one layer, and at least one rank; the first information is used to determine a first artificial intelligence AI model or to configure AI-based channel state information CSI reporting information, and the first AI model is used to generate the CSI reporting information or to decode the CSI reporting information.

[0012] In a third aspect, a communication 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 first aspect are implemented.

[0013] In a fourth aspect, a communication device is provided, comprising a processor and a communication interface, wherein the communication interface is used to receive or send first information related to a first object; wherein the first object includes at least one of the following: a payload, at least one layer, and at least one rank; the first information is used to determine a first artificial intelligence AI model or to configure AI-based channel state information CSI reporting information, and the first AI model is used to generate the CSI reporting information or to decode the CSI reporting information.

[0014] In a fifth 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.

[0015] In a sixth aspect, a chip is provided, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is configured to run a program or instruction to implement the steps of the method described in the first aspect.

[0016] In a seventh aspect, a computer program / program product is provided, wherein the computer program / program product 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.

[0017] In an embodiment of the present application, a communication device receives or sends first information related to a first object; wherein the first object includes at least one of the following: payload, at least one layer, and at least one rank; the first information is used to determine a first AI model or to configure AI-based CSI reporting information, and the first AI model is used to generate the CSI reporting information or to decode the CSI reporting information. That is, the embodiment of the present application determines the AI ​​model for generating or decoding CSI reporting information or configures AI-based CSI reporting information based on the first information related to at least one of payload, layer, and rank, which is conducive to improving the AI-based CSI processing effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] FIG1 is a block diagram of a wireless communication system to which embodiments of the present application may be applied;

[0019] FIG2a is a schematic diagram of the structure of a neural network provided in an embodiment of the present application;

[0020] FIG2 b is a schematic diagram of the structure of a neuron provided in an embodiment of the present application;

[0021] FIG2c is a schematic diagram of time-frequency-spatial domain CSI encoding and decoding provided by an embodiment of the present application;

[0022] FIG2 d is a schematic diagram of AI-based CSI encoding and decoding provided in an embodiment of the present application;

[0023] FIG3 is a flow chart of an information processing method provided in an embodiment of the present application;

[0024] 4a to 5f are schematic diagrams of information transmission of layers provided in embodiments of the present application;

[0025] FIG6 is a schematic diagram of multiple types of layers provided in an embodiment of the present application;

[0026] FIG7 is a structural diagram of an information processing device provided in an embodiment of the present application;

[0027] FIG8 is a structural diagram of a communication device provided in an embodiment of the present application;

[0028] FIG9 is a structural diagram of a terminal provided in an embodiment of the present application;

[0029] FIG10 is a structural diagram of a network-side device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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, a vehicle-mounted controller, a vehicle-mounted module, a vehicle-mounted component, a vehicle-mounted chip or a vehicle-mounted unit, etc. It should be noted that the specific type of the terminal 11 is not limited in the embodiment of the present application. The network side device 12 may include an access network device or a core network device, wherein the access network device may also be called a radio access network (Radio Access Network, RAN) device, a radio access network function or a radio access network unit. The access network device may include a base station, a wireless local area network (WLAN) access point (AP) or a wireless fidelity (WiFi) node, etc.Among them, the base station can be referred to as Node B (NB), Evolved Node B (eNB), the next generation Node B (gNB), New Radio Node B (NR Node B), access point, Relay Base Station (RBS), Serving Base Station (SBS), Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), Home Node B (HNB), Home Evolved Node B (home evolved Node B), Transmission Reception Point (TRP) or other appropriate terms in the 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.

[0035] The core network equipment may include but is not limited to at least one of the following: core network node, core network function, mobility management entity (MME), access mobility management function (AMF), session management function (SMF), user plane function (UPF), policy control function (PCF), policy and charging rules function unit (PCRF), edge application service discovery function (EASDF), unified data management (UDM), unified data repository (UDR), home user server (HSS), centralized network configuration (CNC), network storage function (NRF), network exposure function (NEF), local NEF (L-NEF), binding support function (BSF), application function ( It should be noted that in the embodiments of the present application, only the core network device in the NR system is introduced as an example, and the specific type of the core network device is not limited.

[0036] For ease of understanding, some of the contents involved in the embodiments of this application are described below:

[0037] 1. Artificial Intelligence (AI)

[0038] Artificial intelligence (AI) is currently being widely applied in various fields. Integrating AI into wireless communication networks to significantly improve technical indicators such as throughput, latency, and user capacity is a key task for future wireless communication networks. AI modules can be implemented in a variety of ways, 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.

[0039] For example, a neural network can be shown in FIG2a, and the neural network is composed of neurons, and each neuron can be shown in FIG2b. K is the input, w is the weight (multiplicative coefficient), b is the bias (additive coefficient), and σ(.) is the activation function. Common activation functions include Sigmoid, Tanh, Rectified Linear Unit (ReLU), etc.

[0040] Neural network parameters are optimized using a gradient optimization algorithm. Gradient optimization algorithms are a class of algorithms that minimize or maximize an objective function (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, we construct a neural network model f(.). With this model, we can obtain the predicted output f(x) based on the input x, and calculate the difference between the predicted value and the true value (f(x) - Y). This is the loss function. The goal is to find the appropriate W,b to minimize the value of this loss function. The smaller the loss value, the closer the model is to the true value.

[0041] Currently, most common optimization algorithms are based on the back propagation (BP) algorithm. The basic idea of ​​the BP algorithm is that the learning process consists of two steps: forward propagation of signals and back propagation of errors. During forward propagation, input samples are passed from the input layer, processed layer by layer through each hidden layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, the error begins back propagation. Back propagation involves propagating the output error back through the hidden layers to the input layer layer by layer in some form, distributing the error to all units in each layer. This error signal is then generated for each unit in each layer, which serves as the basis for adjusting the weights of each unit. This process of adjusting the weights of each layer, through forward propagation of signals and back propagation of errors, is repeated over and over again. This process of continuous weight adjustment is the network's learning and training process. This process continues until the error in the network output is reduced to an acceptable level, or until a pre-set number of learning cycles is reached.

[0042] 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 (Adagrad), Adadelta, root mean square error prop (RMSprop), adaptive momentum estimation (Adam), etc.

[0043] 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.

[0044] 2. AI Unit / AI Model

[0045] The AI ​​unit / AI model of the embodiment of the present application may also be referred to as a machine learning (ML) model, ML unit, AI structure, AI function, AI feature, machine learning model, neural network, neural network function, neural network function, etc., or the above-mentioned AI unit / AI model may also refer to a processing unit that can implement specific algorithms, formulas, processing procedures, capabilities, etc. related to AI, or the AI ​​unit / AI model may be a processing method, algorithm, function, module or unit for a specific data set, or the AI ​​unit / AI model may be a processing method, algorithm, function, module or unit running on AI / ML related hardware such as GPU, NPU, TPU, ASIC, etc., which is not specifically limited in the embodiment of the present application. Optionally, the specific data set includes at least one of the input and output of the AI ​​unit / AI model.

[0046] Optionally, the identifier of the AI ​​unit / AI model may be an AI model identifier, an AI structure identifier, an AI algorithm identifier, or an identifier of a specific data set associated with the AI ​​unit / AI model, or an identifier of a specific scenario, environment, channel feature, or device related to the AI / ML, or an identifier of a function, feature, capability, or module related to the AI / ML. This embodiment of the present application does not specifically limit this.

[0047] 3. Non-AI CSI Compression

[0048] The non-AI CSI compression in the current protocol includes type I, type II, and enhanced type II (e type 2 CSI), where:

[0049] 1. Type I CSI

[0050] Type I reports the wideband or subband Precoding Matrix Indicator (PMI), which is a two-dimensional Discrete Fourier Transform (DFT) vector and its phase rotation, when there is no way to report the complete channel or precoder. Type I primarily reports the index of the two-dimensional DFT vector and its phase rotation.

[0051] The reporting format of the above type I CSI is as follows:

[0052] Wideband CSI: Rank Indicator (RI) - PMI - Channel Quality Indicator (CQI). If RI>4, two transport blocks (TB) need to report two CQIs, otherwise one CQI is reported.

[0053] Sub-band CSI:

[0054] Part 1 CSI: RI + CQI of the first TB;

[0055] Part 2 CSI (Part 2 CSI): wideband CQI - wideband PMI - CQI + PMI of even subbands - CQI + PMI of odd subbands; the omission principle is: omit part 2 based on priority, that is, odd subbands can be omitted first.

[0056] 2. Type 2 CSI

[0057] Type 2 represents the precoding vector PMI as a linearly weighted sum of a set of basis vectors, compared to a simple two-dimensional DFT vector and its phase rotation. Type 2 requires reporting the basis vector index and its projection (amplitude and phase) onto the basis vector.

[0058] The above type 2 CSI reporting format is as follows:

[0059] Part 1: RI-CQI - Number of non-zero wideband amplitude coefficients per layer {separately encoded};

[0060] Part 2: Wideband PMI {L vector}-PMI-Layer Indicator (LI) {i 1,4,l (Broadband Amplitude 1)i 2,1,l (Phase)i 2，2，l (subband amplitude 2)}, the above L vector is a basis vector.

[0061] Among them, amplitude 1: 3 bits (scalar); amplitude 2: 1 bit.

[0062] 3. e type 2 CSI

[0063] Since the overhead of type 2 is as large as hundreds or even thousands of bits, e type 2 is a further compression of type 2, that is, the vector composed of weighting coefficients on different subbands is further compressed into a vector composed of a set of frequency domain basis vectors.

[0064] The above e type 2 CSI reporting format is as follows:

[0065] Part 1: RI-CQI - number of non-zero wideband amplitude coefficients per layer {separately encoded};

[0066] Part 2: Broadband PMI {vector}-PMI:i 2,4,l Amplitude i 2,5,l Phase and i 1,7,l ,{report bitmap};

[0067] Pri(l,i,f)=2·L·υ·π(f)+υ·i+l,

[0068] Where π(f)f is the frequency domain basis vector, π(0) = 0, π(N3-1) = 1, π(1) = 2;

[0069] Where l=1,2,…,υ,i=0,1,…,2L-1,and f=0,1,…,M v -1,

[0070] Part 2 above adopts a feedback method of uniform compression of all sub-bands, where:

[0071] 0: L spatial basis vectors i 1,1 ,i 1,2 And the strongest coefficient information of each layer {log2 2L bit}i 1,8,l(l=1,…,υ);

[0072] 1: M frequency domain basis vectors (i 1,5 (If reported),i 1,6,l (If reported)), reference amplitude information i 2,3,l , v2LM-[KNZ / 2]bit i with the highest priority among the non-zero coefficient positions 1,7,l , v is the rank, v2LM-[KNZ / 2] coefficients with the highest priority {i 2,4,l ,i 2,5,l};

[0073] 2: The coefficient with the lowest priority [KNZ / 2] among the non-zero coefficient positions.

