CN117639862A

CN117639862A - CSI prediction processing method, device, communication equipment and readable storage medium

Info

Publication number: CN117639862A
Application number: CN202210957898.6A
Authority: CN
Inventors: 孙布勒
Original assignee: Vivo Mobile Communication Co Ltd
Current assignee: Vivo Mobile Communication Co Ltd
Priority date: 2022-08-10
Filing date: 2022-08-10
Publication date: 2024-03-01
Also published as: WO2024032694A1

Abstract

The application discloses a CSI prediction processing method, a device, communication equipment and a readable storage medium, wherein the method comprises the following steps: the first device sends first information to the second device; wherein the first information includes at least one of: the second information comprises a comparison result of channel information at historical time and target channel information or target channel characteristics, and the second information is used for determining parameters and/or models for CSI prediction; parameters for CSI prediction; identification of a first AI model for CSI prediction.

Description

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

Technical Field

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

Background

The channel state information may describe a current channel environment, in a mobile communication network, a base station transmits a channel state information-Reference Signal (CSI-RS), a terminal evaluates the channel state information and quantitatively feeds back the channel state information to the base station, and the base station side can adjust in time when transmitting the channel state information Reference Signal by introducing the channel state information (Channel State Information, CSI) feedback information, thereby reducing an error rate at the terminal and obtaining an optimal receiving Signal.

In wireless communication, channel prediction can be used to compensate for the delay between the channel measurement and the actual scheduling, so as to improve throughput, and how to obtain the relevant information of the channel prediction is a problem to be solved.

Disclosure of Invention

The embodiment of the application provides a CSI prediction processing method, a device, communication equipment and a readable storage medium, which can solve the problem of acquiring relevant information of channel prediction.

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

the first device sends first information to the second device;

wherein the first information includes at least one of:

the second information comprises a comparison result of channel information at historical time and target channel information or target channel characteristics, and the second information is used for determining parameters and/or models for CSI prediction;

parameters for CSI prediction;

identification of a first AI model for CSI prediction.

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

the second device receives first information from the first device;

wherein the first information includes at least one of:

Parameters for CSI prediction;

identification of a first AI model for CSI prediction.

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

the first sending module is used for sending the first information to the second equipment;

wherein the first information includes at least one of: the second information comprises a comparison result of channel information at historical time and target channel information or target channel characteristics, and the second information is used for determining parameters and/or models for CSI prediction; parameters for CSI prediction; identification of a first AI model for CSI prediction.

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

a second receiving module for receiving first information from the first device;

In a fifth aspect, there is provided a communication device comprising: a processor, a memory and a program or instruction stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the method according to the first or second aspect.

In a sixth aspect, there is provided a readable storage medium having stored thereon a program or instructions which when executed by a processor implement the steps of the method according to the first or second aspect.

In a seventh aspect, there is provided a chip comprising a processor and a communication interface coupled to the processor for running a program or instructions implementing the steps of the method according to the first or second aspect.

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

A ninth aspect provides a communication system comprising a first device for performing the steps of the method as claimed in the first aspect and a second device for performing the steps of the method as claimed in the second aspect.

In the embodiment of the application, the first device may send the first information to the second device, the second device may determine the CSI prediction model or the CSI prediction parameter according to the first information, and the first device may also give the CSI prediction model or the CSI prediction parameter applicable to the actual channel without performing prediction verification, and reduce the overhead of model adjustment or model search by determining the CSI prediction parameter or the CSI prediction model in advance.

Drawings

FIG. 1 is a schematic diagram of a neural network;

FIG. 2 is a schematic diagram of a neuron;

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

FIG. 4 is a schematic diagram of predicting performance at different future times;

FIG. 5 is a schematic diagram of predicting future +5ms performance using different amounts of historical CSI;

fig. 6 is a schematic architecture diagram of a wireless communication system according to an embodiment of the present application;

fig. 7 is one of flowcharts of a CSI prediction processing method provided in an embodiment of the present application;

FIG. 8 is a second flowchart of a CSI prediction processing method according to an embodiment of the present application;

FIG. 9 is a third flowchart of a CSI prediction processing method according to an embodiment of the present application;

fig. 10 is a schematic diagram of a CSI prediction processing apparatus according to an embodiment of the present application;

FIG. 11 is a second schematic diagram of a CSI prediction processing apparatus according to an embodiment of the present application;

Fig. 12 is a schematic diagram of a terminal provided in an embodiment of the present application;

fig. 13 is a schematic diagram of a network side device provided in an embodiment of the present application;

fig. 14 is a schematic diagram of a communication device provided in an embodiment of the present application.

Detailed Description

Technical solutions in the embodiments of the present application will be clearly described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present application are within the scope of the protection of the present application.

The terms first, second and the like in the description and in the claims, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application are capable of operation in sequences other than those illustrated or otherwise described herein, and that the terms "first" and "second" are generally intended to be used in a generic sense and not to limit the number of objects, for example, the first object may be one or more. Furthermore, in the description and claims, "and/or" means at least one of the connected objects, and the character "/" generally means a relationship in which the associated object is an "or" before and after.

