WO2024032695A1 - Csi prediction processing method and apparatus, communication device, and readable storage medium - Google Patents

Abstract

The present application relates to the technical field of communications, and discloses a CSI prediction processing method and apparatus, a communication device, and a readable storage medium. The method comprises: a first device receives first information from a second device; and the first device executes a first behavior according to the first information, wherein the execution of the first behavior comprises determining whether to adjust a first prediction parameter, the first prediction parameter being used for CSI prediction.

Description

CSI prediction processing method, device, communication equipment and readable storage medium

Cross-references to related applications

This application claims priority to Chinese Patent Application No. 202210956569.X filed in China on August 10, 2022, the entire content of which is incorporated herein by reference.

Technical field

The present application belongs to the field of communication technology, and specifically relates to a CSI prediction processing method, device, communication equipment and readable storage medium.

Background technique

Channel state information can describe the current channel environment. In mobile communication networks, the base station transmits channel state information-reference signal (CSI-RS), and the terminal evaluates the channel state information and provides quantitative feedback. By introducing Channel State Information (CSI) feedback information to the base station, the base station can make timely adjustments when sending the channel state information reference signal, thereby reducing the bit error rate at the terminal and obtaining the optimal received signal.

In wireless communications, channel prediction can be used to make up for the delay between channel measurement and actual scheduling operations and improve throughput. Currently, how to update the parameters of channel prediction is an issue that needs to be solved urgently.

Contents of the invention

Embodiments of the present application provide a CSI prediction processing method, device, communication equipment, and readable storage medium to solve the problem of how to update channel prediction parameters.

The first aspect provides a CSI prediction processing method, including:

The first device receives the first information from the second device;

The first device performs a first behavior based on the first information;

Wherein, performing the first behavior includes determining whether to adjust a first prediction parameter, where the first prediction parameter is used for CSI prediction.

In the second aspect, a CSI prediction processing method is provided, including:

The second device sends first information to the first device. The first information is used by the first device to perform a first behavior. The performing the first behavior includes determining and adjusting a first prediction parameter. The first prediction parameter is in CSI prediction.

In a third aspect, a CSI prediction processing device is provided, including:

A first receiving module, configured to receive the first information from the second device;

An execution module, configured to execute the first behavior according to the first information;

Wherein, performing the first behavior includes determining whether to adjust a first prediction parameter, where the first prediction parameter is used for CSI prediction.

In a fourth aspect, a CSI prediction processing device is provided, including:

The third sending module is configured to send first information to the first device. The first information is used by the first device to perform a first behavior. The performing the first behavior includes determining to adjust the first prediction parameter. One prediction parameter is used for CSI prediction.

In a fifth aspect, a communication device is provided, including: a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor Implement the steps of the method described in the first aspect or the second aspect.

In a sixth aspect, a readable storage medium is provided. Programs or instructions are stored on the readable storage medium. When the programs or instructions are executed by a processor, the steps of the method described in the first or second aspect are implemented. .

In a seventh aspect, a chip is provided. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the first aspect or the second aspect. the steps of the method described.

In an eighth aspect, a computer program/program product is provided, the computer program/program product is stored in a non-transitory storage medium, and the program/program product is executed by at least one processor to implement the first aspect or the steps of the method described in the second aspect.

A ninth aspect provides a communication system. The communication system includes a terminal and a network side device. The terminal is configured to perform the steps of the method described in the first aspect or the second aspect. The network side device is configured to perform The steps of the method as described in the first aspect or the second aspect.

In this embodiment of the present application, the first device may determine whether to adjust the first prediction parameter for CSI prediction based on the first information from the second device, thereby achieving signaling interaction between the first device and the second device, Completing the adjustment of CSI prediction parameters in the wireless communication system can improve the execution efficiency and prediction accuracy of CSI prediction.

Description of drawings

Figure 1 is a schematic diagram of a neural network;

Figure 2 is a schematic diagram of a neuron;

Figure 3 is a schematic diagram of AI-based CSI prediction;

Figure 4 is a schematic diagram of the performance of predicting different future moments;

Figure 5 is a schematic diagram of predicting the performance of +5ms in the future using different amounts of historical CSI;

Figure 6 is a schematic architectural diagram of a wireless communication system according to an embodiment of the present application;

Figure 7 is one of the flow charts of the CSI prediction processing method provided by the embodiment of the present application;

Figure 8 is the second flow chart of the CSI prediction processing method provided by the embodiment of the present application;

Figure 9 is the third flowchart of the CSI prediction processing method provided by the embodiment of the present application;

Figure 10 is the fourth flowchart of the CSI prediction processing method provided by the embodiment of the present application;

Figure 11 is the fifth flowchart of the CSI prediction processing method provided by the embodiment of the present application;

Figure 12 is one of the schematic diagrams of the CSI prediction processing device provided by the embodiment of the present application;

Figure 13 is the second schematic diagram of the CSI prediction processing device provided by the embodiment of the present application;

Figure 14 is a schematic diagram of a terminal provided by an embodiment of the present application;

Figure 15 is a schematic diagram of a network side device provided by an embodiment of the present application;

Figure 16 is a schematic diagram of a communication device provided by an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art fall within the scope of protection of this application.

The terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects and are not used to describe a specific order or sequence. It is to be understood that the terms so used are interchangeable under appropriate circumstances so that the embodiments of the present application can be practiced in sequences other than those illustrated or described herein, and that "first" and "second" are distinguished objects It is usually one type, and the number of objects is not limited. For example, the first object can be one or multiple. In addition, "and/or" in the description and claims indicates at least one of the connected objects, and the character "/" generally indicates that the related objects are in an "or" relationship.

It is worth pointing out that the technology described in the embodiments of this application is not limited to Long Term Evolution (LTE)/LTE Evolution (LTE-Advanced, LTE-A) systems, and can also be used in other wireless communication systems, such as code 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) and other systems. The terms "system" and "network" in the embodiments of this application are often used interchangeably, and the described technology can be used not only for the above-mentioned systems and radio technologies, but also for other systems and radio technologies. The following description describes a New Radio (NR) system for example purposes, and NR terminology is used in much of the following description, but these techniques can also be applied to applications other than NR system applications, such as 6th ^generation Generation, 6G) communication system.

