WO2023072239A1

WO2023072239A1 - Channel prediction method and apparatus, network side device, and terminal

Info

Publication number: WO2023072239A1
Application number: PCT/CN2022/128214
Authority: WO
Inventors: 任千尧; 杨昂
Original assignee: 维沃移动通信有限公司
Priority date: 2021-11-01
Filing date: 2022-10-28
Publication date: 2023-05-04
Also published as: CN116074210A

Abstract

The present application relate to the field of mobile communications. Disclosed are a channel prediction method and apparatus, a network side device, and a terminal. The channel prediction method in the embodiments of the present application comprises: a network side device acquires channel prediction information from a terminal; the network side device selects channel prediction configuration according to the channel prediction information, wherein the channel prediction configuration comprises configuration of an artificial intelligence network for performing channel prediction.

Description

Channel prediction method, device, network side equipment and terminal

Cross References to Related Applications

This application claims priority to Chinese Patent Application No. 202111285185.1 filed on November 01, 2021, the entire contents of which are hereby incorporated by reference.

technical field

The present application belongs to the technical field of mobile communication, and specifically relates to a channel prediction method, device, network side equipment and terminal.

Background technique

In downlink communication, the base station needs to obtain as accurate channel information as possible, such as channel state information (Channel State Information, CSI), so as to perform reasonable precoding construction and multi-user scheduling. In high-speed scenarios, the channel changes quickly, and the conventional CSI reporting period is not enough to meet the high-speed channel changes. The base station needs to predict the channel state after a certain period of time based on the obtained CSI information, so as to perform precoding calculation and multi-user in real time. scheduling.

As artificial intelligence (AI) has been widely used in various fields, AI is also used for channel prediction. Wherein, the AI module has multiple implementation methods, such as neural network, decision tree, support vector machine, Bayesian classifier and so on.

However, when channel prediction is based on AI, when the channel conditions are relatively good, the amount of calculation may be wasted and the CSI overhead will be increased. When the channel conditions are relatively poor, the prediction accuracy will be affected.

Contents of the invention

The embodiment of the present application provides a channel prediction method, device, network side equipment and terminal, which can solve the problem that when the channel condition is relatively good, the calculation amount may be wasted and the CSI cost (overhead) will be increased; when the channel condition is relatively poor, Problems where prediction accuracy can be affected.

In the first aspect, a channel prediction method is provided, which is applied to a network side device, including:

The network side device acquires channel prediction information from the terminal;

The network side device determines a configuration for performing channel prediction according to the channel prediction information; wherein the channel prediction configuration includes a configuration of an artificial intelligence network for performing channel prediction.

In a second aspect, a channel prediction device is provided, including:

A transceiver module, configured to obtain channel prediction information from the terminal;

A configuration module, configured to determine a configuration for channel prediction according to the channel prediction information; wherein, the channel prediction configuration includes a configuration of an artificial intelligence network for performing channel prediction.

In the third aspect, a channel prediction method is provided, which is applied to a terminal, including:

The terminal determines channel prediction information;

The terminal reports the channel prediction information to the network side device, and the channel prediction information is used to determine the channel prediction configuration of the network side device; wherein, the channel prediction configuration includes artificial intelligence for performing channel prediction Network configuration.

In a fourth aspect, a channel prediction device is provided, including:

A calculation module, configured to determine channel prediction information;

A reporting module, configured to report the channel prediction information to the network side device, where the channel prediction information is used to determine the channel prediction configuration of the network side device; wherein, the channel prediction configuration includes a channel prediction configuration Configuration of artificial intelligence network.

According to the fifth aspect, a network-side device is provided, the network-side device includes a processor, a memory, and a program or instruction stored in the memory and operable on the processor, the program or instruction being executed by the When executed by the processor, the steps of the method described in the first aspect are realized.

According to a sixth aspect, a terminal is provided. The terminal includes a processor, a memory, and a program or instruction stored in the memory and operable on the processor. When the program or instruction is executed by the processor The steps of the method as described in the third aspect are realized.