[0074] 4. Time-Frequency-Spatial Domain CSI Compression

[0075] For example, as shown in Figure 2c, in traditional space-frequency domain compression, the CSI information (space-frequency domain channel) of time slot (Slot) X is input into encoder (Encoder, ENC) X to generate CSI reporting information (feedback information (Feedback) in Figure 2c); the network (Network, NW) receives the CSI reporting information and decodes it through decoder (Decoder, DEC) X to obtain the recovered CSI information (i.e., CSI').

[0076] In multi-layer compression (where multiple layers can be associated with a single reportconfig ID or with different reportconfig IDs), different layers can be associated with the same time or different times, for example, corresponding to different slots, that is, compression is performed in different time domains.

[0077] In space-time-frequency domain compression, ENC X uses the intermediate information (internal information) of ENC X-1 or ENC X+1, that is, it uses the previous or subsequent historical information to assist in encoding. The intermediate information shown in Figure 2c is sent by ENC X (i.e., the encoder of Slot X) to ENC X+1 (the encoder of Slot X+1). It should be noted that the intermediate information of the decoder is similar to the intermediate information of the encoder mentioned above and will not be described in detail here. In addition, the intermediate state information of the encoder is generally only transmitted between encoders; the intermediate state information of the decoder is generally only transmitted between decoders.

[0078] In Release 18 (R18), AI-based CSI / PMI compression is proposed, where the UE expects or targets CSI or codebook WN*B Compress through AI, such as compressing into an AI-based PMI value, and then reporting it to the network side device, which performs decompression to obtain W′ N*B , as shown in Figure 2d, where N represents the number of CSI ports and B represents the number of subbands (Sunband).

[0079] The information transmission method provided in the embodiments of the present application is described in detail below through some embodiments and their application scenarios in combination with the accompanying drawings.

[0080] Please refer to FIG3 , which is a flowchart of an information transmission method provided in an embodiment of the present application. The method can be executed by a communication device, as shown in FIG3 , and includes the following steps:

[0081] Step 301: A communication device receives or sends first information related to a first object;

[0082] The first object includes at least one of the following: payload, at least one layer, and at least one rank; the first information is used to determine a first AI model or to configure AI-based CSI reporting information, and the first AI model is used to generate the CSI reporting information or to decode the CSI reporting information.

[0083] In this embodiment, the communication device may be an encoding end or a decoding end, or the communication device may be a terminal or a network side device.

[0084] The above-mentioned AI-based CSI reporting information can be understood as part or all of the information in the CSI reporting information being encoded or compressed by the AI ​​model. The above-mentioned CSI reporting (Report) information can be understood as information obtained by encoding or compressing part or all of the CSI information, such as PMI, CQI, RI, etc., wherein the above-mentioned CSI information may include but is not limited to at least one of the precoding matrix (Precoding Matrix), channel information, etc. It should be noted that the above-mentioned CSI reporting information may also be referred to as CSI feedback information, etc.

[0085] Exemplarily, if the above-mentioned first AI model is a coding or compression model, the above-mentioned CSI reporting information may include information of the precoding matrix passed through the first AI model, or the above-mentioned CSI reporting information may include at least one of information of the precoding matrix passed through the first AI model, CQI information, and Rank information.

[0086] Exemplarily, the CSI reporting information may include CSI reporting information corresponding to at least one layer or CSI reporting information corresponding to at least one rank. In some optional embodiments, the at least one rank may include at least one rank greater than 1.

[0087] Exemplarily, the above-mentioned payload may be a payload of CSI reporting information, or the above-mentioned payload may be a payload of a precoding matrix passing through the first AI model, or the above-mentioned payload may be a payload of part of the CSI reporting information.

[0088] In some optional embodiments, different layers may correspond to AI models corresponding to different payloads, or different types of layers may correspond to AI models corresponding to different payloads, or ranks with different values ​​may correspond to AI models corresponding to different payloads.

[0089] The above-mentioned first information is used to determine the first AI model. Exemplarily, the above-mentioned first information is used to perform AI model training to obtain the first AI model, or the above-mentioned first information is used to update or fine-tune the existing AI model to obtain the first model, or to select the first AI model from multiple AI models, etc.

[0090] In some optional embodiments, determining the first AI model based on the first information may include determining parameters of the first AI model based on the first information. Determining the parameters of the first AI model based on the first information may be understood as adjusting the parameters of the first AI model based on the first information. Optionally, adjusting the parameters of the first AI model may be adjusting the parameters of the first AI model to meet payload requirements.

[0091] For example, the above-mentioned first information may include data set information related to the payload (for example, at least one of the input information and output information of the AI ​​model), and based on the data set information, the AI ​​model used to process the CSI reporting information corresponding to the payload, that is, the first AI model, can be trained; or, the above-mentioned first information may include identification information of the AI ​​model related to the payload, and the AI ​​model identified by the identification information (that is, the first AI model) is used to process the CSI reporting information corresponding to the payload; or, the above-mentioned first information may include data set information corresponding to at least one layer or at least one rank, and the data set information is used to train the AI ​​model used to process the CSI reporting information corresponding to the at least one layer or at least one rank, that is, the first AI model.

[0092] In addition, for the encoding end, the above-mentioned first AI model is used to encode or compress the CSI to obtain CSI reporting information; for the decoding end, the above-mentioned first AI model is used to decode or decompress the CSI reporting information.

[0093] Optionally, using the first AI model to generate the CSI reporting information may include using the first AI model to generate all or part of the CSI reporting information, for example, using the first AI model to generate compressed precoding information. Using the first AI model to decode the CSI reporting information may include using the first AI model to decode all or part of the CSI reporting information, for example, using the first AI model to decode compressed precoding information in the CSI reporting information.

[0094] The above-mentioned first information is used to configure AI-based CSI reporting information. For example, if the above-mentioned first information includes a layer type, the above-mentioned CSI reporting information may include the CSI reporting information corresponding to the layer type; or, if the above-mentioned first information includes a layer compression method, the above-mentioned CSI reporting information may be the CSI reporting information obtained by compression using the layer compression method, or the above-mentioned first information includes a payload, then the above-mentioned CSI reporting information or the specified part of the CSI reporting information should satisfy the payload.

[0095] The following examples illustrate the situation:

[0096] Case 1: When the communication device is a terminal, the terminal receives first information related to the first object sent by the network-side device, the model management device, or the data management device, and determines a first AI model based on the first information. The terminal may then compress or encode part or all of the CSI information based on the first AI model to obtain CSI reporting information. Alternatively, the terminal may generate CSI reporting information based on the first information and send the CSI reporting information to the network-side device.

[0097] Alternatively, the terminal sends first information related to the first object to the network side device, and the network side device determines a first AI model based on the received first information, and then the network side device can decode or decompress the CSI reporting information received from the terminal based on the first AI model to obtain decoded CSI information or decompressed CSI information or recovered CSI information or reconstructed CSI information, or the network side device decodes or decompresses the CSI reporting information based on the received first information to obtain decoded CSI information or decompressed CSI information or recovered CSI information or reconstructed CSI information.

[0098] In scenario 2, when the communication device is a network-side device, the network-side device may send first information related to the first object to the terminal. The terminal may determine a first AI model based on the first information. The terminal may then compress or encode part or all of the CSI based on the first AI model to obtain CSI reporting information. Alternatively, the terminal may generate CSI reporting information based on the first information and send the CSI reporting information to the network-side device.

[0099] Alternatively, the network side device receives first information related to the first object sent by the terminal or the model management device or the data management device, and determines the first AI model based on the first information, and then the network side device can decode or decompress the CSI reporting information received from the terminal based on the first AI model to obtain decoded CSI information or decompressed CSI information or recovered CSI information or reconstructed CSI information, or the network side device decodes or decompresses the CSI reporting information based on the received first information to obtain decoded CSI information or decompressed CSI information or recovered CSI information or reconstructed CSI information.

[0100] In an embodiment of the present application, a communication device receives or sends first information related to a first object; wherein, the first object is an object related to CSI reporting information, and the first object includes at least one of the following: payload, at least one layer, and at least one rank; the first information is used to determine a first AI model, and the first AI model is used to generate the CSI reporting information or to decode the CSI reporting information. The embodiment of the present application determines the AI ​​model for generating or decoding CSI reporting information or configures AI-based CSI reporting information based on the first information related to at least one of payload, layer, and rank, which is conducive to improving the AI-based CSI processing effect.

[0101] Optionally, the first information related to the first object includes at least one of the following:

[0102] The first data set information related to the payload;

[0103] The first AI model information related to the payload;

[0104] The first quantitative information related to the payload.

[0105] Exemplarily, the above-mentioned first dataset information may include but is not limited to at least one item of at least one dataset information related to the payload, payload information associated with at least one dataset information, a dataset identifier associated with at least one dataset information related to the payload, AI model information associated with at least one dataset information related to the payload, etc.

[0106] The above-mentioned first AI model information related to the payload may include but is not limited to at least one of the identification information of the AI ​​model related to the payload, the payload information associated with the AI ​​model, the type information or function information of the AI ​​model related to the payload, the parameter information of the AI ​​model related to the payload, etc.

[0107] The first quantization information related to the payload may include but is not limited to at least one of a quantization method corresponding to the output dimension of the AI ​​model, a quantization method corresponding to the payload size, and the like.

[0108] In some optional embodiments, in order to match the first AI model on the terminal and the network side, the terminal and the network side device transmit data set information for training the first AI model. In this case, the above-mentioned first information may include the data set information of the first AI model.

[0109] In some optional embodiments, in order to match the first AI model on the terminal and the network side, the terminal and the network side device transmit parameter information of the first AI model. In this case, the above-mentioned first information may include the parameter information of the first AI model of the first AI model.

[0110] In some optional embodiments, in order to match the first AI model on the terminal and the network side, the terminal and the network side device transmit conditional information of the first AI model, such as payload information, type information of the first AI model, etc.

[0111] Optionally, a first data set information is associated with CSI reporting information of a payload;

[0112] or,

[0113] Different first data set information is associated with CSI reporting information of different payloads.

[0114] In this embodiment, one first data set is associated with CSI reporting information for one payload, and different first data sets are associated with CSI reporting information for different payloads. This allows for the rapid determination of the AI ​​model corresponding to the payload of the CSI reporting information based on the first data set to process the CSI reporting information. It is understood that the CSI reporting information obtained during the AI ​​inference phase by the first AI model trained based on the first data set for the payload will naturally satisfy the CSI reporting information for that payload.

[0115] Optionally, one first data set information is associated with CSI reporting information of multiple payloads.

[0116] Optionally, a first data set may contain input information or output information of multiple AI models. For example, the dimensions of the CSI information input to the AI ​​model for compression are different, or the dimensions or payload of the CSI reporting information output from the AI ​​model are different.

[0117] In this embodiment, a first data set information can be associated with CSI reporting information of multiple payloads. Exemplarily, the above-mentioned first data set information may include multiple data set information, and the multiple data set information respectively corresponds one-to-one to the CSI reporting information of the multiple payloads. For example, if the first data set information may include data set information A, data set information B and data set information C, then data set information A corresponds to the CSI reporting information of the first payload, data set information B corresponds to the CSI reporting information of the second payload, and data set information C corresponds to the CSI reporting information of the third payload; or, the CSI reporting information of the above-mentioned multiple payloads respectively corresponds to different data in the first data set information. For example, data a of the above-mentioned first data set information is associated with the first payload, data b of the above-mentioned first data set information is associated with the first payload, data c of the above-mentioned first data set information is associated with the second payload, data d of the above-mentioned first data set information is associated with the first payload, data e of the above-mentioned first data set information is associated with the third payload, and so on.