In order to facilitate understanding of the implementation of the present application, the following technical points are first introduced below.

1. Introduction to neural networks

Artificial intelligence is currently widely used in various fields. Artificial Intelligence (AI) modules have a variety of implementation methods, such as neural networks, decision trees, support vector machines, Bayesian classifiers, etc.

This application takes a neural network as an example for explanation, but does not limit the specific type of AI module. The structure of the neural network is shown in Figure 1.

Among them, the neural network is composed of neurons, and the schematic diagram of neurons is shown in Figure 2. Where a ₁ , a ₂ ,…a _K is the input, w is the weight (multiplicative coefficient), b is the bias (additive coefficient), σ(.) is the activation function, z=a ₁ w ₁ +…+ a _k w _k +…+a _K w _K +b. Common activation functions include Sigmoid function, tanh function, Rectified Linear Unit (ReLU), etc.

The parameters of neural networks can be optimized through optimization algorithms. An optimization algorithm is a type of algorithm that can minimize or maximize an objective function (sometimes also called a loss function). The objective function is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, construct a neural network model f(.). With the model, the predicted output f(x) can be obtained based on the input The difference between (f(x)-Y), this is the loss function. If the appropriate W and b are found to minimize the value of the above loss function, the smaller the loss value, the closer the model is to the real situation.

Currently, common optimization algorithms are basically based on the error back propagation (error Back Propagation, BP) algorithm. The basic idea of BP algorithm is that the learning process consists of two processes: forward propagation of signals and back propagation of errors. During forward propagation, the input sample is passed in from the input layer, processed layer by layer by each hidden layer, and then transmitted to the output layer. If the actual output of the output layer does not match the expected output, it will enter the error backpropagation stage. Error backpropagation is to propagate the output error back to the input layer layer by layer through the hidden layer in some form, and allocate the error to all units in each layer to obtain the error signal of each layer unit. This error signal is used as a correction for each unit. The basis for the weight. This process of adjusting the weights of each layer in forward signal propagation and error back propagation is carried out over and over again. The process of continuous adjustment of weights is the learning and training process of the network. This process continues until the error of the network output is reduced to an acceptable level, or until a preset number of learning times.

Generally speaking, depending on the type of solution, the AI algorithm selected and the model used are also different. According to related technologies, the main method to use AI to improve the network performance of the fifth generation mobile communication technology ( ^5th Generation, 5G) is to enhance or replace existing algorithms or processing modules through neural network-based algorithms and models. In certain scenarios, neural network-based algorithms and models can achieve better performance than deterministic-based algorithms. The more commonly used neural networks include deep neural networks, convolutional neural networks, and recurrent neural networks. With the help of existing AI tools, the construction, training and verification of neural networks can be achieved.

Replacing modules in related systems through AI or machine learning (ML) methods can effectively improve system performance.

For example, in channel state information (CSI) prediction, historical CSI is input to the AI model, and the AI model analyzes the time domain change characteristics of the channel and outputs future CSI. The details are shown in Figure 3.

The corresponding system performance is shown in Figures 4 and 5. It can be seen that CSI prediction will have a very large performance gain compared to non-prediction solutions. At the same time, depending on the predicted future time, the achievable prediction accuracy will also be different, as shown in Figure 4.

In addition, the number of historical CSI input to the AI model will also affect the performance of CSI prediction. Figure 5 depicts the performance of using different numbers of historical CSI to predict the future +5ms. It can be seen that as the number of historical CSI increases, the prediction accuracy will also increase. However, more historical CSI numbers mean higher complexity and cache overhead, so the number of historical CSIs cannot be increased blindly.

The accuracy of CSI prediction is related to the prediction parameters. However, it is difficult to accurately determine these prediction parameters in advance in actual systems. In this regard, the system can only provide an initial prediction parameter based on the current channel environment, and the prediction parameters need to be adjusted later based on the actual prediction performance. If no adjustments are made, the prediction performance will be difficult to guarantee, and the total throughput of the system will eventually be used. is not up to expectations.

Figure 6 shows a block diagram of a wireless communication system to which embodiments of the present application are applicable. The wireless communication system includes a terminal 61 and a network side device 62. Among them, the wireless communication system can be a communication system with wireless AI functions such as 5G-Advanced or 6G.

Among them, the terminal 61 can be a mobile phone, a tablet computer (Tablet Personal Computer), a laptop computer (Laptop Computer), or a notebook computer, a personal digital assistant (Personal Digital Assistant, PDA), a handheld computer, a netbook, or a super mobile personal computer. (ultra-mobile personal computer, UMPC), mobile Internet device (Mobile Internet Device, MID), augmented reality (augmented reality, AR)/virtual reality (VR) equipment, robots, wearable devices (Wearable Device) , vehicle user equipment (VUE), pedestrian terminal (Pedestrian User Equipment, PUE), smart home (home equipment with wireless communication functions, such as refrigerators, TVs, washing machines or furniture, etc.), game consoles, personal computers (personal computer, PC), teller machine or self-service machine and 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) bracelets, smart anklets, etc.), smart wristbands, smart clothing, etc. In addition to the above terminal equipment, the terminal involved in this application can also be a chip within the terminal, such as a modem chip or a system on chip (SoC). It should be noted that the specific type of terminal 61 is not limited in the embodiment of this application.

The network side equipment 62 may include access network equipment or core network equipment, where the access network equipment may also be called wireless access network equipment, radio access network (Radio Access Network, RAN), radio access network function or wireless access network unit. Access network equipment can include base stations, Wireless Local Area Network (WLAN) access points or WiFi nodes, etc. The base station can be called Node B, Evolved Node B (eNB), access point, base transceiver station ( Base Transceiver Station (BTS), radio base station, radio transceiver, Basic Service Set (BSS), Extended Service Set (ESS), home B-node, home evolved B-node, sending and receiving point ( Transmitting Receiving Point (TRP) or some other suitable term in the field, as long as the same technical effect is achieved, the base station is not limited to specific technical terms. It should be noted that in the embodiment of this application, only the NR system is used The base station is introduced as an example, and the specific type of base station is not limited.