It is noted that the techniques described in embodiments of the present application are not limited to long term evolution (Long Term Evolution, LTE)/LTE evolution (LTE-Advanced, LTE-a) systems, but may also be used in other wireless communication systems, such as code division multiple access (Code Division Multiple Access, CDMA), time division multiple access (Time Division Multiple Access, TDMA), frequency division multiple access (Frequency Division Multiple Access, FDMA), orthogonal frequency division multiple access (Orthogonal Frequency Division Multiple Access, OFDMA), single carrier frequency division multiple access (Single-carrier Frequency Division Multiple Access, SC-FDMA), and other systems. The terms "system" and "network" in embodiments of the present application are often used interchangeably, and the techniques described may be used for both the above-mentioned systems and radio technologies, as well as other systems and radio technologies. The following description describes a New air interface (NR) system for purposes of example and uses NR terminology in much of the description that follows, but these techniques are also applicable to applications other than NR system applications, such as generation 6 (6) ^th Generation, 6G) communication system.

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

1. Introduction to neural networks

Artificial intelligence is currently in wide-spread use in various fields. Artificial intelligence (Artificial Intelligence, AI) modules have a variety of implementations, such as neural networks, decision trees, support vector machines, bayesian classifiers, and the like.

The present application is described by taking a neural network as an example, but the specific type of AI module is not limited, and the structure of the neural network is shown in fig. 1.

The neural network is composed of neurons, and a schematic diagram of the neurons is shown in fig. 2. Wherein a is ₁ ,a ₂ ,…a _K For 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 functions, tanh functions, modified linear units (Rectified Linear Unit, reLU), and the like.

The parameters of the neural network may be optimized by an optimization algorithm. An optimization algorithm is a class of algorithms that minimizes or maximizes an objective function (sometimes called a loss function). Whereas the objective function is often a mathematical combination of model parameters and data. For example, given data X and its corresponding label Y, a neural network model f (), with the model, a predicted output f (X) can be obtained from the input X, and the difference (f (X) -Y) between the predicted value and the actual value, which is the loss function, can be calculated. If a suitable W is found, b minimizes the value of the loss function described above, the smaller the loss value, the closer the model is to the real case.

The most common optimization algorithms are basically based on error back propagation (error Back Propagation, BP) algorithms. The basic idea of the BP algorithm is that the learning process consists of two processes, forward propagation of the signal and backward propagation of the error. In forward propagation, an input sample is transmitted from an input layer, is processed layer by each hidden layer, and is transmitted to an output layer. If the actual output of the output layer does not match the desired output, the back propagation phase of the error is shifted. The error back transmission is to make the output error pass through the hidden layer to the input layer by layer back transmission in a certain form, and to distribute the error to all the units of each layer, so as to obtain the error signal of each layer unit, which is the basis for correcting the weight of each unit. The process of adjusting the weights of the layers of forward propagation and error back propagation of the signal is performed repeatedly. The constant weight adjustment process is the learning training process of the network. This process is continued until the error in the network output is reduced to an acceptable level or until a preset number of learnings is performed.

In general, the AI algorithm chosen and the model employed will also vary depending on the type of solution. According to the related art, a main method of improving the performance of the fifth generation mobile communication technology (5th Generation,5G) network by means of AI is to enhance or replace the currently existing algorithms or processing modules by means of algorithms and models based on neural networks. In certain scenarios, neural network-based algorithms and models may achieve better performance than deterministic-based algorithms. More common neural networks include deep neural networks, convolutional neural networks, recurrent neural networks, and the like. By means of the existing AI tool, the construction, training and verification work of the neural network can be realized.

The system performance can be effectively improved by replacing modules in the existing system by an AI/Machine Learning (ML) method.

For example, channel state information (Channel State Information, CSI) prediction, historical CSI is input to an AI model, which analyzes time-domain variation characteristics of the channel and outputs future CSI. As particularly shown in fig. 3.

The corresponding system performance is shown in fig. 4 and 5. It can be seen that CSI prediction has a very large performance gain compared to the non-predicted scheme. Meanwhile, the predicted future time is different, and the achievable prediction accuracy is also different, as shown in fig. 4.

In addition, the number of historical CSI input to the AI model may also affect the performance of CSI prediction, and fig. 5 depicts the use of a different number of historical CSI to predict the performance of +5ms in the future. As can be seen, as the number of historical CSI increases, the prediction accuracy increases. However, a larger number of historical CSI means higher complexity and buffer overhead, and thus the number of historical CSI cannot be increased all at once.

In wireless communications, channel prediction may be used to compensate for the delay between channel measurement and actual scheduling, improving throughput. The accuracy of CSI prediction has a very large relationship with the prediction parameters. For a predictive model with insufficient generalization capability, different predictive models need to be used for different channels. In practice, the node performing the prediction does not have to store all models, but rather requests a new prediction model dynamically according to the actual demand. Even if the prediction node stores a prediction model applicable to the current channel, the overhead of model adjustment or model search can be reduced by estimating prediction parameters or prediction models in advance.

Fig. 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 device 62. The wireless communication system may be a communication system with a wireless AI function, such as a 5G evolution (5G-Advanced) communication system or a 6G communication system.