[0118] It is understandable that the first data set information may explicitly include different data subsets, or may include different data in an unordered manner that simply belongs to different data subsets based on data characteristics. Optionally, the communication device needs to preprocess or classify the data subsets when obtaining the first data set information. Optionally, the communication device processes the different data subsets into first data set information having the same characteristics through preprocessing, or the communication device extracts the different data subsets through classification. Optionally, the method may further include: the communication device receiving second information, where the second information is used to assist the communication device in preprocessing or classifying the first data set information.

[0119] In some optional embodiments, there is a correspondence between the dataset information corresponding to the CSI reporting information of different payloads in the first dataset information and different AI models.

[0120] In this embodiment, a first data set information is associated with CSI reporting information of multiple payloads. In this way, based on the first data set information, the AI ​​models corresponding to the CSI reporting information of multiple payloads can be determined to process the CSI reporting information of the multiple payloads respectively.

[0121] Optionally, the first data set information includes at least one of the following:

[0122] At least one data set information, wherein the at least one data set information includes at least one of input information of the AI ​​model and output information of the AI ​​model;

[0123] At least one payload information associated with a dataset;

[0124] At least one dataset identifier associated with the dataset information;

[0125] AI model information associated with at least one dataset.

[0126] For example, taking the above-mentioned first AI model as an example for generating CSI report information, the input information of the above-mentioned AI model may include but is not limited to the precoding matrix W N*B and channel information, where N represents the number of CSI ports and B represents the number of subbands. The output information of the AI ​​model may include, but is not limited to, compressed CSI information (e.g., compressed PMI value, compressed precoding matrix), the size of the output information, the output dimension, or the number of bits of the output information. The output dimension of the AI ​​model may be understood as the number of output elements or output parameters of the AI ​​model.

[0127] The payload information associated with the above-mentioned at least one data set information may include but is not limited to at least one item of the payload size of at least one item of the input information of the AI ​​model and the output information of the AI ​​model, the type of the payload of at least one item of the input information of the AI ​​model and the output information of the AI ​​model, the quantization method of the output information of the AI ​​model, the dimension of at least one item of the input information of the AI ​​model and the output information of the AI ​​model, etc., wherein the dimension of the input information of the above-mentioned AI model can be understood as the number of input elements or input parameters of the AI ​​model, and the dimension of the output information of the above-mentioned AI model can be understood as the number of output elements or output parameters of the AI ​​model.

[0128] The dataset identifier (ID) associated with the at least one dataset information may include, but is not limited to, a dataset ID associated with at least one of the AI ​​model input information and the AI ​​model output information. In some optional embodiments, different dataset IDs may be associated with different payload information.

[0129] The AI ​​model information associated with the at least one dataset information may include but is not limited to at least one of the identification information of the AI ​​model associated with the at least one dataset information, payload information associated with the AI ​​model, type information or function information of the AI ​​model, parameter information of the AI ​​model, etc.

[0130] It is understandable that the at least one data set information may include at least one of the input information of the AI ​​model and the output information of the AI ​​model, indicating that the at least one data set information is used for training, updating, and other processing of the first AI model. For example, if the above-mentioned AI model is an encoding or compression model, the input information of the above-mentioned AI model may include uncompressed precoding information or uncompressed CSI information, and the output information of the above-mentioned AI model may include compressed precoding information or compressed CSI information; if the above-mentioned AI model is a decoding or decompression model, the input information of the above-mentioned AI model may include compressed precoding information or compressed CSI information, and the output information of the above-mentioned AI model may include uncompressed precoding information (i.e., recovered precoding information) or uncompressed CSI information (i.e., recovered CSI information). That is, the at least one data set information may include at least one of uncompressed precoding information and compressed precoding information, or the at least one data set information may include at least one of uncompressed CSI information and compressed CSI information.

[0131] Optionally, a first AI model information is associated with CSI reporting information of a payload;

[0132] or,

[0133] Different first AI model information is associated with CSI reporting information of different payloads.

[0134] In this embodiment, one first AI model information is associated with CSI reporting information of one payload, and different first AI model information is associated with CSI reporting information of different payloads. In this way, based on the first AI model information, the AI ​​model corresponding to the payload of the CSI reporting information can be quickly determined to process the CSI reporting information.

[0135] Optionally, the first AI model information includes:

[0136] Identification information of the AI ​​model;

[0137] Payload information associated with the AI ​​model;

[0138] AI model type or function information;

[0139] Parameter information of the AI ​​model.

[0140] Exemplarily, the identification information of the above-mentioned AI model may include but is not limited to at least one of the AI ​​model ID, function ID, etc.

[0141] The payload information associated with the above-mentioned AI model may include but is not limited to at least one of the size of the payload of at least one item of the input information and output information of the AI ​​model, the quantization method of the output information of the AI ​​model, the type of the payload of at least one item of the input information and output information of the AI ​​model, etc.

[0142] The type information of the AI ​​model can be used to indicate the type of the AI ​​model, and the function information of the AI ​​model can be used to indicate the function of the AI ​​model. In some optional embodiments, the type information or function information of the AI ​​model is used to indirectly indicate one or more associated payload information.

[0143] The parameter information of the above-mentioned AI model may include but is not limited to at least one of the number of model layers, model structure, model parameters, etc. For example, the parameter information of the above-mentioned AI model may include at least part of the model structure and at least part of the model parameters of the adaptation layer. Optionally, the above-mentioned adaptation layer may include part of the layers located at the tail of the AI ​​model (encoder tail) or the head of the AI ​​model (decoder head). For example, the number of layers of the above-mentioned adaptation layer is 1 layer, 2 layers, 4 layers, etc., and the structure of the above-mentioned adaptation layer is a multi-layer perceptron (MLP), a convolutional neural network (CNN), etc.

[0144] Optionally, one first quantization information is associated with CSI reporting information of a payload;

[0145] or,

[0146] A first quantization information is associated with CSI reporting information of multiple payloads.

[0147] In one embodiment, a first quantization information is associated with CSI reporting information of a payload. For example, when the output elements of the AI ​​model are fixed and the quantization method is fixed-length quantization, a first quantization information can be associated with CSI reporting information of a payload.

[0148] In this embodiment, a first quantization information is associated with the CSI reporting information of a payload, so that the AI ​​model corresponding to the CSI reporting information of a payload can be determined based on the first quantization information to process the CSI reporting information of the payload.

[0149] In another embodiment, one first quantization information can be associated with CSI reporting information of multiple payloads. For example, when the output elements of the AI ​​model are fixed and the quantization method is variable-length quantization, or when the output elements of the AI ​​model are not fixed, one first quantization information can be associated with CSI reporting information of multiple payloads.

[0150] In this embodiment, one first quantization information is associated with CSI reporting information of multiple payloads. In this way, based on the one first quantization information, the AI ​​models corresponding to the CSI reporting information of multiple payloads can be determined to process the CSI reporting information of the multiple payloads respectively.

[0151] Optionally, the first quantitative information includes at least one of the following:

[0152] A quantization method corresponding to the AI ​​model output dimension, where the AI ​​model output dimension is used to indicate the number of output elements of the AI ​​model;

[0153] A quantization method corresponding to the payload size, where the payload size is the payload size of the CSI reporting information.

[0154] Exemplarily, the quantization method may include but is not limited to a segmentation method, a quantization bit number, a quantization codebook, and the like.

[0155] The above-mentioned AI model output dimension can be understood as the number of output elements or output parameters of the AI ​​model.

[0156] In one embodiment, the first quantization information includes a quantization method corresponding to the output dimension of the AI ​​model. For example, if the output dimension of the first AI model is X, the first quantization information may include a quantization method corresponding to X.

[0157] Optionally, different AI model output dimensions correspond to different quantization methods, or AI model output dimensions within different output dimension value ranges correspond to different quantization methods.

[0158] For example, if the output dimension of the AI ​​model is X1, the corresponding quantization method is the first quantization method; if the output dimension of the AI ​​model is X2, the corresponding quantization method is the second quantization method, wherein X1 and X2 have different values, and the first quantization method and the second quantization method are different. For example, the first quantization method is 8-bit quantization of a floating point number (float) or a segment of a float, and the second quantization method is 4-bit quantization of a float or a segment of a float; or, the first quantization method is variable-length quantization, and the second quantization method is fixed-length quantization; or, the first quantization method is vector quantization (VQ), and the second quantization method is fixed quantization.

[0159] The above-mentioned output dimension value range can be understood as the value range of the AI ​​model output dimension. In some optional embodiments, multiple output dimension value ranges can be pre-set, and a correspondence between different output dimension value ranges and different quantization methods can be established. For example, the first output dimension value range is [Y1, Y2), and its corresponding quantization method is the first quantization method; the second output dimension value range is [Y2, Y3), and its corresponding quantization method is the second quantization method; the second output dimension value range is [Y3, Y4), and its corresponding quantization method is the third quantization method, and so on; if the AI ​​model output dimension is X1, and X1 is located in [Y1, Y2), then its corresponding quantization method is the first quantization method; if the AI ​​model output dimension is X2, and X2 is located in [Y3, Y4), then its corresponding quantization method is the third quantization method.

[0160] In one embodiment, the first quantization information includes a quantization method corresponding to the payload size. For example, if the payload size of the CSI reporting information is Z bits, the first quantization information may include a quantization method corresponding to Z.

[0161] Optionally, different payload sizes correspond to different quantization methods, or payload sizes within different payload size value ranges correspond to different quantization methods.

[0162] For example, if the payload size is Z1 bits, the corresponding quantization mode is the first quantization mode; if the payload size is Z2 bits, the corresponding quantization mode is the second quantization mode, wherein Z1 and Z2 are different, and the first quantization mode and the second quantization mode are different. For example, the first quantization mode is 8-bit quantization of a float or a segment of a float, and the second quantization mode is 4-bit quantization of a float or a segment of a float; or, the first quantization mode is variable-length quantization, and the second quantization mode is fixed-length quantization; or, the first quantization mode is VQ quantization, and the second quantization mode is fixed quantization.

[0163] The above-mentioned payload size value range can be understood as the value range of the payload size. In some optional embodiments, multiple payload size value ranges can be pre-set, and a correspondence between different payload size value ranges and different quantization methods can be established. For example, the first payload size value range is [K1, K2), and its corresponding quantization method is the first quantization method; the second payload size value range is [K2, K3), and its corresponding quantization method is the second quantization method; the second payload size value range is [K3, K4), and its corresponding quantization method is the third quantization method, and so on; if the payload size of the above-mentioned CSI reporting information is Z1, and Z1 is located in [K1, K2), then its corresponding quantization method is the first quantization method; if the payload size of the above-mentioned CSI reporting information is Z2, and Z2 is located in [K1, K2), then its corresponding quantization method is the second quantization method.