Core network equipment may include but is not limited to at least one of the following: core network nodes, core network functions, mobility management entities (Mobility Management Entity, MME), access and mobility management functions (AMF), session management Function (Session Management Function, SMF), User Plane Function (UPF), Policy Control Function (PCF), Policy and Charging Rules Function (PCRF), Edge Application Service Discovery Function (Edge Application Server Discovery Function, EASDF), Unified Data Management (UDM), Unified Data Repository (UDR), Home Subscriber Server (HSS), centralized network configuration (Centralized network configuration, CNC), Network Repository Function (NRF), Network Exposure Function (NEF), Local NEF (Local NEF, or L-NEF), Binding Support Function (Binding Support Function , BSF), application function (Application Function, AF), etc. It should be noted that in the embodiment of this application, only the core network equipment in the NR system is used as an example for introduction, and the specific type of the core network equipment is not limited.

The AI model involved in the embodiment of this application may also be called an ML model.

The CSI prediction processing method, device, communication device and readable storage medium provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings through some embodiments and application scenarios.

Referring to Figure 7, an embodiment of the present application provides a CSI prediction processing method. Specific steps include: step 701 and step 702.

Step 701: The first device receives the first information from the second device;

Step 702: The first device performs the first behavior according to the first information;

Wherein, performing the first behavior includes determining whether to adjust a first prediction parameter, where the first prediction parameter is used for CSI prediction.

In an implementation manner of the present application, performing the first behavior further includes: determining to start CSI prediction, or determining to prohibit CSI prediction, or determining to stop CSI prediction.

Optionally, the first device may obtain CSI prediction performance based on the first information, and decide which first behavior to perform based on the CSI prediction performance.

In one embodiment of the present application, the method further includes:

The first device sends second information to the second device, where the second information is used to indicate one of the following:

(1) The first device adjusts the first prediction parameter;

(2) The first device does not adjust the first prediction parameter;

(3) The second device starts CSI prediction;

(4) The second device disables CSI prediction;

(5) The second device stops CSI prediction.

For example, in the case where the first device decides to adjust the first prediction parameter, the first device may adjust the first prediction parameter and then send the adjusted first prediction parameter to the second device.

For another example, in the case where the first device decides to start CSI prediction, the first device may instruct the second device to start CSI prediction.

For another example, in the case where the first device decides to stop CSI prediction, the first device may instruct the second device to stop CSI prediction.

In one embodiment of the present application, the method further includes:

Before the first device receives the first information from the second device, the first device sends the first prediction parameter to the second device.

Optionally, the second device can verify the prediction performance based on the received first prediction parameter, and decide whether to adjust the prediction parameter based on the prediction performance, etc.

In an implementation manner of the present application, the first information includes at least one of the following:

(1) Third information, the third information is used to indicate the feasibility of CSI prediction by the second device;

For example, the third information is 1-bit indication information, "1" indicates that CSI prediction is performed, and "0" indicates that CSI prediction is not performed.

(2) Second prediction parameters, the second prediction parameters include prediction parameters provided by the second device;

It can be understood that the second prediction parameter is a prediction parameter suggested by the second device.

(3) First CSI, the first CSI is used to represent the CSI prediction result corresponding to the third prediction parameter;

It can be understood that the first CSI can also be called predicted CSI, and the CSI result predicted by using the third prediction parameter. For example, if the specified time is A, and the third prediction parameter includes 1 ms in the future or 2 ms in the future, then A+1 ms in the future is predicted. Or the CSI result at A+2ms in the future.

(4) The third prediction parameter;

Optionally, the third prediction parameter is the same as or different from the first prediction parameter and/or the second prediction parameter.

(5) Second CSI, the second CSI is used to represent the actual CSI measurement result corresponding to the third prediction parameter;

It can be understood that the second CSI can also be called measured CSI. The CSI result is measured using the third prediction parameter. For example, if the specified time is A and the third prediction parameter includes 1ms in the future or 2ms in the future, then when A+1ms is reached Or when A+2ms, the CSI is actually measured and the second CSI is obtained.

It can be understood that the first CSI and the second CSI can be sent in two times, or the first CSI can be saved first, and after the second CSI is obtained, the first CSI and the second CSI can be sent together, where the first CSI and the second CSI can be sent together. The second CSI can be compressed independently or jointly.

(6) Fourth information, the fourth information is used to indicate the performance of CSI prediction.

It can be understood that performance can be used to quantify the quality of CSI prediction.

Optionally, the fourth information is determined by the second device based on the first CSI and the second CSI.

Optionally, the fourth information includes at least one of the following: (1) error indicators, such as error, mean square error, normalized mean square error, etc., (2) accuracy indicators, such as cosine similarity.

For example, the second device calculates an error, a mean square error, a normalized mean square error or a cosine similarity based on the first CSI and the second CSI, thereby obtaining fourth information that can quantify the performance of CSI prediction.

In an implementation manner of the present application, the first device performs a first behavior based on the first information, including:

In the case where the first information includes at least the first CSI and the second CSI, the first device determines fifth information based on the first CSI and the second CSI, and the fifth information Information is used to represent the performance of CSI prediction;

The first device determines (or makes a decision) based on the fifth information whether to adjust the first prediction parameter;

or,

In the case where the first information includes at least the fourth information, the first device determines (or decides) whether to adjust the first prediction parameter based on the fourth information.

In an implementation manner of the present application, the first device performs a first behavior based on the first information, and further includes:

In the case where the first information includes at least the third information and a second prediction parameter, the first device determines (or decides) whether the second device performs prediction according to the second prediction parameter;

or,

In the case where the first information includes at least the first CSI, the first device determines (or decides) whether to perform scheduling according to the first CSI.

In one embodiment of the present application, the first prediction parameter, the second prediction parameter, or the third prediction parameter includes at least one of the following:

(1) Prediction time information;

Optionally, the prediction time information is used to represent the time position to be measured, such as 1 ms in the future, 2 ms in the future, etc., or 1 time slot in the future, 2 slots in the future, etc.

(2) CSI interval;

Optionally, the CSI interval is used to represent the interval between multiple predicted historical CSIs.

The above-mentioned CSI interval may also be called a CSI period.

(3) Number of CSI;

Optionally, the number of CSIs is used to represent the number of predicted multiple historical CSIs.

(4) CSI window length;

Optionally, the CSI window length is used to represent the time domain length occupied by multiple predicted historical CSIs.