In the seventh aspect, a readable storage medium is provided, and programs or instructions are stored on the readable storage medium, and when the programs or instructions are executed by a processor, the steps of the method described in the first aspect are realized, or the steps of the method described in the first aspect are realized, or The steps of the method described in the third aspect.

In an eighth aspect, a chip is provided, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the method as described in the first aspect , or implement the method described in the third aspect.

A ninth aspect provides a computer program/program product, the computer program/program product is stored in a non-transitory storage medium, the program/program product is executed by at least one processor to implement the first aspect The method, or the steps to implement the method as described in the third aspect.

In this embodiment of the present application, the channel prediction information from the terminal is obtained through the network side equipment; the network side equipment determines the configuration for channel prediction according to the channel prediction information; wherein, the configuration of the channel prediction includes the configuration for performing The configuration of the artificial intelligence network for channel prediction, so that the AI network can be reasonably configured or switched, and a more suitable AI network is used for channel prediction. When the channel changes rapidly, the complex network is used to track the channel and improve the accuracy of channel prediction. When the channel changes slowly, use a relatively simple network for channel prediction, reduce the complexity of the network, and also reduce the RS overhead and CSI overhead.

Description of drawings

FIG. 1 shows a schematic structural diagram of a wireless communication system to which an embodiment of the present application is applicable;

FIG. 2 shows a schematic flow chart of a channel prediction method in an embodiment of the present application;

FIG. 3 shows another schematic flowchart of a channel prediction method in an embodiment of the present application;

FIG. 4 shows a schematic structural diagram of a channel prediction device according to an embodiment of the present application;

FIG. 5 shows another schematic flowchart of a channel prediction method in an embodiment of the present application;

FIG. 6 shows a schematic structural diagram of a channel prediction device according to an embodiment of the present application;

FIG. 7 shows a schematic structural diagram of a communication device provided by an embodiment of the present application;

FIG. 8 is a schematic structural diagram of a network-side device implementing an embodiment of the present application;

FIG. 9 is a schematic structural diagram of a terminal implementing an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application will be clearly described below in conjunction with the 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 them. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in this application belong to the protection scope of this application.

The terms "first", "second" and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific sequence or sequence. 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 described herein and that "first" and "second" distinguish objects. It is usually one category, and the number of objects is not limited. For example, there may be one or more first objects. In addition, "and/or" in the description and claims means at least one of the connected objects, and the character "/" generally means that the related objects are an "or" relationship.

It is worth noting that the technology described in the embodiment of this application is not limited to the Long Term Evolution (Long Term Evolution, LTE)/LTE-Advanced (LTE-Advanced, LTE-A) system, 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 (Single-carrier Frequency-Division Multiple Access, SC-FDMA) and other systems. The terms "system" and "network" in the embodiments of the present application are often used interchangeably, and the described technology can be used for the above-mentioned system and radio technology, and can also be used for other systems and radio technologies. The following description describes the New Radio (NR) system for illustrative purposes, and uses NR terminology in most of the following descriptions, but these techniques can also be applied to applications other than NR system applications, such as the 6th generation (6 ^th Generation, 6G) communication system.

Fig. 1 shows a schematic structural diagram of a wireless communication system to which this embodiment of the present application is applicable. The wireless communication system includes a terminal 11 and a network side device 12 . Wherein, the terminal 11 can also be called a terminal device or a user terminal (User Equipment, UE), and the terminal 11 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), handheld computer, netbook, ultra-mobile personal computer (UMPC), mobile Internet device (Mobile Internet Device, MID), wearable device (Wearable Device) or vehicle-mounted device (Vehicle User Equipment, VUE), pedestrian terminal (Pedestrian User Equipment, PUE) and other terminal-side equipment, wearable devices include: smart watches, bracelets, earphones, glasses, etc. It should be noted that, the embodiment of the present application does not limit the specific type of the terminal 11 . The network side device 12 may be a base station or a core network, where a base station may be called a node B, an evolved node B, an access point, a base transceiver station (Base Transceiver Station, BTS), a radio base station, a radio transceiver, a basic service Basic Service Set (BSS), Extended Service Set (ESS), Node B, Evolved Node B (eNB), Home Node B, Home Evolved Node B, Wireless Local Area Network (WLAN) ) access point, wireless fidelity (Wireless Fidelity, WiFi) node, transmitting 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 the present application, only the base station in the NR system is taken as an example, but the specific type of the base station is not limited.