The terminal 61 may be a mobile phone, a tablet (Tablet Personal Computer), a Laptop (Laptop Computer) or a terminal-side Device called a notebook, a personal digital assistant (Personal Digital Assistant, PDA), a palm top, a netbook, an ultra-mobile personal Computer (ultra-mobile personal Computer, UMPC), a mobile internet appliance (Mobile Internet Device, MID), an augmented reality (augmented reality, AR)/Virtual Reality (VR) Device, a robot, a Wearable Device (weather Device), a vehicle-mounted Device (VUE), a pedestrian terminal (PUE), a smart home (home Device with a wireless communication function, such as a refrigerator, a television, a washing machine, or a furniture), a game machine, a personal Computer (personal Computer, PC), a teller machine, or a self-service machine, and the Wearable Device includes: intelligent wrist-watch, intelligent bracelet, intelligent earphone, intelligent glasses, intelligent ornament (intelligent bracelet, intelligent ring, intelligent necklace, intelligent anklet, intelligent foot chain etc.), intelligent wrist strap, intelligent clothing etc.. In addition to the above terminal device, the terminal related to the present application may also be a Chip in the terminal, such as a Modem (Modem) Chip, a System on Chip (SoC). Note that the specific type of the terminal 61 is not limited in the embodiment of the present application.

The network side device 62 may comprise an access network device or a core network device, wherein the access network device may also be referred to as a radio access network device, a radio access network (Radio Access Network, RAN), a radio access network function or a radio access network element. The access network device may include a base station, a WLAN access point, a WiFi node, or the like, where the base station may be referred to as a node B, an evolved node B (eNB), an access point, a base transceiver station (Base Transceiver Station, BTS), a radio base station, a radio transceiver, a basic service set (Basic Service Set, BSS), an extended service set (Extended Service Set, ESS), a home node B, a home evolved node B, a transmission receiving point (Transmitting Receiving Point, TRP), or some other suitable terminology in the field, and the base station is not limited to a specific technical vocabulary so long as the same technical effect is achieved, and it should be noted that in the embodiment of the present application, only the base station in the NR system is described by way of example, and the specific type of the base station is not limited.

The core network device may include, but is not limited to, at least one of: core network nodes, core network functions, mobility management entities (Mobility Management Entity, MME), access and mobility management functions (Access and Mobility Management Function, AMF), session management functions (Session Management Function, SMF), user plane functions (User Plane Function, UPF), policy control functions (Policy Control Function, PCF), policy and charging rules function units (Policy and Charging Rules Function, PCRF), edge application service discovery functions (Edge Application Server Discovery Function, EASDF), unified data management (Unified Data Management, UDM), unified data repository (Unified Data Repository, UDR), home subscriber server (Home Subscriber Server, HSS), centralized network configuration (Centralized network configuration, CNC), network storage functions (Network Repository Function, NRF), network opening functions (Network Exposure Function, NEF), local NEF (or L-NEF), binding support functions (Binding Support Function, BSF), application functions (Application Function, AF), and the like. In the embodiment of the present application, only the core network device in the NR system is described as an example, and the specific type of the core network device is not limited.

The AI model to which embodiments of the present application relate may also be referred to as an ML model.

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

Referring to fig. 7, an embodiment of the present application provides a CSI prediction processing method, which includes the following specific steps: step 701.

Step 701: the first device sends first information to the second device;

the first information may assist the second device in CSI prediction, that is, the second device may determine a model for CSI prediction or parameters for CSI prediction according to the first information.

The first device may include a communication device having wireless AI functionality.

Wherein the first information includes at least one of:

(1) The second information comprises a comparison result of channel information representing historical time and target channel information or target channel characteristics, and the second information is used for determining parameters and/or models for CSI prediction;

wherein, the channel information of the historical time may be referred to as historical CSI, the historical time is used to represent the time of the corresponding historical CSI, as shown in fig. 3, and the plurality of historical CSI includes: CSI t _{_K} ，……，CSI t _{_1} ，CSI t ₀ The corresponding historical time is t before the current time _{_K} ，t _{_1} ，t ₀ 。

Illustratively, the error, mean square error, normalized mean square error or cosine similarity between the channel information at the historical moment and the target channel information or the target channel characteristic is calculated to obtain a comparison result, and optionally, the comparison result can be represented by a score, wherein the higher the score is, the more similar the channel information at the historical moment is to the target channel information or the target channel characteristic.

The target channel information may also be referred to as typical channel information, and the target channel characteristics may also be referred to as typical channel characteristics.

(2) Parameters for CSI prediction;

optionally, the first device may determine parameters for CSI prediction according to the second information, that is, determine prediction parameters based on a comparison result of channel information at a historical time and target channel information or target channel characteristics, and may also give prediction parameters applicable to an actual channel without performing prediction verification, and reduce the overhead of AI model adjustment or AI model search by determining the prediction parameters in advance.

(3) Identification of a first AI model for CSI prediction.

Optionally, the first device may determine the identifier of the first AI model according to the second information, specifically, the first device selects the first AI model matched with the current environment according to the second information, and sends the identifier of the first AI model to the second device, that is, may determine the first AI model based on a comparison result of the channel information at the historical moment and the target channel information or the target channel characteristics, and may give the first AI model applicable to the actual channel without performing the prediction verification, and reduce the overhead of AI model adjustment or AI model searching by determining the first AI model in advance.