(5) Predicted frequency domain information;

Optionally, the predicted frequency domain information may include: at least one of the number of Physical Resource Blocks (PRBs, PRBs), PRB positions, subband numbers, subband positions, etc.

(6) Predicted spatial information.

Optionally, the predicted airspace information may include: at least one of the number of antennas, the number of ports, the number of beams, etc.

In an implementation manner of the present application, the reference signal related to the second CSI includes at least one of the following:

(1) Periodic CSI-RS;

(2) Aperiodic CSI-RS;

(3) CSI-RS cluster (burst);

(4) Verify dedicated RS.

It can be understood that the verification-specific RS includes a new RS specifically used for performance verification or supervision of CSI prediction.

That is, in this embodiment, CSI measurement can be performed using at least one of the reference signals in (1) to (4) above to obtain actual CSI measurement results.

In an implementation manner of the present application, the first device is a network-side device or terminal, and the second device is a network-side device or terminal.

In this embodiment of the present application, the first device may determine whether to adjust the first prediction parameter for CSI prediction based on the first information from the second device, thereby achieving signaling interaction between the first device and the second device, Exchanging CSI prediction related information and completing the adjustment of CSI prediction parameters in the wireless communication system can improve the execution efficiency and prediction accuracy of CSI prediction.

Referring to Figure 8, this embodiment of the present application provides a CSI prediction processing method, and specific steps include: step 801.

Step 801: The second device sends first information to the first device. The first information is used by the first device to perform a first behavior. The performing the first behavior includes determining to adjust the first prediction parameter. The first Prediction parameters are used for CSI prediction.

In an implementation manner of the present application, the first behavior further includes: determining to start CSI prediction, or determining to prohibit CSI prediction, or determining to stop CSI prediction.

In one embodiment of the present application, the method further includes:

The second device receives second information from the first device, the second information being used to indicate one of the following:

(1) The first device adjusts the first prediction parameter;

(2) The first device does not adjust the first prediction parameter;

(3) The second device starts CSI prediction;

(4) The second device prohibits CSI prediction;

(5) The second device stops CSI prediction.

In one embodiment of the present application, the method further includes:

The second device receives the first prediction parameter from the first device before the second device sends the first information to the first device.

In an implementation manner of the present application, the first information includes at least one of the following:

(1) Third information, the third information is used to indicate the feasibility of CSI prediction by the second device;

(2) Second prediction parameters, the second prediction parameters include prediction parameters provided by the second device;

(3) First CSI, the first CSI is used to represent the CSI prediction result corresponding to the third prediction parameter;

(4) The third prediction parameter;

Optionally, the third prediction parameter is the same as or different from the first prediction parameter and/or the second prediction parameter.

(5) Second CSI, the second CSI is used to represent the actual CSI measurement result corresponding to the third prediction parameter;

(6) Fourth information, the fourth information is used to indicate the performance of CSI prediction.

In one embodiment of the present application, the method further includes:

The second device determines the first CSI and/or the second CSI according to the third prediction parameter.

It can be understood that the third prediction parameter may include prediction time information, and the predicted CSI and/or measured CSI are performed based on the prediction time information to obtain the first CSI and/or the second CSI.

For example, if the specified time is A and the third prediction parameter includes the future 1ms or the future 2ms, then the CSI at the future A+1ms or the future A+2ms will be predicted to obtain the first CSI. When A+1ms or A+2ms is reached, the actual measured CSI , get the second CSI.

In one embodiment of the present application, the method further includes:

The second device determines the fourth information based on the first CSI and the second CSI.

For example, the second device calculates an error, a mean square error, a normalized mean square error or a cosine similarity based on the first CSI and the second CSI, thereby obtaining fourth information that can quantify the performance of CSI prediction.

It can be understood that the first CSI and the second CSI can be sent in two times, or the first CSI can be saved first, and then the second CSI can be obtained. After the second CSI is obtained, the first CSI and the second CSI are sent together. The first CSI and the second CSI can be compressed independently or jointly.

In one embodiment of the present application, the method further includes:

The second device adjusts the first prediction parameter according to the fourth information to obtain an adjusted prediction parameter.

That is, the first prediction parameter is adjusted according to the prediction performance in order to make the predicted CSI and the measured CSI not much different, thereby improving the accuracy of CSI prediction using the adjusted prediction parameters.

In an implementation manner of the present application, the adjusted prediction parameter is the same as or different from the second prediction parameter and/or the third prediction parameter.

In an implementation manner of the present application, the fourth information includes at least one of the following: an error index and a precision index.

In one embodiment of the present application, the first prediction parameter, the second prediction parameter, or the third prediction parameter includes at least one of the following:

(1) Prediction time information;

Optionally, the prediction time information is used to represent the time position to be measured, such as 1 ms in the future, 2 ms in the future, etc., or 1 time slot in the future, 2 slots in the future, etc.

(2) CSI interval;

Optionally, the CSI interval is used to represent the interval between multiple predicted historical CSIs.

The above-mentioned CSI interval may also be called a CSI period.

(3) Number of CSI;

Optionally, the number of CSIs is used to represent the number of predicted multiple historical CSIs.

(4) CSI window length;

Optionally, the CSI window length is used to represent the time domain length occupied by multiple predicted historical CSIs.

(5) Predicted frequency domain information;

Optionally, the predicted frequency domain information may include: at least one of the number of Physical Resource Blocks (PRBs, PRBs), PRB positions, subband numbers, subband positions, etc.

(6) Predicted spatial information.

Optionally, the predicted airspace information may include: at least one of the number of antennas, the number of ports, the number of beams, etc.

In an implementation manner of the present application, the reference signal related to the second CSI includes at least one of the following:

(1) Periodic CSI-RS;

(2) Aperiodic CSI-RS;

(3) CSI-RS cluster;

(4) Verify dedicated RS.

In an implementation manner of the present application, the first device is a network-side device or terminal, and the second device is a network-side device or terminal.

In this embodiment of the present application, the second device sends the first information to the first device, and the first information is used to assist the first device Determine whether to adjust the first prediction parameter used for CSI prediction, thereby realizing the exchange of CSI prediction related information through signaling interaction between the first device and the second device, completing the adjustment of the CSI prediction parameter in the wireless communication system, and improving the CSI Prediction execution efficiency and prediction accuracy.