[0164] Optionally, the first information related to the first object includes at least one of the following:

[0165] first information related to a payload in one of the at least one layer;

[0166] First information related to the payload of each layer in the at least one layer;

[0167] First information related to the payload of all layers in the at least one layer.

[0168] In one embodiment, the first information related to the first object may include first information related to the payload of one of the at least one layers, for example, first information related to the payload of a first type of layer in the at least one layer. The first information related to the payload of the layer may include, but is not limited to, at least one of first dataset information related to the payload of the layer, first AI model information related to the payload of the layer, and first quantitative information related to the payload of the layer.

[0169] In another embodiment, the first information related to the first object may include first information related to the payload for each layer in the at least one layer (i.e., per layer). For example, when the CSI reporting information includes CSI reporting information corresponding to at least two layers, the first information related to the first object may include first information related to the payload for each layer in the at least two layers, wherein the first information related to the payload for each layer may include, but is not limited to, at least one of the following: first dataset information related to the payload for each layer, first AI model information related to the payload for each layer, first quantization information related to the payload for each layer, etc. Optionally, the first information related to the payload for each layer may be the same.

[0170] It can be understood that in this embodiment, different layers in the at least one layer mentioned above can be associated with different payloads, or different layers can be associated with different AI models.

[0171] In another embodiment, the first information related to the first object may include the first information related to the payload of all layers (across layer) of the at least one layer. For example, all layers related to the CSI reporting information are taken as a whole to obtain their first information related to the payload, that is, the first information related to the payload is for all layers related to the CSI reporting information. The first information related to the payload of all layers in the at least one layer may include but is not limited to at least one of the first data set information related to the payload of all layers in the at least one layer, the first AI model information related to the payload of all layers in the at least one layer, and the first quantitative information related to the payload of all layers in the at least one layer.

[0172] It should be noted that the first data set information, the first AI model information and the first quantization information of this embodiment can be found in the relevant description of the aforementioned embodiment and will not be elaborated here.

[0173] Optionally, the at least one layer comprises at least two types of layers;

[0174] Among them, the payload of CSI reporting information associated with different types of layers is different;

[0175] or,

[0176] The input information of AI models associated with different types of layers is different;

[0177] or,

[0178] Different types of layers are associated with different AI models;

[0179] or,

[0180] Different types of layers are associated with different CSI reporting parameters, and the CSI reporting parameters include at least one of the following: CSI reporting content, CSI reporting location, and CSI reporting priority.

[0181] The following examples illustrate this embodiment:

[0182] In case 1, the payloads of CSI reporting information associated with different types of layers are different. For example, the type 1 layer is associated with the first payload, or the type 2 layer is associated with the second payload, so that the payloads between different types of layers are different. Then, the correlation between the layers can be utilized to improve the effect of AI-based CSI processing. For example, for CSI information compression, the information of the type 1 layer can be used to further compress the CSI information of the type 2 layer.

[0183] In the second case, the input information of the AI ​​models associated with different types of layers is different. For example, the input information of the AI ​​model corresponding to the type 1 layer does not include information of other layers, and the input information of the AI ​​model corresponding to the type 2 layer includes information of other layers.

[0184] Case three: Different types of layers are associated with different AI models, for example, type 1 layers are associated with AI model 1, and type 2 layers are associated with AI model 2.

[0185] Case 4: Different types of layers are associated with different CSI reporting parameters. For example, a type 1 layer is associated with a first CSI reporting parameter. For example, the first CSI reporting parameter may include at least one of a first CSI reporting content, a first CSI reporting position, and a first CSI reporting priority. A type 2 layer is associated with a second CSI reporting parameter. The second CSI reporting parameter may include at least one of a second CSI reporting content, a second CSI reporting position, and a second CSI reporting priority.

[0186] It should be noted that the above-mentioned situations can be reasonably combined according to actual needs, and this embodiment does not limit this.

[0187] Optionally, the first information related to the first object includes first information related to payload of each type of layer of the at least two types of layers.

[0188] For example, if the at least one layer includes a first type of layer and a second type of layer, the first information related to the first object may include first information related to the first type of layer and the payload and first information related to the second type of layer and the payload. It is understood that each of the above types of layers may include at least one layer.

[0189] It should be noted that, for the first information related to the payload in this embodiment, reference can be made to the relevant description of the aforementioned embodiment, and no further details will be given here.

[0190] Optionally, the first information related to the first object includes first information related to layer information of each type of layer in the at least two types of layers. Exemplarily, the layer information may include but is not limited to at least one of layer type, layer identifier, layer priority, etc.

[0191] Optionally, the at least two types of layers include a first type of layer and a second type of layer, wherein the AI ​​model corresponding to the first type of layer is used for processing based on the channel state information corresponding to the first type of layer, and the AI ​​model corresponding to the second type of layer is used for processing based on the channel state information corresponding to the second type of layer and information of associated layers of the second type of layer.

[0192] In this embodiment, the AI ​​model corresponding to the above-mentioned first type of layer is processed only based on the channel state information corresponding to the above-mentioned first type of layer, that is, the above-mentioned first type of layer can be understood as a layer that performs independent encoding or decoding processing.

[0193] The AI ​​model corresponding to the second type of layer is used to perform processing based on the channel state information corresponding to the second type of layer and information about associated layers of the second type of layer, where the associated layers of the second type of layer may include at least one layer of the at least one layer other than the second type of layer. In other words, the second type of layer can be understood as a layer that needs to be encoded or decoded in conjunction with information from other layers.

[0194] In some optional embodiments, the associated layer of the second type of layer may be a first type of layer, that is, the second type of layer is a layer that needs to be encoded or decoded in combination with information of the first type of layer.

[0195] Optionally, the information of the associated layer of the second type of layer includes at least one of the following: output information of the AI ​​model corresponding to the associated layer of the second type of layer, input information of the AI ​​model corresponding to the associated layer of the second type of layer, and output information of the intermediate layer of the AI ​​model corresponding to the associated layer of the second type of layer.

[0196] Optionally, the associated layer of the first target layer in the second type of layer includes at least one of the following: a first layer, a second layer, and a third layer;

[0197] The first target layer and the first layer are both layers corresponding to the first time unit, and the first layer is a layer lower than the first target layer, or the index of the first layer is the index of the first target layer minus 1;

[0198] The second layer is a layer corresponding to a second time unit, the second time unit is a time unit before the first time unit, and the index of the second layer is the same as the index of the first target layer;

[0199] The third layer is the topmost layer or the layer with the largest index among all layers corresponding to the third time unit, or the third layer is the topmost layer or the layer with the largest index among all layers corresponding to the third time unit and the corresponding AI model uses the information of the lower layer for processing, and the third time unit is the time unit before the first time unit.

[0200] In this embodiment, the first target layer may be any layer of the second type of layer.

[0201] The first time unit may be any time unit, and the time unit may include but is not limited to a time slot, a sub-time slot, a frame or a sub-frame.

[0202] The second time unit is a time unit before the first time unit. For example, the second time unit may be the K1 time units before the first time unit, where K1 is a positive integer. For example, the value of K1 may be 1 or 2. In some optional embodiments, the second time unit is a time unit before the first time unit in which the AI ​​model was most recently used for CSI processing.

[0203] The third time unit and the second time unit may be the same time unit or different time units. For example, the second time unit is the K2 time units before the first time unit, where K2 is a positive integer, for example, the value of K2 may be 1 or 2. In some optional embodiments, the third time unit may be the time unit in which the AI ​​model was most recently used for CSI processing before the first time unit.

[0204] It should be noted that, when the second type of layer includes multiple layers, the AI ​​model corresponding to each of the multiple layers is processed based on the channel state information corresponding to the layer and the information of the associated layer of the layer. In addition, the types of associated layers of different layers in the multiple layers may be different, for example, the associated layers of some layers are the first layer, the associated layers of some layers are the second layer, the associated layers of some layers include the first layer and the second layer, etc.; or the types of associated layers of different layers in the multiple layers may be the same, for example, the associated layers of each of the multiple layers are all the first layer, or are all the second layer, or include the first layer and the second layer, etc. For example, the above-mentioned second type of layers includes layer 1, layer 2 and layer 3 of the first time unit, wherein the associated layer of layer 1 of the first time unit may include the first layer, that is, the associated layer of layer 1 of the first time unit is layer 0 of the first time unit; the associated layer of layer 2 of the first time unit may include the first layer and the second layer, that is, the associated layer of layer 2 of the first time unit includes layer 1 of the first time unit and layer 2 of the second time unit; the associated layer of layer 3 of the first time unit may include the second layer, that is, the associated layer of layer 3 of the first time unit is layer 3 of the second time unit.

[0205] For example, the first layer can be understood as a layer that transmits information in a first transmission mode, wherein the first transmission mode is: transmitting the information of Layer x at time t to Layer (x+1) at time t, x <K t -1,K t Indicates the value of the rank corresponding to time t.

[0206] The second layer can be understood as a layer that uses the second transmission method to transmit information, wherein the second transmission method is: transmitting the information of Layer x at time t to Layer x at time t+1, x <K t -1 and x <K t+1 -1, the above K t+1 Indicates the value of the rank corresponding to time t+1.

[0207] The third layer can be understood as a layer that uses the third transmission method to transmit information, wherein the third transmission method is: Layer (K t -1) or the highest layer among all layers that use the first transfer method to transfer information, is transferred to Layer 0 at time t+1, or is transferred to the lowest layer among all layers that use the first transfer method to transfer information at time t+1.

[0208] It should be noted that the above-mentioned transmission of information to a certain layer can be understood as transmitting the information to the AI ​​model corresponding to the layer for processing. In addition, the above-mentioned various transmission methods can be combined in any way, and the following examples are illustrated with reference to the accompanying figures:

[0209] Example 1: Some layers transmit information in the first transmission mode, that is, the information of Layer x at time t is transmitted to Layer (x+1) at time t; some layers transmit information in the third transmission mode, that is, the information of Layer x at time t is transmitted to Layer (x+1) at time t, and the information of Layer (K t -1) is transmitted to Layer 0 at time t+1. For example, as shown in FIG4a , Layer 0 to Layer 2 at each time instant transmit information according to the first transmission mode, and Layer 3 at each time instant except the last time instant transmits information according to the third transmission mode.

[0210] Example 2: Some layers use the second transmission method to transmit information, that is, the information of Layer x at time t is transmitted to Layer x at time t+1. For example, as shown in Figure 4b, the layers at all times except the last time use the second transmission method to transmit information.

[0211] Example 3: Some low layers transmit information according to the first transmission method, some low layers transmit information according to the third method, and some high layers transmit information according to the second transmission method. For example, as shown in Figure 4c and Figure 4d, in Figure 4c, the above-mentioned low layers include Layer0 and Layer1, and the above-mentioned high layers include Layer2 and Layer3; in Figure 4d, the above-mentioned low layers include Layer0, Layer1 and Layer2, and the above-mentioned high layers include Layer3.