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

the first device receiving third information from the second device;

wherein the third information includes at least one of: (1) A second AI model for CSI prediction; (2) identification of the second AI model.

Optionally, the first device receives third information sent by the second device according to the first information, where the third information carries related information of the second AI model suggested by the second device, so that the second AI model can be synchronized between the first device and the second device. The first device may perform CSI prediction through the second AI model.

In one embodiment of the present application, the first AI model and the second AI model are the same model, or the first AI model and the second AI model are not the same model.

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

and the first equipment obtains the first information according to a third AI model.

In one embodiment of the present application, the input of the third AI model includes at least one of: and N pieces of channel information at historical moments, the target channel information or the target channel characteristics, wherein N is an integer greater than or equal to 1.

For example, the input of the third AI model, which may also include target channel information or channel characteristics corresponding to the second AI model or parameters for CSI prediction, includes channel information for at least one historical moment.

In one embodiment of the present application, the third AI model includes a model for channel alignment and/or a model for obtaining parameters for CSI prediction.

In one embodiment of the present application, the model comprises at least one of:

(1) Twin networks (Siamese networks);

such as twin networks based on binary cross entropy, contrast function or triplet loss;

(2) A contrast learning network (Contrastive Learning Network);

(3) Matching networks (Matching networks);

(4) A prototype network (Prototypical Network);

(5) A relational Network (relationship Network).

In one embodiment of the present application, the first AI model has a first correspondence with at least one of the target channel information or target channel characteristics.

Optionally, each first AI model has target channel information or target channel characteristics corresponding thereto, i.e., the first AI model corresponding thereto may be determined based on the target channel information or target channel.

In one embodiment of the present application, the parameter for CSI prediction corresponds to at least one target channel information or target channel characteristic with a second correspondence.

That is, parameters for CSI prediction corresponding thereto may be determined based on the target channel information or the target channel.

In one embodiment of the present application, the first correspondence or the second correspondence is agreed, or configured by the first device, or determined by negotiation between the first device and the second device, or configured by the second device for the first device.

For example, the first correspondence relationship or the second correspondence relationship may be predefined by a protocol, or may be negotiated by the first device and the second device through signaling interaction.

In one embodiment of the present application, the parameters for CSI prediction include at least one of:

(1) Predicting time information;

optionally, the predicted time information is used to represent a time position to be measured, such as, for example, future 1ms, future 2ms, etc., or future 1 slot (slot), future 2slot, etc.

(2) CSI interval;

optionally, the CSI interval is used to represent an interval between predicted multiple historical CSI.

The above CSI interval may also be referred to as a CSI period.

(3) Number of CSI;

optionally, the CSI number is used to represent the number of predicted multiple historical CSI.

(4) CSI window length;

optionally, the CSI window length is used to represent the predicted time domain length of the plurality of historical CSI occupancy.

(5) Predicted frequency domain information;

alternatively, the predicted frequency domain information may include: at least one of a number of physical resource blocks (Physical Resource Block, PRBs), a PRB position, a number of subbands, a subband position, and the like.

(6) Predicted spatial information.

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

In one embodiment of the present application, the first information and/or third information is carried on at least one of the following signaling or information:

(1) Layer 1 signaling of the physical uplink control channel (Physical Uplink Control Channel, PUCCH);

(2) MSG 1 of physical random access channel (Physical Random Access Channel, PRACH);

(3) MSG 3 of PRACH;

(4) MSG A of PRACH;

(5) Information of a physical uplink shared channel (Physical Uplink Shared Channel, PUSCH).

In this embodiment, the first device is a terminal, and the second device is a network device.

In another embodiment of the present application, the first information and/or third information is carried on at least one of the following signaling or information:

(1) A media access control (medium access control, MAC) Control Element (CE);

(2) A radio resource control (Radio Resource Control, RRC) message;

(3) Non-Access Stratum (NAS) messages;

(4) Managing the orchestration message;

(5) User plane data;

(6) Downlink control information (Downlink Control Information, DCI);

(7) System information blocks (System Information Block, SIB);

(8) Layer 1 signaling of the physical downlink control channel (Physical Downlink Control Channel, PDCCH);

(9) Information of a physical downlink shared channel (Physical Downlink Shared Channel, PDSCH);

(10) MSG 2 of PRACH;

(11) MSG 4 of PRACH;

(12) MSG B of PRACH.

In this embodiment, the first device is a network side device, and the second device is a terminal.

In yet another embodiment of the present application, the first information and/or third information is carried on at least one of the following signaling or information:

(1) Xn interface signaling;

(2) PC5 interface signaling;

(3) Information of the physical through link control channel (Physical Sidelink Control Channel, PSCCH);

(4) Information of the physical through link shared channel (Physical Sidelink Shared Channel, PSSCH);

(5) Information of the physical through link broadcast channel (Physical Sidelink Broadcast Channel, PSBCH);

(6) Information of a physical through link discovery channel (Physical Sidelink Discovery Channel, PSDCH);

(7) Information of the physical through link feedback channel (Physical Sidelink Feedback Channel, PSFCH).