In order to better understand the implementation manner of the present application, the following is introduced in conjunction with Embodiment 1 to Embodiment 3.

Embodiment 1

See Figure 9, the specific steps are as follows:

Step 1: The first device sends the first prediction parameter;

Step 2: The second device verifies the performance of CSI prediction;

For example, the second device can obtain the first CSI and the second CSI based on the third prediction parameter, where the first CSI is the predicted CSI and the second CSI is the measured CSI, and the second device can obtain the fourth information based on the first CSI and the second CSI. , the fourth information is used to represent the performance of CSI prediction;

The third prediction parameter may be the same as or different from the first prediction parameter.

Step 3: The second device decides to execute step 4, step 5, step 6 or step 7 based on the performance of the CSI prediction in step 2;

Step 4: The second device adjusts the first prediction parameter, obtains the new first prediction parameter, and then returns to step 2;

Step 5: The second device reports the third information and the fourth information to the first device, and the first device decides whether to adjust the first prediction parameter based on the third information and the fourth information;

Step 6: The second device reports the second prediction parameter to the first device, and the first device decides whether to perform CSI prediction according to the second prediction parameter. The second prediction parameter includes the prediction provided by the second device. parameter;

Step 7: The second device reports the first CSI and the third prediction parameter to the first device, and the first device decides whether to use the first CSI for scheduling.

Embodiment 2

See Figure 10, the specific steps are as follows:

Step 1: The base station sends the first prediction parameter;

Step 2: The terminal obtains the first CSI, where the first CSI is used to represent the CSI prediction result corresponding to the third prediction parameter;

The third prediction parameter may be the same as or different from the first prediction parameter.

Step 3: The terminal feeds back the first CSI to the base station;

Step 4: The terminal feeds back the second CSI to the base station, where the second CSI is used to represent the actual CSI measurement result corresponding to the third prediction parameter;

Step 5: The base station obtains the fourth information based on the first CSI and the second CSI, that is, calculates the prediction performance based on the first CSI and the second CSI;

Step 6: The base station decides to execute step 7, step 8 or step 9 based on the fourth information;

Step 7: The base station adjusts the first prediction parameter and obtains a new first prediction parameter;

Step 8: The base station instructs the terminal to prohibit prediction;

Step 9: The base station instructs the terminal to perform prediction.

Embodiment 2

See Figure 11, the specific steps are as follows:

Step 1: The base station indicates the first prediction parameter to the terminal;

Step 2: The terminal obtains fourth information, where the fourth information is used to indicate the performance of CSI prediction;

For example, the terminal can obtain the first CSI and the second CSI according to the third prediction parameter, where the first CSI is the predicted CSI and the second CSI is the measured CSI, and the terminal obtains the fourth information according to the first CSI and the second CSI;

The third prediction parameter may be the same as or different from the first prediction parameter.

Step 3: The terminal feeds back the fourth information to the base station;

Step 4: The base station decides to execute step 5, step 6 or step 7 based on the fourth information;

Step 5: The base station adjusts the first prediction parameter and obtains a new first prediction parameter;

Step 6: The base station indicates that prediction is prohibited, and the terminal prohibits prediction according to the instruction of the base station;

Step 7: The base station instructs to perform prediction, and the terminal performs prediction according to the instruction of the base station.

Referring to Figure 12, an embodiment of the present application provides a CSI prediction processing device, which is applied to a first device. The device 1200 includes:

The first receiving module 1201 is used to receive the first information from the second device;

Execution module 1202, configured to execute the first behavior according to the first information;

Wherein, performing the first behavior includes determining whether to adjust a first prediction parameter, where the first prediction parameter is used for CSI prediction.

In an implementation manner of the present application, performing the first behavior further includes: determining to start CSI prediction, or determining to prohibit CSI prediction, or determining to stop CSI prediction.

In an embodiment of the present application, the device further includes:

A first sending module, configured to send second information to the second device, where the second information is used to indicate one of the following:

(1) The first device adjusts the first prediction parameter;

(2) The first device does not adjust the first prediction parameter;

(3) The second device starts CSI prediction;

(4) The second device disables CSI prediction;

(5) The second device stops CSI prediction.

In an embodiment of the present application, the device further includes:

The second sending module is configured to send the first prediction parameter to the second device before receiving the first information from the second device.

In an implementation manner of the present application, the first information includes at least one of the following:

(1) Third information, the third information is used to indicate the feasibility of CSI prediction by the second device;

(2) Second prediction parameters, the second prediction parameters include prediction parameters provided by the second device;

(3) First CSI, the first CSI is used to represent the CSI prediction result corresponding to the third prediction parameter;

(4) The third prediction parameter;

Optionally, the third prediction parameter is the same as or different from the first prediction parameter and/or the second prediction parameter.

(5) Second CSI, the second CSI is used to represent the actual CSI measurement result corresponding to the third prediction parameter;

(6) Fourth information, the fourth information is used to indicate the performance of CSI prediction.

Optionally, the fourth information includes at least one of the following: error index and accuracy index.

In an implementation manner of the present application, the fourth information is determined by the second device according to the first CSI and the second CSI.

In an implementation manner of the present application, the execution module 1202 is further used to:

When the first information includes at least the first CSI and the second CSI, determine fifth information based on the first CSI and the second CSI, where the fifth information is used to represent CSI prediction. performance;

Determine whether to adjust the first prediction parameter according to the fifth information;

or,

In the case where the first information includes at least the fourth information, it is determined whether to adjust the first prediction parameter based on the fourth information.

In an implementation manner of the present application, the execution module 1202 is also used to:

In the case where the first information includes at least the third information and a second prediction parameter, determine whether the second device performs prediction according to the second prediction parameter;

or,

If the first information includes at least the first CSI, determine whether to perform scheduling according to the first CSI.

In one embodiment of the present application, the first prediction parameter, the second prediction parameter, or the third prediction parameter includes at least one of the following:

(1) Prediction time information;

Optionally, the prediction time information is used to represent the time position to be measured, such as 1 ms in the future, 2 ms in the future, etc., or 1 time slot in the future, 2 slots in the future, etc.

(2) CSI interval;

Optionally, the CSI interval is used to represent the interval between multiple predicted historical CSIs.

The above-mentioned CSI interval may also be called a CSI period.