[0212] Example 4: Some low layers transmit information according to the second transmission method, some high layers transmit information according to the first transmission method, and some high layers transmit information according to the third transmission method. For example, as shown in Figure 4e, in Figure 4e, the above-mentioned low layers include Layer0 and Layer1, and the above-mentioned high layers include Layer2 and Layer3.

[0213] Example 5: Some layers use both the first and third transmission modes to transmit information, some layers use the second transmission mode to transmit information, and some layers use the first transmission mode to transmit information, for example, as shown in FIG4f .

[0214] Example 6: Some layers use the first transmission method to transmit information, and some layers use both the first transmission method and the second transmission method to transmit information. For example, as shown in Figure 4g, in Figure 4g, Layer 0 and Layer 1 use both the first transmission method and the second transmission method to transmit information, and Layer 2 uses the first transmission method to transmit information.

[0215] It should be noted that the arrows in FIG. 4 a to FIG. 4 g indicate the transmission of information.

[0216] Optionally, in a case where the first target layer includes a fourth layer, the associated layer of the fourth layer includes at least one of the second layer and the third layer;

[0217] or,

[0218] In a case where the first target layer includes a fifth layer, the associated layer of the fifth layer includes at least one of the first layer and the second layer;

[0219] The fourth layer is the lowest layer or the layer with the smallest index among all layers corresponding to the second time unit, or the fourth layer is the lowest layer or the layer with the smallest index among all layers corresponding to the second time unit that transmit information to a higher layer;

[0220] The fifth layer is a layer different from the fourth layer among all layers corresponding to the second time unit, or the fifth layer is a layer different from the fourth layer among all layers corresponding to the second time unit that transmit information to a higher layer.

[0221] Illustratively, as shown in FIG4 a , the fourth layer is Layer 0, and the fifth layer includes Layer 1 to Layer 3; or, as shown in FIG4 e , the fourth layer is Layer 2, and the fifth layer is Layer 3.

[0222] Optionally, the associated layer of the second target layer in the second type of layer includes at least one of the following: the sixth layer, the seventh layer, and the eighth layer;

[0223] The second target layer and the sixth layer are both layers corresponding to the fourth time unit, and the sixth layer is one layer higher than the second target layer, or the index of the sixth layer is the index of the second target layer plus 1;

[0224] The seventh layer is a layer corresponding to a fifth time unit, the fifth time unit is a time unit before the fourth time unit, and an index of the seventh layer is the same as an index of the second target layer;

[0225] The eighth layer is the lowest layer or the layer with the smallest index among all layers corresponding to the sixth time unit, or the eighth layer is the lowest layer or the layer with the smallest index among all layers corresponding to the sixth time unit in which the AI ​​model corresponding to the sixth time unit uses information from a higher layer for processing, and the sixth time unit is the time unit before the fourth time unit.

[0226] In this embodiment, the second target layer may be any layer of the second type of layer.

[0227] The fourth time unit may be any time unit, and the time unit may include but is not limited to a time slot, a sub-time slot, a frame or a sub-frame.

[0228] The fifth time unit is a time unit before the fourth time unit. For example, the fifth time unit may be the K3 time units before the fourth time unit, where K3 is a positive integer, such as 1 or 2. In some optional embodiments, the fifth time unit is the time unit of the most recent time CSI processing using the AI ​​model before the fourth time unit.

[0229] The sixth time unit and the fifth time unit may be the same time unit or different time units. For example, the fifth time unit is the K4 time units before the fourth time unit, where K4 is a positive integer, such as 1 or 2. In some optional embodiments, the sixth time unit may be the time unit of the most recent CSI processing performed using the AI ​​model before the fourth time unit.

[0230] It should be noted that, when the second type of layer includes multiple layers, the AI ​​model corresponding to each of the multiple layers is processed based on the channel state information corresponding to the layer and the information of the associated layer of the layer. In addition, the types of associated layers of different layers in the multiple layers may be different, for example, the associated layers of some layers are the sixth layer, the associated layers of some layers are the seventh layer, the associated layers of some layers include the sixth layer and the seventh layer, etc.; or the types of associated layers of different layers in the multiple layers may be the same, for example, the associated layers of each of the multiple layers are all the sixth layer, or are all the seventh layer, or include the sixth layer and the seventh layer, etc. For example, the above-mentioned second type of layers include layer 1, layer 2 and layer 3 of the fourth time unit, wherein the associated layer of layer 1 of the fourth time unit may include the sixth layer, that is, the associated layer of layer 1 of the fourth time unit is layer 2 of the fourth time unit; the associated layer of layer 2 of the fourth time unit may include the sixth layer and the seventh layer, that is, the associated layer of layer 2 of the fourth time unit includes layer 3 of the fourth time unit and layer 2 of the fifth time unit; the associated layer of layer 3 of the fourth time unit may include the seventh layer, that is, the associated layer of layer 3 of the fourth time unit is layer 3 of the fifth time unit.

[0231] For example, the sixth layer can be understood as a layer that uses the fourth transmission method to transmit information, wherein the fourth transmission method is: transmitting the information of Layer x at time t to Layer (x-1) at time t, x <K t -1,K t Indicates the value of the rank corresponding to time t.

[0232] The seventh layer can be understood as a layer that uses the second transmission method to transmit information, wherein the second transmission method is: transmitting the information of Layer x at time t to Layer x at time t+1, x <K t -1 and x <K t+1 -1, the above K t+1 Indicates the value of the rank corresponding to time t+1.

[0233] The eighth layer can be understood as a layer that uses the fifth transfer method to transmit information, wherein the fifth transfer method is to transmit the information of Layer 0 at time t or the lowest layer of all layers that use the fourth transfer method to transmit information to Layer K at time t+1. t+1 -1, or the highest layer of all layers that adopt the fourth transmission mode to transmit information at time t+1, where K t+1 Indicates the value of the rank at time t+1.

[0234] It should be noted that the above-mentioned transmission of information to a certain layer can be understood as transmitting the information to the AI ​​model corresponding to the layer for processing. In addition, the above-mentioned various transmission methods can be combined in any way, and the following examples are illustrated with reference to the accompanying figures:

[0235] Example 1: Some layers transfer information using the fourth transfer method, that is, the information of Layer x at time t is transferred to Layer x-1 at time t, and some layers transfer information using the fifth transfer method, that is, the information of Layer 0 at time t is transferred to Layer K at time t+1. t+1 -1, for example, as shown in FIG5a, Layer 1 to Layer 3 at each moment transmit information according to the fourth transmission mode, and Layer 0 at each moment except the last moment transmits information according to the fifth transmission mode.

[0236] Example 2: Some low layers transmit information according to the fourth transmission method, some low layers transmit information according to the fifth transmission method, and some high layers transmit information according to the second transmission method, for example, as shown in Figure 5b and Figure 5c, wherein, in Figure 5b, the above-mentioned low layers include Layer0 and Layer1, and the above-mentioned high layers include Layer2 and Layer3; in Figure 5c, the above-mentioned low layers include Layer0, Layer1 and Layer2, and the above-mentioned high layers include Layer3.

[0237] Example 4: Some low layers transmit information according to the second transmission method, some high layers transmit information according to the fourth transmission method, and some high layers transmit information according to the fifth transmission method. For example, as shown in Figure 5d, in Figure 5d, the above-mentioned low layers include Layer0 and Layer1, and the above-mentioned high layers include Layer2 and Layer3.

[0238] Example 5: Some layers transmit information according to both the second transmission mode and the fourth transmission mode, and some layers transmit information according to the second transmission mode, for example, as shown in FIG5e.

[0239] Example 6: Some layers use the fourth transmission method to transmit information, and some layers use both the fourth transmission method and the second transmission method to transmit information. For example, as shown in Figure 5f, in Figure 5f, Layer 0 and Layer 1 use both the fourth transmission method and the second transmission method to transmit information, and Layer 2 and Layer 3 use the fourth transmission method to transmit information.

[0240] It should be noted that the arrows in FIG. 5 a to FIG. 5 f indicate information transmission paths.

[0241] Optionally, in a case where the second target layer includes a ninth layer, the associated layer of the ninth layer includes at least one of the seventh layer and the eighth layer;

[0242] or,

[0243] In a case where the second target layer includes the tenth layer, the associated layer of the tenth layer includes at least one of the sixth layer and the seventh layer;

[0244] The ninth layer is the topmost layer or the layer with the largest index among all layers corresponding to the sixth time unit, or the ninth layer is the topmost layer or the layer with the largest index among all layers corresponding to the sixth time unit that transmit information to a lower layer.

[0245] The tenth layer is a layer different from the ninth layer among all layers corresponding to the sixth time unit, or the tenth layer is a layer different from the ninth layer among all layers corresponding to the sixth time unit that transmit information to a higher layer.

[0246] Exemplarily, as shown in FIG5 a , the ninth layer is Layer 0, and the tenth layer includes Layer 1 to Layer 3; or, as shown in FIG5 d , the ninth layer is Layer 2, and the tenth layer is Layer 3.

[0247] Optionally, the CSI reporting information corresponding to the second type of layer includes the encoded CSI information corresponding to the second type of layer and relevant information of the associated layer of the second type of layer.

[0248] In this embodiment, the CSI reporting information corresponding to the above-mentioned second type of layer includes relevant information of the associated layer of the second type of layer, for example, the identifier of the associated layer of the second type of layer, so that the decoding end can quickly determine the associated layer of the above-mentioned second type of layer, and then the decoding end can obtain the information of the associated layer of the above-mentioned second type of layer to decode the encoded CSI information corresponding to the above-mentioned second type of layer to obtain the decoded CSI information or the decompressed CSI information or the recovered CSI information or the reconstructed CSI information.

[0249] Optionally, the CSI reporting information corresponding to the second type of layer includes layer type information.

[0250] Optionally, the CSI reporting information includes layer type information.

[0251] Optionally, the CSI reporting information includes at least one of first data set information, layer information, payload information, and encoded CSI information.

[0252] Optionally, the relevant information of the associated layer of the second type of layer includes at least one of an index of the associated layer of the second type of layer and a time unit corresponding to the associated layer of the second type of layer.

[0253] In some optional embodiments, the associated layer of the second type of layer is the first type of layer, that is, the AI ​​model corresponding to the second type of layer needs to be compressed or decompressed based on the information of the first type of layer (that is, the output information of the AI ​​model corresponding to the first type of layer). Accordingly, the CSI reporting information corresponding to the second type of layer may include at least one of the encoded CSI information corresponding to the second type of layer and the index (index), time unit, etc. of the associated first type of layer. For example, as shown in Figure 6, the AI ​​model corresponding to the P type layer needs to be encoded or decoded based on the information of the I type layer.

[0254] Optionally, the payload of the CSI reporting information corresponding to the second type of layer is smaller than the payload of the CSI reporting information corresponding to the first type of layer.

[0255] Optionally, the at least two types of layers further include a third type of layer, and the payload of CSI reporting information corresponding to the third type of layer is smaller than the payload of CSI reporting information corresponding to the second type of layer.

[0256] For example, as shown in FIG6 , the payload of the CSI reporting information corresponding to the B type layer is smaller than the payload of the CSI reporting information corresponding to the P type layer, and the payload of the CSI reporting information corresponding to the P type layer is smaller than the payload of the CSI reporting information corresponding to the I type layer.