The channel prediction method provided by the embodiment of the present application will be described in detail below through some embodiments and application scenarios with reference to the accompanying drawings.

As shown in FIG. 2 , the embodiment of the present application provides a channel prediction method, which can be executed by a network-side device. In other words, the method can be executed by software or hardware installed on the network-side device. The execution steps of the method are as follows.

Step S210, the network side device acquires channel prediction information from the terminal;

The terminal may determine the channel prediction information according to actual requirements. In one embodiment, the terminal may determine the channel prediction information based on the agreement, or determine the channel prediction information according to the period configured on the network side.

In one embodiment, the terminal determining the channel prediction information includes performing channel estimation, receiving a channel state information reference signal (CSI Reference Signal, CSI-RS) at several time domain positions, performing channel estimation, and obtaining the downlink channel _H. _. _{_} _{_} _{_} _{_} _{_} _{_} _{_} is the channel estimation result in different frequency domains at time i, and M is the number of CSI-RSs in the frequency domain.

The channel prediction information can be varied. In one embodiment, the channel prediction information includes at least one of the following:

channel change rate;

The level of the channel change rate, the level of the channel change rate may be obtained by quantizing the channel change rate.

It should be understood that the channel prediction information may also be other expression information used to indicate channel change status.

Wherein, the channel change rate includes at least one of the following:

the rate of change of the temporal correlation;

the rate of change of the magnitude of a particular diameter;

the rate of change of the phase of the specified diameter;

The rate of change of the time delay of the specific path;

the rate of change of the amplitude of a particular port;

the rate of change of the phase of the particular port;

the moving speed of the terminal;

the rotational speed of the terminal;

The rate of change of the beam.

Further, the specific diameter includes at least one of the following:

diameter with maximum power;

Several diameters with maximum power;

The path where power is concentrated in the line of sight (Line of Sight, LOS) propagation direction;

Paths whose power exceeds the first threshold.

The calculation method for the terminal to determine the time-domain correlation can be determined by the terminal or the network-side device according to the actual situation. For example, the time-domain correlation of two channels with a time domain interval of k can be expressed as:

There are various methods for the terminal to determine the change rate of the time-domain correlation. In one embodiment, if the terminal calculates the time-domain correlation at time t ₀ as K ₀ =[k ₀ ,k ₁ ,...k _{N -1} ], and then calculate the time-domain correlation K ₁ at the next time t ₁ , then the terminal will make statistics on the time-domain correlation changes at each adjacent time, and calculate sum(|K ₁ -K ₀ |) as the time-domain correlation The change rate is reported directly or quantified and reported to the network side device.

In another implementation manner, the rate of change of the time-domain correlation may include a rate of change of a target parameter of the U-shaped spectrum. The terminal can calculate the discrete Fourier transform (Discrete Fourier Transform, DFT) change of the time-domain correlation K ₀ and K ₁ , that is, the U-shaped spectrum in the frequency domain, and use the change rate of the 3dB width of the U-shaped spectrum as the time-domain correlation The rate of change is directly reported or quantified and reported to the network side device.

In one embodiment, the terminal can determine the rate of change of the amplitude or phase of the specific path or specific port of the channel within a period of time by performing channel estimation on a specific path or specific port, and report it directly or after quantization to the network side device .

In one embodiment, the terminal estimates the channel of the same specific port within several symbols, obtains the time delay position of the specific path searched in the time domain channel, and calculates the time domain position of the specific path over time The rate of change is directly reported or quantified and reported to the network side device.

In one embodiment, the rate of change of the beam may include the rate of change of a target parameter of a specific beam; wherein, the target parameter may include Reference Signal Received Power (Reference Signal Received Power, RSRP), Reference Signal Received Quality ( Reference Signal Received Quality, RSRQ), Signal to Interference plus Noise Ratio (Signal to Interference plus Noise Ratio, SINR), etc. The terminal tracks the channel quality of the same specific beam during the beam switching period, calculates the rate of change of information such as RSRP or RSRQ, and directly reports or quantifies and reports to the network side device.