(3) Number of CSI;

Optionally, the number of CSIs is used to represent the number of predicted multiple historical CSIs.

(4) CSI window length;

Optionally, the CSI window length is used to represent the time domain length occupied by multiple predicted historical CSIs.

(5) Predicted frequency domain information;

Optionally, the predicted frequency domain information may include at least one of: physical resource block (Physical Resource Block, PRB) number, PRB position, subband number, subband position, etc.

(6) Predicted spatial information.

Optionally, the predicted airspace information may include: at least one of the number of antennas, the number of ports, the number of beams, etc.

In an implementation manner of the present application, the reference signal related to the second CSI includes at least one of the following:

(1) Periodic CSI-RS;

(2) Aperiodic CSI-RS;

(3) CSI-RS cluster;

(4) Verify dedicated RS.

In an implementation manner of the present application, the first device is a network-side device or terminal, and the second device is a network-side device or terminal.

The device provided by the embodiment of the present application can implement each process implemented by the method embodiment in Figure 7 and achieve the same technical effect. To avoid duplication, the details will not be described here.

Referring to Figure 13, an embodiment of the present application provides a CSI prediction processing device, which is applied to a second device. The device 1300 includes:

The third sending module 1301 is configured to send first information to the first device, the first information is used for the first device to perform a first behavior, and the execution of the first behavior includes determining to adjust the first prediction parameter, the The first prediction parameter is used for CSI prediction.

In an implementation manner of the present application, the first behavior further includes: determining to start CSI prediction, or determining to prohibit CSI prediction, or determining to stop CSI prediction.

In an embodiment of the present application, the device further includes:

A second receiving module, configured to receive second information from the first device, where the second information is used to indicate one of the following:

(1) The first device adjusts the first prediction parameter;

(2) The first device does not adjust the first prediction parameter;

(3) The second device starts CSI prediction;

(4) The second device disables CSI prediction;

(5) The second device stops CSI prediction.

In an embodiment of the present application, the device further includes:

The third receiving module is configured to receive the first prediction parameter from the first device before sending the first information to the first device.

In an implementation manner of the present application, the first information includes at least one of the following:

(1) Third information, the third information is used to indicate the feasibility of CSI prediction by the second device;

(2) Second prediction parameters, the second prediction parameters include prediction parameters provided by the second device;

(3) First CSI, the first CSI is used to represent the CSI prediction result corresponding to the third prediction parameter;

(4) The third prediction parameter;

Optionally, the third prediction parameter is the same as or different from the first prediction parameter and/or the second prediction parameter.

(5) Second CSI, the second CSI is used to represent the actual CSI measurement result corresponding to the third prediction parameter;

(6) Fourth information, the fourth information is used to indicate the performance of CSI prediction.

In an embodiment of the present application, the device further includes:

A first determination module, configured to determine the first CSI and/or the second CSI according to the third prediction parameter.

In an embodiment of the present application, the device further includes:

A second determination module is configured to determine the fourth information according to the first CSI and the second CSI.

In an embodiment of the present application, the device further includes:

An adjustment module, configured to adjust the first prediction parameter according to the fourth information to obtain an adjusted prediction parameter.

In an implementation manner of the present application, the adjusted prediction parameter is the same as or different from the second prediction parameter and/or the third prediction parameter.

In an implementation manner of the present application, the third prediction parameter is the same as or different from the first prediction parameter and/or the second prediction parameter.

In an implementation manner of the present application, the fourth information includes at least one of the following: an error index and a precision index.

In one embodiment of the present application, the first prediction parameter, the second prediction parameter, or the third prediction parameter includes at least one of the following:

(1) Prediction time information;

Optionally, the prediction time information is used to represent the time position to be measured, such as 1 ms in the future, 2 ms in the future, etc., or 1 time slot in the future, 2 slots in the future, etc.

(2) CSI interval;

Optionally, the CSI interval is used to represent the interval between multiple predicted historical CSIs.

The above-mentioned CSI interval may also be called a CSI period.

(3) Number of CSI;

Optionally, the number of CSIs is used to represent the number of predicted multiple historical CSIs.

(4) CSI window length;

Optionally, the CSI window length is used to represent the time domain length occupied by multiple predicted historical CSIs.

(5) Predicted frequency domain information;

Optionally, the predicted frequency domain information may include: at least one of the number of Physical Resource Blocks (PRBs, PRBs), PRB positions, subband numbers, subband positions, etc.

(6) Predicted spatial information.

Optionally, the predicted airspace information may include: at least one of the number of antennas, the number of ports, the number of beams, etc.

In an implementation manner of the present application, the reference signal related to the second CSI includes at least one of the following:

(1) Periodic CSI-RS;

(2) Aperiodic CSI-RS;

(3) CSI-RS cluster;

(4) Verify dedicated RS.

In an implementation manner of the present application, the first device is a network-side device or terminal, and the second device is a network-side device or terminal.

The device provided by the embodiment of the present application can implement each process implemented by the method embodiment in Figure 8 and achieve the same technical effect. To avoid duplication, the details will not be described here.

Figure 14 is a schematic diagram of the hardware structure of a terminal that implements an embodiment of the present application. The terminal 1400 includes but is not limited to: a radio frequency unit 1401, a network module 1402, an audio output unit 1403, an input unit 1404, a sensor 1405, a display unit 1406, a user input unit 1407, an interface unit 1408, a memory 1409, a processor 1440, etc. At least some parts.

Those skilled in the art can understand that the terminal 1400 may also include a power supply (such as a battery) that supplies power to various components. The power supply may be logically connected to the processor 1440 through a power management system, thereby managing charging, discharging, and power consumption through the power management system. Management and other functions. The terminal structure shown in FIG. 14 does not constitute a limitation on the terminal. The terminal may include more or fewer components than shown in the figure, or some components may be combined or arranged differently, which will not be described again here.

It should be understood that in the embodiment of the present application, the input unit 1404 may include a graphics processing unit (Graphics Processing Unit, GPU) 14041 and a microphone 14042. The graphics processor 14041 is responsible for the image capture device (GPU) in the video capture mode or the image capture mode. Process the image data of still pictures or videos obtained by cameras (such as cameras). The display unit 1406 may include a display panel 14061, which may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 507 includes at least one of a touch panel 14071 and other input devices 14072. Touch panel 14071, also known as touch screen. The touch panel 14071 may include two parts: a touch detection device and a touch controller. Other input devices 14072 may include but are not limited to physical keyboards, function keys (such as volume control keys, switch keys, etc.), trackballs, mice, and joysticks, which will not be described again here.