In this embodiment, the first device is a first terminal, and the second device is a second terminal.

In yet another embodiment of the present application, the first information and/or third information is carried on at least one of the following signaling:

(1) S1 interface signaling;

(2) Xn interface signaling.

Such as X2 interface signaling.

In this embodiment, the first device is a first network side device, and the second device is a second network side device.

In the embodiment of the application, the first device may send the first information to the second device, and the second device may determine the CSI prediction model or the CSI prediction parameter according to the first information, so that the first device may provide the CSI prediction model or the CSI prediction parameter applicable to the actual channel without performing prediction verification, and reduce the overhead of AI model adjustment or AI model search by determining the CSI prediction parameter or the CSI prediction model in advance.

Referring to fig. 8, an embodiment of the present application provides a CSI prediction processing method, which is applied to a second device, where the second device may include a communication device with a wireless AI function, and specific steps include: step 801.

Step 801: the second device receives first information from the first device;

wherein the first information includes at least one of: (1) The second information comprises a comparison result of channel information at historical time and target channel information or target channel characteristics, and the second information is used for determining parameters and/or models for CSI prediction; (2) parameters for CSI prediction; (3) Identification of a first AI model for CSI prediction.

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

the second device sends third information to the first device;

In one embodiment of the present application, the first information is obtained by the first device according to a third AI model.

In one embodiment of the present application, the first AI model includes a model for channel alignment and/or a model for obtaining parameters of CSI prediction.

In one embodiment of the present application, the model comprises at least one of: twin networks, contrast learning networks, prototype networks, relational networks.

(1) Predicting time information;

(2) CSI interval;

(3) Number of CSI;

(4) CSI window length;

(5) Predicted frequency domain information;

(6) Predicted spatial information.

(1) Layer 1 signaling of PUCCH;

(2) MSG 1 of PRACH;

(3) MSG 3 of PRACH;

(4) MSG A of PRACH;

(5) Information of PUSCH.

(1)MAC CE；

(2) An RRC message;

(3) NAS messages;

(4) Managing the orchestration message;

(5) User plane data;

(6)DCI；

(7)SIB；

(8) Layer 1 signaling of PDCCH;

(9) Information of PDSCH;

(10) MSG 2 of PRACH;

(11) MSG 4 of PRACH;

(12) MSG B of PRACH.

(1) Xn interface signaling;

(2) PC5 interface signaling;

(3) Information of the PSCCH;

(4) Information of PSSCH;

(5) Information of PSBCH;

(6) Information of PSDCH;

(7) Information of the PSFCH.

(1) S1 interface signaling;

(2) Xn interface signaling.

In the embodiment of the application, the second device may determine the CSI prediction model or the CSI prediction parameter according to the first information from the first device, so that the first device may provide the CSI prediction model or the CSI prediction parameter applicable to the actual channel without performing prediction verification, and reduce the overhead of AI model adjustment or AI model search by determining the CSI prediction parameter or the CSI prediction model in advance.

In order to facilitate an understanding of the embodiments of the present application, the following description is provided in connection with example one.

Example 1

Referring to fig. 9, the specific steps are as follows:

step 1: the first equipment compares the channel information at the historical moment with the target channel information or the target channel characteristics through a third AI model to obtain a comparison result;

Step 2: the first device sends first information to the second device according to the comparison result;

step 3: the second equipment determines a second AI model according to the first information;

step 4: the second device sends third information to the first device, wherein the third information carries related information of a second AI model;

step 5: the first device performs CSI prediction according to the second AI model.

In the embodiment of the application, the first device may send the first information to the second device, the second device may determine the CSI prediction model or the CSI prediction parameter according to the first information, and the first device may also give the CSI prediction model or the CSI prediction parameter applicable to the actual channel without performing prediction verification, and reduce the overhead of AI model adjustment or AI model search by determining the CSI prediction parameter or the CSI prediction model in advance.

Referring to fig. 10, an embodiment of the present application provides a CSI prediction processing apparatus, which is applied to a first device, where the first device may include a communication device with a wireless AI function, and an apparatus 1000 includes:

a first sending module 1001, configured to send first information to a second device;

wherein the first information includes at least one of:

(1) The second information comprises a comparison result of channel information at historical time and target channel information or target channel characteristics, and the second information is used for determining parameters and/or models for CSI prediction;

(2) Parameters for CSI prediction;

(3) Identification of a first AI model for CSI prediction.

In one embodiment of the present application, the apparatus further comprises:

a first receiving module for receiving third information from the second device;

In one embodiment of the present application, the apparatus further comprises:

and the processing module is used for obtaining the first information according to the third AI model.

In one embodiment of the present application, the third AI model includes a model for channel alignment and/or a model for obtaining the parameters for CSI prediction.

In one embodiment of the present application, the model comprises at least one of: twin network, contrast learning network, matching network, prototype network, relationship network.

(1) Predicting time information;

(2) CSI interval;

(3) Number of CSI;

(4) CSI window length;

(5) Predicted frequency domain information;

(6) Predicted spatial information.