In another implementation manner, the beam change rate may include a beam switching rate. The terminal calculates the number of times of beam switching within a long period of time, thereby determining the rate of beam switching, and reports it directly or quantifies it to the network side device.

Further, the specific beam includes at least one of the following:

The beam corresponding to the smallest control resource set ID (Control resource set ID, CORESET ID);

Control the beam corresponding to resource set CORESET 0;

The beam indicated by the network side device or base station.

Step S220, the network side device determines a channel prediction configuration according to the channel prediction information; wherein, the channel prediction configuration includes a configuration of an AI network for performing channel prediction. The network side device performs channel prediction through the configured AI network according to the determined configuration.

The network-side device finds the configuration of the corresponding AI network and other parameter configurations according to the channel prediction information reported by the terminal. In one embodiment, the configuration of the channel prediction includes at least one of the following:

the structure of the AI network;

parameters of the AI network;

input data for said AI network;

The time span that the AI network can predict;

RS configuration;

CSI configuration;

configuration of reporting of the CSI;

Period of reporting CSI;

The complexity of the non-AI prediction algorithm, which may include: number of iterations, polynomial exponent, etc.

It can be seen from the technical solutions of the above-mentioned embodiments that in the embodiment of the present application, the channel prediction information from the terminal is obtained through the network-side device; the network-side device determines the configuration for channel prediction according to the channel prediction information; wherein, the channel prediction The configuration includes the configuration of the artificial intelligence network for performing channel prediction, so that the AI network can be reasonably configured or switched, and a more suitable AI network is used for channel prediction. When the channel changes rapidly, the complex network is used to track the channel. Improve the accuracy of channel prediction. When the channel changes slowly, use a relatively simple network for channel prediction, reduce the complexity of the network, and also reduce RS overhead and CSI overhead.

Based on the above embodiment, further, as shown in FIG. 3, before the step S210, the method further includes:

Step S200, the network side device sends a first indication to the terminal, where the first indication is used to instruct the terminal to determine channel prediction information.

When the terminal receives the first indication, it determines channel prediction information according to the first indication; when the terminal does not receive the first indication, it may determine channel prediction information and/or Determine the channel prediction information according to the agreement.

Wherein, the content indicated by the first indication may include at least one of the following:

instructing the terminal to report or not to report channel prediction information;

The corresponding relationship between the channel prediction information and the channel prediction configuration;

A calculation method for the channel prediction information;

The time-frequency resource position used by the terminal for channel estimation, so that the terminal receives the CSI-RS at the time-frequency resource position to perform channel estimation.

In one embodiment, the form of the first indication includes at least one of the following:

Radio Resource Control (RRC) signaling;

Medium Access Control Element (MAC CE) signaling;

Downlink Control Information (DCI).

In one embodiment, before the step S200, the method further includes:

The network side device sends first configuration information to the terminal, where the first configuration information includes configuration for determining the channel prediction information.

Wherein, the first configuration information may include at least one of the following:

A calculation method for the channel prediction information;

The location of the time-frequency resource used by the terminal for channel estimation.

After acquiring the first configuration information, the terminal may determine channel prediction information based on the first configuration information when receiving the first indication sent by the network side device.

It can be seen from the technical solutions of the above embodiments that in the embodiments of the present application, the network side device sends a first indication to the terminal, the first indication is used to instruct the terminal to determine channel prediction information, and according to the channel prediction information reported by the terminal , to determine the configuration for channel prediction, so as to instruct the terminal to report channel prediction information in a timely manner, so as to properly configure or switch the AI network in time, and use a more suitable AI network for channel prediction.

It should be noted that, the channel prediction method provided in the embodiment of the present application may be executed by a channel prediction device, or a control module in the channel prediction device for executing the channel prediction method. In the embodiment of the present application, the channel prediction device provided in the embodiment of the present application is described by taking the channel prediction method performed by the channel prediction device as an example.