[0257] Optionally, the AI ​​model corresponding to the third type of layer is used for processing based on the channel state information corresponding to the third type of layer and the information of the associated layer of the third type of layer, and the associated layer of the third type of layer includes the first type of layer and the second type of layer.

[0258] For example, as shown in Figure 6, the AI ​​model corresponding to the B-type layer needs to be processed in combination with the information of the P-type layer and the I-type layer. For example, on the encoding side, the AI ​​model corresponding to the B-type layer needs to encode the CSI of the B-type layer in combination with the information of the P-type layer and the I-type layer; on the decoding side, the AI ​​model corresponding to the B-type layer needs to decode the CSI of the B-type layer in combination with the information of the P-type layer and the I-type layer.

[0259] It can be understood that, for the encoding end, the channel state information corresponding to the above third type of layer is the channel state information before encoding; for the decoding end, the channel state information corresponding to the above third type of layer may be the channel state information after encoding.

[0260] Optionally, the CSI reporting information corresponding to the first type of layer is located before the CSI reporting information corresponding to the second type of layer, or,

[0261] The CSI reporting information corresponding to the first type of layer is located in a specific CSI reporting group.

[0262] Exemplarily, the CSI reporting information corresponding to the first type of layer may be located in a specific CSI reporting group among multiple CSI reporting groups predefined by the protocol, wherein the specific CSI reporting group may be predefined by the protocol or may be configured by a network layer device.

[0263] Optionally, the Nth layer in the at least one layer is the first type of layer, or the Mth group of layers in the at least one layer is the first type of layer, and both M and N are positive integers predefined by the protocol.

[0264] Exemplarily, the Nth layer in at least one layer related to CSI reporting information can be predefined by agreement as the first type of layer, or the Mth group of layers in at least one layer related to CSI reporting information can be predefined by agreement as the first type of layer.

[0265] Optionally, the CSI reporting information associated with one reporting identifier only includes CSI reporting information corresponding to one layer of the first type.

[0266] For example, it may be predefined by agreement that the CSI reporting information associated with a reporting identifier (Report ID) only includes CSI reporting information corresponding to a layer of the first type, which is beneficial to reducing the payload of the CSI reporting information and thus saving CSI reporting resources.

[0267] Optionally, the CSI reporting information carried by the CSI reporting resources used for one CSI reporting only includes CSI reporting information corresponding to one layer of the first type.

[0268] In this embodiment, the CSI reporting resource used for a CSI report may carry CSI reporting information associated with one or more reporting identifiers. The CSI reporting resource may include, but is not limited to, a physical uplink shared channel (PUSCH), a physical uplink control channel (PUCCH), and the like.

[0269] In this embodiment, the CSI reporting information carried on the CSI reporting resources used for one CSI reporting only includes CSI reporting information corresponding to one layer of the first type, which is beneficial to reducing the payload of the CSI reporting information and thus saving CSI reporting resources.

[0270] Optionally, the payload includes at least one of the following: the bit size of the CSI reporting information, the number of floating-point numbers in the CSI reporting information, the number of elements in the CSI reporting information, the output dimension of the first AI model, the quantization method of each element or floating-point number in the CSI reporting information, and the quantization method of the CSI reporting information.

[0271] In this embodiment, the number of elements in the CSI reporting information may also be referred to as the number of parameters included in the CSI reporting information. The output dimension of the first AI model may be understood as the number of output elements or output parameters of the first AI model.

[0272] Optionally, the method further includes:

[0273] The communication device sends or receives capability information related to the first object.

[0274] Exemplarily, the capability information related to the first object may include capability information related to the payload, for example, supported AI models related to the payload, or the capability information related to the first object may include capability information related to at least one layer or at least one rank, for example, IA models supported by each layer.

[0275] It should be noted that if the above step 301 is that the communication device receives the first information related to the first object, then this embodiment is that the communication device sends the capability information related to the first object; if the above step 301 is that the communication device sends the first information related to the first object, then this embodiment is that the communication device receives the capability information related to the first object.

[0276] For example, the terminal reports the capability information of the terminal related to the first object to the network side device, and then the network side device can send the first information related to the first object to the terminal based on the capability information of the terminal related to the first object; or, the network side device can send the capability information of the network side device related to the first object to the terminal, and then the terminal can send the first information related to the first object to the network side device based on the capability information of the network side device related to the first object.

[0277] Optionally, the first object includes the payload, and the capability information related to the payload includes at least one of the following:

[0278] Supported payload candidate values;

[0279] Supported candidate quantization methods;

[0280] Supported AI models related to payloads.

[0281] Exemplarily, if the capability information related to the payload reported by the terminal includes candidate values ​​of supported payloads, the network side device can select a payload value from the candidate values ​​of supported payloads, determine the first information related to the value of the payload and send it to the terminal.

[0282] Optionally, the first object includes the at least one layer or the at least one rank; and the capability information related to the at least one layer or the at least one rank includes at least one of the following:

[0283] AI models supported by each layer;

[0284] The types of layers supported.

[0285] Exemplarily, the type of the above-mentioned layer may include at least one of a first type (eg, I type), a second type (eg, P type), and a third type (eg, B type).

[0286] Optionally, the first information includes at least one of the following: layer type, number of layers, layer compression method, and payload information.

[0287] Exemplarily, the above-mentioned layer type may include at least one of the above-mentioned first type, second type and third type.

[0288] Exemplarily, CSI reporting information can be generated based on the first information. For example, if the above-mentioned first information includes a layer type, the communication device can generate CSI reporting information corresponding to the layer type, or, if the above-mentioned first information includes a layer compression method, the communication device can use the layer compression method to compress and obtain the CSI reporting information; or, the CSI reporting information can be decoded based on the first information. For example, if the above-mentioned first information includes a layer compression method, the communication device can use the decompression method corresponding to the layer compression method to decode the above-mentioned CSI reporting information.

[0289] Optionally, the communication device is a terminal, and the method further includes:

[0290] The communication device reports CSI reporting information related to the first information.

[0291] Exemplarily, the CSI reporting information related to the first information may be CSI reporting information determined based on the first information. It is understandable that the CSI reporting information is CSI reporting information based on AI processing.

[0292] Optionally, before the communication device receives or sends the first information related to the first object, the method further includes:

[0293] The communication device sends or receives a first request message, wherein the first request message is used to request the first information, and the first request message includes at least one of the following: payload, layer type, number of layers, and layer compression method.

[0294] It should be noted that if the above step 301 is that the communication device receives the first information related to the first object, then this embodiment is that the communication device sends a first request message; if the above step 301 is that the communication device sends the first information related to the first object, then this embodiment is that the communication device receives the first request message.

[0295] Exemplarily, the terminal sends a first request message to the network-side device, where the first request message includes information related to the first object, such as payload, layer type, number of layers, and layer compression method; the network-side device sends first information related to the first object to the terminal based on the first request message; or, the network-side device sends a first request message to the terminal, where the first request message includes information related to the first object, such as payload, layer type, number of layers, and layer compression method; the terminal sends first information related to the first object to the network-side device based on the first request message. For example, the first request message sent by the network side to the terminal device includes payload information and dataset request information, and the terminal sends a dataset that satisfies the payload information to the network-side device.

[0296] It should be noted that the encoding involved in the embodiments of the present application can also be called compression, and the decoding involved in the embodiments of the present application can also be called decompression.

[0297] It should be noted that the information transmission method provided in the embodiments of the present application can be executed by an information transmission device, or a control module in the information transmission device for executing the information transmission method. In the embodiments of the present application, the information transmission device provided in the embodiments of the present application is described by taking the information transmission device executing the information transmission method as an example.

[0298] Please refer to FIG. 7 , which is a structural diagram of an information transmission device provided in an embodiment of the present application. As shown in FIG. 7 , the information transmission device 700 includes:

[0299] a transceiver module, configured to receive or send first information related to a first object;

[0300] Among them, the first object includes at least one of the following: payload, at least one layer, and at least one rank; the first information is used to determine a first artificial intelligence AI model or to configure AI-based channel state information CSI reporting information, and the first AI model is used to generate the CSI reporting information or to decode the CSI reporting information.

[0301] Optionally, the first information related to the first object includes at least one of the following:

[0302] The first data set information related to the payload;

[0303] The first AI model information related to the payload;

[0304] The first quantitative information related to the payload.

[0305] Optionally, a first data set information is associated with CSI reporting information of a payload;

[0306] or,

[0307] Different first data set information is associated with CSI reporting information of different payloads.

[0308] Optionally, one first data set information is associated with CSI reporting information of multiple payloads.

[0309] Optionally, the first data set information includes at least one of the following:

[0310] At least one data set information, wherein the at least one data set information includes at least one of input information of the AI ​​model and output information of the AI ​​model;

[0311] At least one payload information associated with a dataset;

[0312] At least one dataset identifier associated with the dataset information;

[0313] AI model information associated with at least one dataset.

[0314] Optionally, a first AI model information is associated with CSI reporting information of a payload;

[0315] or,

[0316] Different first AI model information is associated with CSI reporting information of different payloads.

[0317] Optionally, the first AI model information includes:

[0318] Identification information of the AI ​​model;

[0319] Payload information associated with the AI ​​model;

[0320] AI model type or function information;

[0321] Parameter information of the AI ​​model.

[0322] Optionally, one first quantization information is associated with CSI reporting information of a payload;

[0323] or,

[0324] A first quantization information is associated with CSI reporting information of multiple payloads.

[0325] Optionally, the first quantitative information includes at least one of the following:

[0326] A quantization method corresponding to the AI ​​model output dimension, where the AI ​​model output dimension is used to indicate the number of output elements of the AI ​​model;

[0327] A quantization method corresponding to the payload size, where the payload size is the payload size of the CSI reporting information.

[0328] Optionally, different AI model output dimensions correspond to different quantization methods, or AI model output dimensions within different output dimension value ranges correspond to different quantization methods;

[0329] or,

[0330] Different payload sizes correspond to different quantization methods, or payload sizes within different payload size ranges correspond to different quantization methods.

[0331] Optionally, the first information related to the first object includes at least one of the following:

[0332] first information related to a payload in one of the at least one layer;

[0333] First information related to the payload of each layer in the at least one layer;

[0334] First information related to the payload of all layers in the at least one layer.

[0335] Optionally, the at least one layer comprises at least two types of layers;

[0336] Among them, the payload of CSI reporting information associated with different types of layers is different;

[0337] or,

[0338] The input information of AI models associated with different types of layers is different;

[0339] or,

[0340] Different types of layers are associated with different AI models;

[0341] or,

[0342] Different types of layers are associated with different CSI reporting parameters, and the CSI reporting parameters include at least one of the following: CSI reporting content, CSI reporting location, and CSI reporting priority.

[0343] Optionally, the first information related to the first object includes first information related to payload of each type of layer of the at least two types of layers.

[0344] Optionally, the at least two types of layers include a first type of layer and a second type of layer, wherein the AI ​​model corresponding to the first type of layer is used for processing based on the channel state information corresponding to the first type of layer, and the AI ​​model corresponding to the second type of layer is used for processing based on the channel state information corresponding to the second type of layer and information of associated layers of the second type of layer.