(1) Layer 1 signaling of PUCCH;

(2) MSG 1 of PRACH;

(3) MSG 3 of PRACH;

(4) MSG A of PRACH;

(5) Information of PUSCH.

(1)MAC CE；

(2) An RRC message;

(3) NAS messages;

(4) Managing the orchestration message;

(5) User plane data;

(6) DCI information;

(7)SIB；

(8) Layer 1 signaling of PDCCH;

(9) Information of PDSCH;

(10) MSG 2 of PRACH;

(11) MSG 4 of PRACH;

(12) MSG B of PRACH.

(1) Xn interface signaling;

(2) PC5 interface signaling;

(3) Information of the PSCCH;

(4) Information of PSSCH;

(5) Information of PSBCH;

(6) Information of PSDCH;

(7) Information of the PSFCH.

(1) S1 interface signaling;

(2) Xn interface signaling.

The device provided in this embodiment of the present application can implement each process implemented by the method embodiment of fig. 7, and achieve the same technical effects, so that repetition is avoided, and details are not repeated here.

Referring to fig. 11, an embodiment of the present application provides a CSI prediction processing apparatus applied to a second device including a communication device having a wireless AI function, and an apparatus 1100 includes:

a second receiving module 1101 for receiving first information from a first device;

In one embodiment of the present application, the apparatus further comprises:

the second sending module is used for sending third information to the first equipment;

(1) Predicting time information;

(2) CSI interval;

(3) Number of CSI;

(4) CSI window length;

(5) Predicted frequency domain information;

(6) Predicted spatial information.

(1) Layer 1 signaling of PUCCH;

(2) MSG 1 of PRACH;

(3) MSG 3 of PRACH;

(4) MSG A of PRACH;

(5) Information of PUSCH.

(1)MAC CE；

(2) An RRC message;

(3) NAS messages;

(4) Managing the orchestration message;

(5) User plane data;

(6) DCI information;

(7)SIB；

(8) Layer 1 signaling of PDCCH;

(9) Information of PDSCH;

(10) MSG 2 of PRACH;

(11) MSG 4 of PRACH;

(12) MSG B of PRACH.

(1) Xn interface signaling;

(2) PC5 interface signaling;

(3) Information of the PSCCH;

(4) Information of PSSCH;

(5) Information of PSBCH;

(6) Information of PSDCH;

(7) Information of the PSFCH.

(1) S1 interface signaling;

(2) Xn interface signaling.

The device provided in this embodiment of the present application can implement each process implemented by the method embodiment of fig. 8, and achieve the same technical effects, so that repetition is avoided, and details are not repeated here.

Fig. 12 is a schematic hardware structure of a terminal implementing an embodiment of the present application. The terminal 1200 includes, but is not limited to: at least some of the components of the radio frequency unit 1201, the network module 1202, the audio output unit 1203, the input unit 1204, the sensor 1205, the display unit 1206, the user input unit 1207, the interface unit 1208, the memory 1209, and the processor 1220.

Those skilled in the art will appreciate that the terminal 1200 may further include a power source (e.g., a battery) for supplying power to the respective components, and the power source may be logically connected to the processor 1220 through a power management system, so as to perform functions of managing charge, discharge, power consumption management, etc. through the power management system. The terminal structure shown in fig. 12 does not constitute a limitation of the terminal, and the terminal may include more or less components than shown, or may combine certain components, or may be arranged in different components, which will not be described in detail herein.

It should be understood that in the embodiment of the present application, the input unit 1204 may include a graphics processing unit (Graphics Processing Unit, GPU) 12041 and a microphone 12042, and the graphics processor 12041 processes image data of still pictures or videos obtained by an image capturing device (such as a camera) in a video capturing mode or an image capturing mode. The display unit 1206 may include a display panel 12061, and the display panel 12061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 1207 includes at least one of a touch panel 12071 and other input devices 12072. The touch panel 12071 is also called a touch screen. The touch panel 12071 may include two parts, a touch detection device and a touch controller. Other input devices 12072 may include, but are not limited to, a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and so forth, which are not described in detail herein.

In this embodiment, after receiving downlink data from the network side device, the radio frequency unit 1201 may transmit the downlink data to the processor 1220 for processing; in addition, the radio frequency unit 1201 may send uplink data to the network side device. Typically, the radio frequency unit 1201 includes, but is not limited to, an antenna, an amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.

Memory 1209 may be used to store software programs or instructions as well as various data. The memory 1209 may mainly include a first memory area storing programs or instructions and a second memory area storing data, wherein the first memory area may store an operating system, application programs or instructions (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like. Further, the memory 1209 may include volatile memory or nonvolatile memory, or the memory 1209 may include both volatile and nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM), static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (ddr SDRAM), enhanced SDRAM (Enhanced SDRAM), synchronous DRAM (SLDRAM), and Direct RAM (DRRAM). Memory 1209 in embodiments of the present application includes, but is not limited to, these and any other suitable types of memory.

Processor 1220 may include one or more processing units; optionally, processor 1220 integrates an application processor that primarily processes operations involving an operating system, user interface, application programs, and the like, and a modem processor that primarily processes wireless communication signals, such as a baseband processor. It will be appreciated that the modem processor described above may not be integrated into processor 1220.