As shown in FIG. 4 , an embodiment of the present application provides a channel prediction device, and the channel prediction device includes: a transceiver module 401 and a configuration module 402 .

The transceiver module 401 is used to obtain channel prediction information from the terminal; the configuration module 402 is used to determine the configuration for channel prediction according to the channel prediction information; The configuration of the artificial intelligence network.

Further, the channel prediction information includes at least one of the following:

channel change rate;

The level of the rate of change of the channel.

Further, the channel change rate includes at least one of the following:

the rate of change of the temporal correlation;

the rate of change of the magnitude of a particular diameter;

the rate of change of the phase of the specified diameter;

The rate of change of the time delay of the specific path;

the rate of change of the amplitude of a particular port;

the rate of change of the phase of the particular port;

the moving speed of the terminal;

the rotational speed of the terminal;

The rate of change of the beam.

Further, the rate of change of the time-domain correlation includes:

The rate of change of the target parameter of the U-shaped spectrum.

Further, the specific diameter includes at least one of the following:

diameter with maximum power;

Several diameters with maximum power;

The path along which power is concentrated in the direction of line-of-sight propagation;

Paths whose power exceeds the first threshold.

Further, the rate of change of the beam includes at least one of the following:

rate of change of target parameters for a particular beam;

The rate at which beams are switched.

Further, the specific beam includes at least one of the following:

The beam corresponding to the smallest control resource set identifier;

Control the beam corresponding to resource set CORESET 0;

The beam indicated by the network side device or base station.

Further, the channel prediction configuration includes at least one of the following:

the structure of said artificial intelligence network;

parameters of said artificial intelligence network;

input data to said artificial intelligence network;

forecast time span;

Configuration of the reference signal;

Configuration of channel state information;

configuration of reporting of said channel state information;

Period for reporting channel state information;

The complexity of non-AI prediction algorithms.

It can be seen from the technical solutions of the above-mentioned embodiments that the embodiments of the present application obtain channel prediction information from the terminal; according to the channel prediction information, determine the configuration for performing channel prediction; wherein, the channel prediction configuration includes a The configuration of the artificial intelligence network, so that the AI network can be reasonably configured or switched, and a more suitable AI network is used for channel prediction. When the channel changes rapidly, the complex network is used to track the channel and improve the accuracy of channel prediction. When the change is slow, use a relatively simple network for channel prediction, reduce the complexity of the network, and also reduce the RS overhead and CSI overhead.

Based on the above embodiment, further, the transceiving module is further configured to send a first indication to the terminal, where the first indication is used to instruct the terminal to determine channel prediction information.

Further, the content indicated by the first indication includes at least one of the following:

A calculation method for the channel prediction information;

Further, the form of the first indication includes at least one of the following:

radio resource control signaling;

Media access control unit signaling;

Downlink control information.

Further, the transceiving module is further configured to send first configuration information to the terminal, where the first configuration information includes configuration for determining the channel prediction information.

Further, the first configuration information includes at least one of the following:

A calculation method for the channel prediction information;

It can be seen from the technical solutions of the above-mentioned embodiments that in this embodiment of the present application, by sending a first indication to the terminal, the first indication is used to instruct the terminal to determine channel prediction information, and determine the channel prediction information according to the channel prediction information reported by the terminal. The configuration of channel prediction can instruct the terminal to report channel prediction information in time, which can be used to reasonably configure or switch the AI network in time, and use a more suitable AI network for channel prediction.

The channel prediction device in the embodiment of the present application may be a device, a device with an operating system or an electronic device, or a component, an integrated circuit, or a chip in a terminal. The apparatus or electronic equipment may be a mobile terminal or a non-mobile terminal. Exemplarily, the mobile terminal may include but not limited to the types of terminals 11 listed above, and the non-mobile terminal may be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television ( television, TV), teller machines or self-service machines, etc., are not specifically limited in this embodiment of the present application.

The channel prediction device provided in the embodiment of the present application can realize each process realized by the method embodiments in FIG. 2 to FIG. 3 and achieve the same technical effect. To avoid repetition, details are not repeated here.