[0345] Optionally, the information of the associated layer of the second type of layer includes at least one of the following: output information of the AI ​​model corresponding to the associated layer of the second type of layer, input information of the AI ​​model corresponding to the associated layer of the second type of layer, and output information of the intermediate layer of the AI ​​model corresponding to the associated layer of the second type of layer.

[0346] Optionally, the associated layer of the first target layer in the second type of layer includes at least one of the following: a first layer, a second layer, and a third layer;

[0347] The first target layer and the first layer are both layers corresponding to the first time unit, and the first layer is a layer lower than the first target layer, or the index of the first layer is the index of the first target layer minus 1;

[0348] The second layer is a layer corresponding to a second time unit, the second time unit is a time unit before the first time unit, and the index of the second layer is the same as the index of the first target layer;

[0349] The third layer is the topmost layer or the layer with the largest index among all layers corresponding to the third time unit, or the third layer is the topmost layer or the layer with the largest index among all layers corresponding to the third time unit and the corresponding AI model uses the information of the lower layer for processing, and the third time unit is the time unit before the first time unit.

[0350] Optionally, in a case where the first target layer includes a fourth layer, the associated layer of the fourth layer includes at least one of the second layer and the third layer;

[0351] or,

[0352] In a case where the first target layer includes a fifth layer, the associated layer of the fifth layer includes at least one of the first layer and the second layer;

[0353] The fourth layer is the lowest layer or the layer with the smallest index among all layers corresponding to the second time unit, or the fourth layer is the lowest layer or the layer with the smallest index among all layers corresponding to the second time unit that transmit information to a higher layer;

[0354] The fifth layer is a layer different from the fourth layer among all layers corresponding to the second time unit, or the fifth layer is a layer different from the fourth layer among all layers corresponding to the second time unit that transmit information to a higher layer.

[0355] Optionally, the associated layer of the second target layer in the second type of layer includes at least one of the following: the sixth layer, the seventh layer, and the eighth layer;

[0356] The second target layer and the sixth layer are both layers corresponding to the fourth time unit, and the sixth layer is one layer higher than the second target layer, or the index of the sixth layer is the index of the second target layer plus 1;

[0357] The seventh layer is a layer corresponding to a fifth time unit, the fifth time unit is a time unit before the fourth time unit, and an index of the seventh layer is the same as an index of the second target layer;

[0358] The eighth layer is the lowest layer or the layer with the smallest index among all layers corresponding to the sixth time unit, or the eighth layer is the lowest layer or the layer with the smallest index among all layers corresponding to the sixth time unit in which the AI ​​model corresponding to the sixth time unit uses information from a higher layer for processing, and the sixth time unit is the time unit before the fourth time unit.

[0359] Optionally, in a case where the second target layer includes a ninth layer, the associated layer of the ninth layer includes at least one of the seventh layer and the eighth layer;

[0360] or,

[0361] In a case where the second target layer includes the tenth layer, the associated layer of the tenth layer includes at least one of the sixth layer and the seventh layer;

[0362] The ninth layer is the topmost layer or the layer with the largest index among all layers corresponding to the sixth time unit, or the ninth layer is the topmost layer or the layer with the largest index among all layers corresponding to the sixth time unit that transmit information to a lower layer.

[0363] The tenth layer is a layer different from the ninth layer among all layers corresponding to the sixth time unit, or the tenth layer is a layer different from the ninth layer among all layers corresponding to the sixth time unit that transmit information to a higher layer.

[0364] Optionally, the CSI reporting information corresponding to the second type of layer includes the encoded CSI information corresponding to the second type of layer and relevant information of the associated layer of the second type of layer.

[0365] Optionally, the relevant information of the associated layer of the second type of layer includes at least one of an index of the associated layer of the second type of layer and a time unit corresponding to the associated layer of the second type of layer.

[0366] Optionally, the payload of the CSI reporting information corresponding to the second type of layer is smaller than the payload of the CSI reporting information corresponding to the first type of layer.

[0367] Optionally, the at least two types of layers further include a third type of layer, and the payload of CSI reporting information corresponding to the third type of layer is smaller than the payload of CSI reporting information corresponding to the second type of layer.

[0368] Optionally, the AI ​​model corresponding to the third type of layer is used for processing based on the channel state information corresponding to the third type of layer and the information of the associated layer of the third type of layer, and the associated layer of the third type of layer includes the first type of layer and the second type of layer.

[0369] Optionally, the CSI reporting information corresponding to the first type of layer is located before the CSI reporting information corresponding to the second type of layer, or,

[0370] The CSI reporting information corresponding to the first type of layer is located in a specific CSI reporting group.

[0371] Optionally, the Nth layer in the at least one layer is the first type of layer, or the Mth group of layers in the at least one layer is the first type of layer, and both M and N are positive integers predefined by the protocol.

[0372] Optionally, the CSI reporting information associated with one reporting identifier only includes CSI reporting information corresponding to one layer of the first type.

[0373] Optionally, the CSI reporting information carried by the CSI reporting resources used for one CSI reporting only includes CSI reporting information corresponding to one layer of the first type.

[0374] Optionally, the payload includes at least one of the following: the bit size of the CSI reporting information, the number of floating-point numbers in the CSI reporting information, the number of elements in the CSI reporting information, the output dimension of the first AI model, the quantization method of each element or floating-point number in the CSI reporting information, and the quantization method of the CSI reporting information.

[0375] Optionally, the transceiver module is further configured to:

[0376] Send or receive capability information related to the first object.

[0377] Optionally, the first object includes the payload, and the capability information related to the payload includes at least one of the following:

[0378] Supported payload candidate values;

[0379] Supported candidate quantization methods;

[0380] Supported AI models related to payloads.

[0381] Optionally, the first object includes the at least one layer or the at least one rank; and the capability information related to the at least one layer or the at least one rank includes at least one of the following:

[0382] AI models supported by each layer;

[0383] The types of layers supported.

[0384] Optionally, the first information includes at least one of the following: layer type, number of layers, layer compression method, and payload indication information.

[0385] Optionally, the transceiver module is further configured to:

[0386] Report CSI reporting information related to the first information.

[0387] The information transmission 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 in an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or a network-side device, or can be a device other than a terminal or a network-side device. For example, the terminal can include but is not limited to the types of terminals 11 listed above, the network-side device can include but is not limited to the types of network-side devices 12 listed above, and other devices can be servers, network attached storage (NAS), etc., which are not specifically limited in the embodiments of the present application.

[0388] The information transmission device provided in the embodiment of the present application can implement each process implemented in the method embodiment of Figure 3 and achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0389] Optionally, as shown in Figure 8, an embodiment of the present application further provides a communication device 800, including a processor 801 and a memory 802, wherein the memory 802 stores a program or instruction that can be run on the processor 801. For example, when the communication device 800 is a terminal, the program or instruction is executed by the processor 801 to implement the various steps of the above-mentioned information transmission method embodiment and can achieve the same technical effect. When the communication device 800 is a network-side device, the program or instruction is executed by the processor 801 to implement the various steps of the above-mentioned information transmission method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0390] An embodiment of the present application also provides a terminal, comprising a processor and a communication interface, wherein the communication interface is used to receive or send first information related to a first object; wherein the first object includes at least one of the following: a payload, at least one layer, and at least one rank; the first information is used to determine a first artificial intelligence AI model or to configure AI-based channel state information CSI reporting information, and the first AI model is used to generate the CSI reporting information or to decode the CSI reporting information. Each implementation process and implementation method of the above-mentioned method embodiment can be applied to the terminal embodiment and can achieve the same technical effect. Specifically, Figure 9 is a schematic diagram of the hardware structure of a terminal that implements an embodiment of the present application.

[0391] The terminal 900 includes but is not limited to: a radio frequency unit 901, a network module 902, an audio output unit 903, an input unit 904, a sensor 905, a display unit 906, a user input unit 907, an interface unit 908, a memory 909 and at least some of the components of the processor 910.

[0392] Those skilled in the art will appreciate that the terminal 900 may further include a power source (such as a battery) for powering various components. The power source may be logically connected to the processor 910 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The terminal structure shown in FIG9 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.

[0393] It should be understood that in an embodiment of the present application, the input unit 904 may include a graphics processing unit (GPU) 9041 and a microphone 9042, and the graphics processor 9041 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 906 may include a display panel 9061, and the display panel 9061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 907 includes a touch panel 9071 and at least one of other input devices 9072. The touch panel 9071 is also called a touch screen. The touch panel 9071 may include two parts: a touch detection device and a touch controller. Other input devices 9072 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 an operating stick, which will not be repeated here.

[0394] In the embodiment of the present application, after receiving downlink data from a network-side device, the RF unit 901 may transmit the data to the processor 910 for processing. Furthermore, the RF unit 901 may send uplink data to the network-side device. Typically, the RF unit 901 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low-noise amplifier, a duplexer, and the like.

[0395] The memory 909 can be used to store software programs or instructions and various data. The memory 909 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 909 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 909 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0396] Processor 910 may include one or more processing units. Optionally, processor 910 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 910.

[0397] Among them, the radio frequency unit 901 is used to receive or send first information related to a first object; wherein, the first object includes at least one of the following: payload, at least one layer, and at least one rank; the first information is used to determine a first artificial intelligence AI model or to configure AI-based channel state information CSI reporting information, and the first AI model is used to generate the CSI reporting information or to decode the CSI reporting information.

[0398] It can be understood that the implementation process of each implementation method mentioned in this embodiment can refer to the relevant description of the aforementioned method embodiment and achieve the same or corresponding technical effects. To avoid repetition, it will not be repeated here.

[0399] The embodiment of the present application also provides a network-side device, including a processor and a communication interface, the communication interface is used to receive or send first information related to a first object; wherein the first object includes at least one of the following: payload, at least one layer, at least one rank; the first information is used to determine a first artificial intelligence AI model or to configure AI-based channel state information CSI reporting information, and the first AI model is used to generate the CSI reporting information or to decode the CSI reporting information. Each implementation process and implementation method of the above method embodiment can be applied to the network-side device embodiment and can achieve the same technical effect.

[0400] Specifically, an embodiment of the present application also provides a network-side device. As shown in Figure 10, the network-side device 1000 includes: an antenna 1001, a radio frequency device 1002, a baseband device 1003, a processor 1004, and a memory 1005. Antenna 1001 is connected to radio frequency device 1002. In the uplink direction, radio frequency device 1002 receives information via antenna 1001 and sends the received information to baseband device 1003 for processing. In the downlink direction, baseband device 1003 processes the information to be transmitted and sends it to radio frequency device 1002. Radio frequency device 1002 processes the received information and sends it through antenna 1001.

[0401] The method executed by the network-side device in the above embodiment may be implemented in the baseband device 1003 , which includes a baseband processor.

[0402] The baseband device 1003 may, for example, include at least one baseband board, on which multiple chips are arranged, as shown in Figure 10, one of which is, for example, a baseband processor, which is connected to the memory 1005 through a bus interface to call the program in the memory 1005 and execute the network device operations shown in the above method embodiment.