The terminal provided in this embodiment of the present application can implement each process implemented by the method embodiment of fig. 7 or fig. 8, and achieve the same technical effects, so that repetition is avoided, and no further description is given here.

Referring to fig. 13, fig. 13 is a block diagram of a communication device to which an embodiment of the present invention is applied, and as shown in fig. 13, a communication device 1300 includes: processor 1301, transceiver 1302, memory 1303 and bus interfaces, wherein processor 501 may be responsible for managing the bus architecture and general processing. The memory 1303 may store data used by the processor 1301 in performing operations.

In one embodiment of the present invention, the communications device 1300 further comprises: a program stored in the memory 1203 and executable on the processor 1301, which when executed by the processor 1301 performs the steps in the method shown in fig. 7 or 8 above.

In fig. 13, a bus architecture may comprise any number of interconnected buses and bridges, with one or more processors, represented by processor 1301, and various circuits of memory, represented by memory 1303, linked together. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., which are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. The transceiver 1302 may be a number of elements, i.e., including a transmitter and a receiver, providing a means for communicating with various other apparatus over a transmission medium.

Optionally, as shown in fig. 14, the embodiment of the present application further provides a communication device 1400, including a processor 1401 and a memory 1402, where the memory 1402 stores a program or instructions that can be executed on the processor 1401, for example, when the communication device 1400 is a terminal, the program or instructions implement, when executed by the processor 1401, the steps of the method embodiment of fig. 7 or fig. 8, and when the communication device 1400 is a network-side device, the program or instructions implement, when executed by the processor 1401, the steps of the method embodiment of fig. 8 or fig. 7, and achieve the same technical effects, and are not repeated herein.

The embodiment of the present application further provides a readable storage medium, where a program or an instruction is stored, and when the program or the instruction is executed by a processor, the method of fig. 7 or fig. 8 and each process of each embodiment described above are implemented, and the same technical effects can be achieved, so that repetition is avoided, and no further description is given here.

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

The embodiment of the application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled with the processor, and the processor is configured to run a program or instructions, implement each process of each method embodiment shown in fig. 7 or fig. 8 and described above, and achieve the same technical effect, so that repetition is avoided, and no further description is provided here.

It should be understood that the chips referred to in the embodiments of the present application may also be referred to as system-on-chip chips, or the like.

The embodiments of the present application further provide a computer program/program product, where 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 the respective processes shown in fig. 7 or fig. 8 and described above in the respective method embodiments, and achieve the same technical effects, so that repetition is avoided, and details are not repeated here.

The embodiment of the present application further provides a communication system, where the communication system includes a terminal and a network side device, where the terminal is configured to execute each process of the embodiments of the method shown in fig. 7 or fig. 8 and the embodiments of the method described above, and the network side device is configured to execute each process of the embodiments of the method shown in fig. 8 or fig. 7 and the embodiments of the method described above, and achieve the same technical effects, so that repetition is avoided and redundant description is omitted here.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. Furthermore, it should be noted that the scope of the methods and apparatus in the embodiments of the present application is not limited to performing the functions in the order shown or discussed, but may also include performing the functions in a substantially simultaneous manner or in an opposite order depending on the functions involved, e.g., the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solutions of the present application may be embodied essentially or in a part contributing to the prior art in the form of a computer software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present application.

The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those of ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are also within the protection of the present application.

Claims

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

the first device sends first information to the second device;

wherein the first information includes at least one of:

parameters for CSI prediction;

identification of a first artificial intelligence AI model, the first AI model for CSI prediction.

2. The method according to claim 1, wherein the method further comprises:

the first device receiving third information from the second device;

wherein the third information includes at least one of:

a second AI model for CSI prediction;

identification of the second AI model.

3. The method of claim 2, wherein the first AI model and the second AI model are the same model or the first AI model and the second AI model are not the same model.

4. The method according to claim 1, wherein the method further comprises:

5. The method of claim 4, wherein the input of the third AI model comprises at least one of: and N pieces of channel information at historical moments, the target channel information or the target channel characteristics, wherein N is an integer greater than or equal to 1.

6. The method according to claim 4 or 5, characterized in that the third AI model comprises a model for channel alignment and/or a model for obtaining parameters for CSI prediction.

7. The method of claim 6, wherein the model comprises at least one of: twin network, contrast learning network, matching network, prototype network, relationship network.

8. The method of claim 1, wherein the first AI model has a first correspondence with at least one of the target channel information or target channel characteristics.

9. The method of claim 1, wherein the parameter for CSI prediction corresponds to at least one of target channel information or target channel characteristics with a second correspondence.

10. The method according to claim 8 or 9, wherein the first correspondence or the second correspondence is agreed upon, or configured by the first device, or determined by negotiation of the first device and the second device, or configured by the second device for the first device.

11. The method of claim 1, wherein the parameters for CSI prediction comprise at least one of:

predicting time information;

CSI interval;

number of CSI;

CSI window length;

predicted frequency domain information;

predicted spatial information.