In this embodiment of the present application, after receiving downlink data from the network side device, the radio frequency unit 1401 can transmit it to the processor 1440 for processing; in addition, the radio frequency unit 1401 can send uplink data to the network side device. Generally, the radio frequency unit 1401 includes, but is not limited to, an antenna, amplifier, transceiver, coupler, low noise amplifier, duplexer, etc.

Memory 1409 may be used to store software programs or instructions as well as various data. The memory 1409 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, an application program or instructions required for at least one function (such as a sound playback function, Image playback function, etc.) etc. Additionally, memory 1409 may include volatile memory or nonvolatile memory, or memory 1409 may include both volatile and nonvolatile memory. Among them, the non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electrically removable memory. Erase programmable read-only memory (Electrically EPROM, EEPROM) or flash memory. Volatile memory can be random access memory (Random Access Memory, RAM), static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDRSDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (Synch link DRAM, SLDRAM) and direct memory bus random access memory (Direct Rambus RAM, DRRAM). Memory 1409 in embodiments of the present application includes, but is not limited to, these and any other suitable types of memory.

The processor 1440 may include one or more processing units; optionally, the processor 1440 integrates an application processor and a modem processor, where the application processor mainly handles operations related to the operating system, user interface, application programs, etc., Modem processors mainly process wireless communication signals, such as baseband processors. It can be understood that the above modem processor may not be integrated into the processor 1440.

The terminal provided by the embodiment of the present application can implement each process implemented by the method embodiment of Figure 7 or Figure 8 and achieve the same technical effect. To avoid duplication, details will not be described here.

Please refer to Figure 15. Figure 15 is a structural diagram of a communication device applied in an embodiment of the present application. As shown in Figure 15, the communication device 1500 includes: a processor 1501, a transceiver 1502, a memory 1503 and a bus interface. The processor 1501 May be responsible for managing the bus architecture and general processing. Memory 1503 may store data used by processor 1501 in performing operations.

In one embodiment of the present application, the communication device 1500 further includes: a program stored in the memory 1503 and executable on the processor 1501. When the program is executed by the processor 1501, the steps in the method shown in Figure 7 or Figure 8 are implemented. .

In Figure 15, the bus architecture may include any number of interconnected buses and bridges, specifically linked together by various circuits of one or more processors represented by processor 1501 and memory represented by memory 1503. The bus architecture can also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are all well known in the art and therefore will not be described further herein. The bus interface provides the interface. Transceiver 1502 may be a plurality of elements, including a transmitter and a receiver, providing a unit for communicating with various other devices over a transmission medium.

Optionally, as shown in Figure 16, this embodiment of the present application also provides a communication device 1600, which includes a processor 1601 and a memory 1602. The memory 1602 stores programs or instructions that can be run on the processor 1601, such as , when the communication device 1600 is a terminal, the program or instruction is executed by the processor 1601 to implement each step of the method embodiment in FIG. 7 or FIG. 8 , and when the communication device 1600 is a network-side device, the program or instruction is executed by the processor 1601 When 1601 is executed, each step of the above method embodiment in Figure 7 or Figure 8 is implemented and the same technical effect can be achieved. To avoid duplication, the details will not be described here.

Embodiments of the present application also provide a readable storage medium, with programs or instructions stored on the readable storage medium. When the program or instructions are executed by a processor, the method in Figure 7 or Figure 8 and each process of the above embodiments are implemented. , and can achieve the same technical effect, so to avoid repetition, they will not be described again here.

Wherein, the processor is the processor in the terminal described in the above embodiment. The readable storage medium includes computer readable storage media, such as computer read-only memory ROM, random access memory RAM, magnetic disk or optical disk, etc.

An embodiment of the present application further provides a chip. The chip includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement what is shown in Figure 7 or Figure 8 and the above methods Each process of the embodiment can achieve the same technical effect, so to avoid repetition, it will not be described again here.

It should be understood that the chips mentioned in the embodiments of this application may also be called system-on-chip, system-on-a-chip, system-on-chip or system-on-chip, etc.

The embodiment of the present application further provides a computer program/program product, the computer program/program product is stored in a storage medium, and the computer program/program product is executed by at least one processor to implement what is shown in Figure 3 or Figure 4 Each process of each of the above method embodiments is shown and can achieve the same technical effect. To avoid repetition, it will not be described again here.

An embodiment of the present application further provides a communication system. The communication system includes a terminal and a network side device. The terminal is used to perform various processes in Figure 3 and the above method embodiments. The network side device is used to perform the following: The processes in Figure 4 and the above-mentioned method embodiments can achieve the same technical effect. To avoid repetition, they will not be described again here.

It should be noted that, in this document, the terms "comprising", "comprises" or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article or device that includes a series of elements not only includes those elements, It also includes other elements not expressly listed or inherent in the process, method, article or apparatus. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article or apparatus that includes that 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, but may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. Functions may be performed, for example, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. implementation. Based on this understanding, the technical solution of the present application can be embodied in the form of a computer software product that is essentially or contributes to related technologies. The computer software product is stored in a storage medium (such as ROM/RAM, disk, CD), including several instructions to cause a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of this application.

The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above-mentioned specific implementations. The above-mentioned specific implementations are only illustrative and not restrictive. Those of ordinary skill in the art will Inspired by this application, many forms can be made without departing from the purpose of this application and the scope protected by the claims, all of which fall within the protection of this application.