[0403] The network side device may further include a network interface 1006, which is, for example, a Common Public Radio Interface (CPRI).

[0404] Specifically, the network side device 1000 of the embodiment of the present application also includes: instructions or programs stored in the memory 1005 and executable on the processor 1004. The processor 1004 calls the instructions or programs in the memory 1005 to execute the method of execution of each module shown in Figure 7 and achieve the same technical effect. To avoid repetition, it will not be described here.

[0405] 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 information transmission method embodiment are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0406] 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.

[0407] 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 information transmission method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0408] 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.

[0409] An embodiment of the present application further provides a computer program / program product, which is stored in a storage medium. The computer program / program product is executed by at least one processor to implement the various processes of the above-mentioned information transmission method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0410] 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.

[0411] 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.

[0412] 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 information transmission method, comprising: The communication device receives or sends first information related to the first object; Among them, the first object includes at least one of the following: payload, at least one layer, and at least one rank; the first information is used to determine a first artificial intelligence AI model or to configure AI-based channel state information CSI reporting information, and the first AI model is used to generate the CSI reporting information or to decode the CSI reporting information.

2. The method according to claim 1, wherein The first information related to the first object includes at least one of the following: First data set information related to the payload; The first AI model information related to the payload; The first quantitative information related to the payload.

3. The method according to claim 2, wherein: A first data set information is associated with CSI reporting information of a payload; or, Different first data set information is associated with CSI reporting information of different payloads.

4. The method according to claim 2, wherein: A first data set information is associated with CSI reporting information of multiple payloads.

5. The method according to any one of claims 2 to 4, wherein The first data set information includes at least one of the following: At least one data set information, wherein the at least one data set information includes at least one of input information of the AI ​​model and output information of the AI ​​model; At least one payload information associated with a dataset; At least one dataset identifier associated with the dataset information; AI model information associated with at least one dataset.

6. The method according to any one of claims 2 to 5, wherein A first AI model information is associated with CSI reporting information of a payload; or, Different first AI model information is associated with CSI reporting information of different payloads.

7. The method according to any one of claims 2 to 6, wherein The first AI model information includes: Identification information of the AI ​​model; Payload information associated with the AI ​​model; AI model type or function information; Parameter information of the AI ​​model.

8. The method according to any one of claims 2 to 7, wherein A first quantization information is associated with CSI reporting information of a payload; or, A first quantization information is associated with CSI reporting information of multiple payloads.

9. The method according to any one of claims 2 to 8, wherein The first quantitative information includes at least one of the following: A quantization method corresponding to the AI ​​model output dimension, where the AI ​​model output dimension is used to indicate the number of output elements of the AI ​​model; A quantization method corresponding to the payload size, where the payload size is the payload size of the CSI reporting information.

10. The method according to claim 9, wherein: Different AI model output dimensions correspond to different quantization methods, or AI model output dimensions within different output dimension value ranges correspond to different quantization methods; or, Different payload sizes correspond to different quantization methods, or payload sizes within different payload size ranges correspond to different quantization methods.

11. The method according to any one of claims 1 to 10, wherein The first information related to the first object includes at least one of the following: first information related to a payload in one of the at least one layer; First information related to the payload of each layer in the at least one layer; First information related to the payload of all layers in the at least one layer.

12. The method according to any one of claims 1 to 11, wherein The at least one layer comprises at least two types of layers; Among them, the payload of CSI reporting information associated with different types of layers is different; or, The input information of AI models associated with different types of layers is different; or, Different types of layers are associated with different AI models; or, Different types of layers are associated with different CSI reporting parameters, and the CSI reporting parameters include at least one of the following: CSI reporting content, CSI reporting location, and CSI reporting priority.

13. The method according to claim 12, wherein: The first information related to the first object includes first information related to payload of each type of layer of the at least two types of layers.

14. The method according to claim 12 or 13, wherein: The at least two types of layers include a first type of layer and a second type of layer, wherein the AI ​​model corresponding to the first type of layer is used for processing based on the channel state information corresponding to the first type of layer, and the AI ​​model corresponding to the second type of layer is used for processing based on the channel state information corresponding to the second type of layer and information of associated layers of the second type of layer.

15. The method according to claim 14, wherein The information of the associated layer of the second type of layer includes at least one of the following: output information of the AI ​​model corresponding to the associated layer of the second type of layer, input information of the AI ​​model corresponding to the associated layer of the second type of layer, and output information of the intermediate layer of the AI ​​model corresponding to the associated layer of the second type of layer.

16. The method according to claim 14 or 15, wherein: The associated layer of the first target layer in the second type of layers includes at least one of the following: a first layer, a second layer, and a third layer; The first target layer and the first layer are both layers corresponding to the first time unit, and the first layer is a layer lower than the first target layer, or the index of the first layer is the index of the first target layer minus 1; The second layer is a layer corresponding to a second time unit, the second time unit is a time unit before the first time unit, and an index of the second layer is the same as an index of the first target layer; The third layer is the topmost layer or the layer with the largest index among all layers corresponding to the third time unit, or the third layer is the topmost layer or the layer with the largest index among all layers corresponding to the third time unit and the corresponding AI model uses the information of the lower layer for processing, and the third time unit is the time unit before the first time unit.

17. The method according to claim 16, wherein In a case where the first target layer includes a fourth layer, the associated layer of the fourth layer includes at least one of the second layer and the third layer; or, In a case where the first target layer includes a fifth layer, the associated layer of the fifth layer includes at least one of the first layer and the second layer; The fourth layer is the lowest layer or the layer with the smallest index among all layers corresponding to the second time unit, or the fourth layer is the lowest layer or the layer with the smallest index among all layers corresponding to the second time unit that transmit information to a higher layer; The fifth layer is a layer different from the fourth layer among all layers corresponding to the second time unit, or the fifth layer is a layer different from the fourth layer among all layers corresponding to the second time unit that transmit information to a higher layer.

18. The method according to any one of claims 14 to 17, wherein The associated layer of the second target layer in the second type of layer includes at least one of the following: the sixth layer, the seventh layer, and the eighth layer; The second target layer and the sixth layer are both layers corresponding to the fourth time unit, and the sixth layer is one layer higher than the second target layer, or the index of the sixth layer is the index of the second target layer plus 1; The seventh layer is a layer corresponding to a fifth time unit, the fifth time unit is a time unit before the fourth time unit, and an index of the seventh layer is the same as an index of the second target layer; The eighth layer is the lowest layer or the layer with the smallest index among all layers corresponding to the sixth time unit, or the eighth layer is the lowest layer or the layer with the smallest index among all layers corresponding to the sixth time unit in which the AI ​​model corresponding to the sixth time unit uses information from a higher layer for processing, and the sixth time unit is the time unit before the fourth time unit.

19. The method according to claim 18, wherein In a case where the second target layer includes a ninth layer, the associated layer of the ninth layer includes at least one of the seventh layer and the eighth layer; or, In a case where the second target layer includes the tenth layer, the associated layer of the tenth layer includes at least one of the sixth layer and the seventh layer; The ninth layer is the topmost layer or the layer with the largest index among all layers corresponding to the sixth time unit, or the ninth layer is the topmost layer or the layer with the largest index among all layers corresponding to the sixth time unit that transmit information to a lower layer. The tenth layer is a layer different from the ninth layer among all layers corresponding to the sixth time unit, or the tenth layer is a layer different from the ninth layer among all layers corresponding to the sixth time unit that transmit information to a higher layer.

20. The method according to any one of claims 14 to 19, wherein The CSI reporting information corresponding to the second type of layer includes the encoded CSI information corresponding to the second type of layer and related information of the associated layer of the second type of layer.

21. The method according to claim 20, wherein The related information of the associated layer of the second type of layer includes at least one of an index of the associated layer of the second type of layer and a time unit corresponding to the associated layer of the second type of layer.

22. The method according to any one of claims 14 to 21, wherein The payload of the CSI reporting information corresponding to the second type of layer is smaller than the payload of the CSI reporting information corresponding to the first type of layer.

23. The method according to any one of claims 14 to 22, wherein The at least two types of layers further include a third type of layer, and a payload of CSI reporting information corresponding to the third type of layer is smaller than a payload of CSI reporting information corresponding to the second type of layer.

24. The method according to claim 23, wherein The AI ​​model corresponding to the third type of layer is used for processing based on the channel state information corresponding to the third type of layer and the information of the associated layer of the third type of layer, and the associated layer of the third type of layer includes the first type of layer and the second type of layer.

25. The method according to any one of claims 14 to 24, wherein The CSI reporting information corresponding to the first type of layer is located before the CSI reporting information corresponding to the second type of layer, or, The CSI reporting information corresponding to the first type of layer is located in a specific CSI reporting group.

26. The method according to any one of claims 14 to 24, wherein The Nth layer in the at least one layer is a layer of the first type, or the layers in the Mth group in the at least one layer are layers of the first type, where M and N are both positive integers predefined by the protocol.

27. The method according to any one of claims 14 to 26, wherein The CSI reporting information associated with one reporting identifier only includes CSI reporting information corresponding to one layer of the first type.

28. The method according to any one of claims 14 to 26, wherein The CSI reporting information carried by the CSI reporting resources used for one CSI reporting only includes CSI reporting information corresponding to one layer of the first type.

29. The method according to any one of claims 1 to 28, wherein The payload includes at least one of the following: a bit size of the CSI reporting information, a number of floating-point numbers in the CSI reporting information, a number of elements in the CSI reporting information, an output dimension of the first AI model, a quantization method for each element or floating-point number in the CSI reporting information, and a quantization method for the CSI reporting information.

30. The method according to any one of claims 1 to 29, wherein The method further comprises: The communication device sends or receives capability information related to the first object.

31. The method according to claim 30, wherein The first object includes the payload, and the capability information related to the payload includes at least one of the following: Supported payload candidate values; Supported candidate quantization methods; Supported AI models related to payloads.

32. The method according to claim 30 or 31, wherein The first object includes the at least one layer or the at least one rank; and the capability information related to the at least one layer or the at least one rank includes at least one of the following: AI models supported by each layer; The types of layers supported.

33. The method according to any one of claims 1 to 32, wherein The first information includes at least one of the following: layer type, number of layers, layer compression method, and payload indication information.

34. The method according to any one of claims 1 to 33, wherein The communication device is a terminal, and the method further includes: The communication device reports CSI reporting information related to the first information.

35. An information transmission device comprising: a transceiver module, configured to receive or send first information related to a first object; Among them, the first object includes at least one of the following: payload, at least one layer, and at least one rank; the first information is used to determine a first artificial intelligence AI model or to configure AI-based channel state information CSI reporting information, and the first AI model is used to generate the CSI reporting information or to decode the CSI reporting information.

36. A communication device comprising a processor and a memory, wherein the memory stores a program or instruction that can be run on the processor, and when the program or instruction is executed by the processor, the steps of the information transmission method according to any one of claims 1 to 34 are implemented.

37. 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, the steps of the information transmission method according to any one of claims 1 to 34 are implemented.

Images