12. The method according to claim 1 or 2, characterized in that the first information and/or third information is carried on at least one of the following signaling or information:

layer 1 signaling of physical uplink control channel PUCCH;

MSG 1 of physical random access channel PRACH;

MSG 3 of PRACH;

MSG A of PRACH;

information of a physical uplink shared channel PUSCH.

13. The method of claim 12, wherein the first device is a terminal and the second device is a network-side device.

14. The method according to claim 1 or 2, characterized in that the first information and/or third information is carried on at least one of the following signaling or information:

a medium access control element (MAC CE);

a radio resource control, RRC, message;

non-access stratum NAS messages;

managing the orchestration message;

user plane data;

downlink control information DCI;

a system information block SIB;

layer 1 signaling of a physical downlink control channel PDCCH;

Information of a Physical Downlink Shared Channel (PDSCH);

MSG 2 of PRACH;

MSG 4 of PRACH;

MSG B of PRACH.

15. The method of claim 14, wherein the first device is a network-side device and the second device is a terminal.

16. The method according to claim 1 or 2, characterized in that the first information and/or third information is carried on at least one of the following signaling or information:

xn interface signaling;

PC5 interface signaling;

information of a physical direct link control channel PSCCH;

information of a physical through link shared channel PSSCH;

information of a physical through link broadcast channel PSBCH;

physical through link discovery channel PSDCH information;

the physical through link feeds back the information of the channel PSFCH.

17. The method of claim 16, wherein the first device is a first terminal and the second device is a second terminal.

18. Method according to claim 1 or 2, characterized in that the first information and/or third information is carried in at least one of the following signaling:

s1 interface signaling;

xn interface signaling.

19. The method of claim 18, wherein the first device is a first network-side device and the second device is a second network-side device.

20. A CSI prediction processing method, comprising:

the second device receives first information from the first device;

wherein the first information includes at least one of:

parameters for CSI prediction;

identification of a first AI model for CSI prediction.

21. The method of claim 20, wherein the method further comprises:

the second device sends third information to the first device;

wherein the third information includes at least one of:

a second AI model for CSI prediction;

identification of the second AI model.

22. The method of claim 21, wherein the first AI model and the second AI model are the same model or the first AI model and the second AI model are not the same model.

23. The method of claim 20, wherein the first information is derived by the first device according to a third AI model.

24. The method of claim 23, wherein the input of the third AI model comprises at least one of: and N pieces of channel information at historical moments, the target channel information or the target channel characteristics, wherein N is an integer greater than or equal to 1.

25. The method of claim 23 or 24, wherein the first AI model comprises a model for channel alignment and/or a model for obtaining parameters of CSI prediction.

26. The method of claim 25, wherein the model comprises at least one of: twin networks, contrast learning networks, prototype networks, relational networks.

27. The method of claim 20, wherein the first AI model has a first correspondence with at least one of the target channel information or target channel characteristics.

28. The method of claim 20, wherein the parameter for CSI prediction corresponds to at least one of target channel information or target channel characteristics with a second correspondence.

29. The method of claim 27 or 28, wherein the first correspondence or the second correspondence is agreed upon, or configured by the first device, or determined by negotiation of the first device and the second device, or configured by the second device for the first device.

30. The method of claim 20, wherein the parameters for CSI prediction comprise at least one of:

predicting time information;

CSI interval;

number of CSI;

CSI window length;

predicted frequency domain information;

predicted spatial information.

31. The method according to claim 20 or 21, characterized in that the first information and/or third information is carried on at least one of the following signaling or information:

layer 1 signaling of PUCCH;

MSG 1 of PRACH;

MSG 3 of PRACH;

MSG A of PRACH;

information of PUSCH.

32. The method of claim 31, wherein the first device is a terminal and the second device is a network-side device.

33. The method according to claim 20 or 21, characterized in that the first information and/or third information is carried on at least one of the following signaling or information:

MAC CE；

an RRC message;

NAS messages;

managing the orchestration message;

user plane data;

DCI；

SIB；

layer 1 signaling of PDCCH;

information of PDSCH;

MSG 2 of PRACH;

MSG 4 of PRACH;

MSG B of PRACH.

34. The method of claim 33, wherein the first device is a network-side device and the second device is a terminal.

35. The method according to claim 20 or 21, characterized in that the first information and/or third information is carried on at least one of the following signaling or information:

xn interface signaling;

PC5 interface signaling;

information of the PSCCH;

information of PSSCH;

information of PSBCH;

information of PSDCH;

information of the PSFCH.

36. The method of claim 35, wherein the first device is a first terminal and the second device is a second terminal.

37. The method according to claim 20 or 21, characterized in that the first information and/or third information is carried on at least one of the following signaling or information:

s1 interface signaling;

xn interface signaling.

38. The method of claim 37, wherein the first device is a first network-side device and the second device is a second network-side device.

39. A CSI prediction processing apparatus, comprising:

40. A CSI prediction processing apparatus, comprising:

41. A communication device comprising a processor, a memory and a program or instruction stored on the memory and executable on the processor, which when executed by the processor implements the steps of the method of any one of claims 1 to 38.

42. A readable storage medium, characterized in that it has stored thereon a program or instructions which, when executed by a processor, implement the steps of the method according to any of claims 1 to 38.