Claims (31)

1. A channel state information CSI prediction processing method, including:

   The first device receives the first information from the second device;

   The first device performs a first behavior based on the first information;

   Wherein, performing the first behavior includes determining whether to adjust a first prediction parameter, where the first prediction parameter is used for CSI prediction.
2. The method of claim 1, wherein performing the first behavior further includes: determining to start CSI prediction, or determining to prohibit CSI prediction, or determining to stop CSI prediction.
3. The method according to claim 1 or 2, wherein the method further includes:

   The first device sends second information to the second device, where the second information is used to indicate one of the following:

   The first device adjusts the first prediction parameter;

   The first device does not adjust the first prediction parameter;

   The second device starts CSI prediction;

   The second device disables CSI prediction;

   The second device stops CSI prediction.
4. The method of claim 1, further comprising:

   Before the first device receives the first information from the second device, the first device sends the first prediction parameter to the second device.
5. The method according to any one of claims 1 to 4, wherein the first information includes at least one of the following:

   Third information, the third information is used to indicate the feasibility of CSI prediction by the second device;

   second prediction parameters, the second prediction parameters include prediction parameters provided by the second device;

   First CSI, the first CSI is used to represent the CSI prediction result corresponding to the third prediction parameter;

   The third prediction parameter;

   second CSI, the second CSI is used to represent the actual CSI measurement result corresponding to the third prediction parameter;

   Fourth information, the fourth information is used to indicate the performance of CSI prediction.
6. The method of claim 5, wherein the third prediction parameter is the same as or different from the first prediction parameter and/or the second prediction parameter.
7. The method of claim 5, wherein the fourth information is determined by the second device based on the first CSI and the second CSI.
8. The method according to claim 5 or 7, wherein the fourth information includes at least one of the following: an error index and a precision index.
9. The method of claim 5, wherein the first device performs a first behavior based on the first information, including:

   In the case where the first information includes at least the first CSI and the second CSI, the first device according to The first CSI and the second CSI determine fifth information, and the fifth information is used to represent the performance of CSI prediction;

   The first device determines whether to adjust the first prediction parameter based on the fifth information;

   or,

   If the first information includes at least the fourth information, the first device determines whether to adjust the first prediction parameter based on the fourth information.
10. The method of claim 5, wherein the first device performs a first behavior based on the first information, further comprising:

    In the case where the first information includes at least the third information and a second prediction parameter, the first device determines whether the second device performs prediction according to the second prediction parameter;

    or,

    If the first information includes at least the first CSI, the first device determines whether to perform scheduling according to the first CSI.
11. The method according to claim 1, 3, 4, 5, 6, 9 or 10, wherein the first prediction parameter or the second prediction parameter or the third prediction parameter includes at least one of the following:

    CSI interval;

    Number of CSI;

    CSI window length;

    Forecast time information;

    Predicted frequency domain information;

    Predicted airspace information.
12. The method of claim 5, wherein the reference signal related to the second CSI includes at least one of the following:

    Periodic channel state information reference signal CSI-RS;

    Aperiodic CSI-RS;

    CSI-RS cluster;

    Verify the dedicated reference signal RS.
13. The method according to claim 1, wherein the first device is a network side device or terminal, and the second device is a network side device or terminal.
14. A CSI prediction processing method, including:

    The second device sends first information to the first device. The first information is used by the first device to perform a first behavior. The performing the first behavior includes determining and adjusting a first prediction parameter. The first prediction parameter is in CSI prediction.
15. The method of claim 14, wherein the first behavior further includes: determining to start CSI prediction, or determining to prohibit CSI prediction, or determining to stop CSI prediction.
16. The method according to claim 14 or 15, wherein the method further includes:

    The second device receives second information from the first device, the second information being used to indicate one of the following:

    The first device adjusts the first prediction parameter;

    The first device does not adjust the first prediction parameter;

    The second device starts CSI prediction;

    The second device disables CSI prediction;

    The second device stops CSI prediction.
17. The method of claim 14, further comprising:

    The second device receives the first prediction parameter from the first device before the second device sends the first information to the first device.
18. The method according to any one of claims 14 to 17, wherein the first information includes at least one of the following:

    Third information, the third information is used to indicate the feasibility of CSI prediction by the second device;

    second prediction parameters, the second prediction parameters include prediction parameters provided by the second device;

    First CSI, the first CSI is used to represent the CSI prediction result corresponding to the third prediction parameter;

    The third prediction parameter;

    second CSI, the second CSI is used to represent the actual CSI measurement result corresponding to the third prediction parameter;

    Fourth information, the fourth information is used to indicate the performance of CSI prediction.
19. The method of claim 18, wherein the method further includes:

    The second device determines the first CSI and/or the second CSI according to the third prediction parameter.
20. The method of claim 19, further comprising:

    The second device determines the fourth information based on the first CSI and the second CSI.
21. The method of claim 20, wherein the method further includes:

    The second device adjusts the first prediction parameter according to the fourth information to obtain an adjusted prediction parameter.
22. The method of claim 21, wherein the adjusted prediction parameter is the same as or different from the second prediction parameter and/or the third prediction parameter.
23. The method of claim 18, wherein the third prediction parameter is the same as or different from the first prediction parameter and/or the second prediction parameter.
24. The method according to claim 18, wherein the fourth information includes at least one of the following: an error index and a precision index.
25. The method according to claim 14, 16, 17, 18, 19, 21, 22 or 23, wherein the first prediction parameter or the second prediction parameter or the third prediction parameter includes at least one of the following:

    CSI interval;

    Number of CSI;

    CSI window length;

    Forecast time information;

    Predicted frequency domain information;

    Predicted airspace information.
26. The method of claim 18, wherein the reference signal related to the second CSI includes at least one of the following:

    periodic CSI-RS;

    Aperiodic CSI-RS;

    CSI-RS cluster;

    Verify dedicated RS.
27. The method according to claim 14, wherein the first device is a network side device or terminal, and the second device is a network side device or terminal.
28. A CSI prediction processing device, including:

    A first receiving module, configured to receive the first information from the second device;

    An execution module, configured to execute the first behavior according to the first information;

    Wherein, performing the first behavior includes determining whether to adjust a first prediction parameter, where the first prediction parameter is used for CSI prediction.
29. A CSI prediction processing device, including:

    The third sending module is configured to send first information to the first device. The first information is used by the first device to perform a first behavior. The performing the first behavior includes determining to adjust the first prediction parameter. One prediction parameter is used for CSI prediction.
30. A communication device, including a processor, a memory and a program or instructions stored on the memory and executable on the processor. When the program or instructions are executed by the processor, the implementation of claims 1 to 27 is achieved. The steps of any of the methods.
31. A readable storage medium on which a program or instructions are stored. When the program or instructions are executed by a processor, the steps of the method according to any one of claims 1 to 27 are implemented.