As shown in FIG. 5 , the embodiment of the present application also provides a channel prediction method. The method may be executed by a terminal. In other words, the method may be executed by software or hardware installed in the terminal. The execution steps of the method are as follows.

Step S510, the terminal determines channel prediction information.

channel change rate;

The level of the rate of change of the channel.

Further, the channel change rate includes at least one of the following:

the rate of change of the temporal correlation;

the rate of change of the magnitude of a particular diameter;

the rate of change of the phase of the specified diameter;

The rate of change of the time delay of the specific path;

the rate of change of the amplitude of a particular port;

the rate of change of the phase of the particular port;

Terminal movement speed;

the rotational speed of the terminal;

The rate of change of the beam.

Further, the rate of change of the time-domain correlation includes:

The rate of change of the target parameter of the U-shaped spectrum.

Further, the specific diameter includes at least one of the following:

diameter with maximum power;

Several diameters with maximum power;

Paths whose power exceeds the first threshold.

Further, the rate of change of the beam includes at least one of the following:

rate of change of target parameters for a particular beam;

The rate at which beams are switched.

Further, the specific beam includes at least one of the following:

The beam corresponding to the smallest control resource set identifier;

Control the beam corresponding to resource set CORESET 0;

The beam indicated by the network side device or base station.

Step S520, the terminal reports the channel prediction information to the network side device, and the channel prediction information is used to determine the channel prediction configuration of the network side device; wherein, the channel prediction configuration includes The configuration of the artificial intelligence network.

the structure of said artificial intelligence network;

parameters of said artificial intelligence network;

input data to said artificial intelligence network;

forecast time span;

Configuration of the reference signal;

Configuration of channel state information;

configuration of reporting of said channel state information;

Period for reporting channel state information;

The complexity of non-AI prediction algorithms.

The steps S510-520 can implement the method embodiment of the steps S210-S220 shown in FIG. 2, and obtain the same technical effect, and the repeated parts will not be repeated here.

It can be seen from the technical solutions of the above embodiments that in the embodiment of the present application, the terminal determines the channel prediction information; the terminal reports the channel prediction information to the network side device, and the channel prediction information is used to determine that the network side device performs channel prediction configuration; wherein, the configuration of the channel prediction includes the configuration of an artificial intelligence network for performing channel prediction, so that the AI network can be reasonably configured or switched, and a more suitable AI network is used for channel prediction, and when the channel changes rapidly Sometimes, use a complex network to track the channel to improve the accuracy of channel prediction. When the channel changes slowly, use a relatively simple network for channel prediction to reduce the complexity of the network, and also reduce RS overhead and CSI overhead.

Based on the above embodiments, further, the step S510 includes at least one of the following:

The terminal determines channel prediction information according to the first indication sent by the network side device;

If the terminal does not receive the first indication, determine channel prediction information according to the configured period;

The terminal determines the channel prediction information according to the agreement.

A calculation method for the channel prediction information;

radio resource control signaling;

Media access control unit signaling;

Downlink control information.

Further, before the terminal determines the channel prediction information according to the first indication sent by the network side device, the method further includes:

The terminal receives first configuration information sent by the network side device, where the first configuration information includes configuration for determining the channel prediction information.

A calculation method for the channel prediction information;

It can be seen from the technical solutions of the above embodiments that the terminal in this embodiment of the present application can determine and report the channel prediction information according to the first indication sent by the network-side device, the configuration period and/or the protocol agreement, so that the network-side device can determine to perform channel prediction configuration, so that the channel prediction information can be reported in time, so that the network side equipment can reasonably configure or switch the AI network in time, and use a more suitable AI network for channel prediction.

It should be noted that, the channel prediction method provided by the embodiment of the present application may be executed by a channel prediction device, or a control module in the channel prediction device for executing the channel prediction method. In the embodiment of the present application, the channel prediction device provided in the embodiment of the present application is described by taking the channel prediction method performed by the channel prediction device as an example.

As shown in FIG. 6 , the embodiment of the present application also provides another channel prediction device, which includes: a calculation module 601 and a reporting module 602 .

The calculation module 601 is used to determine channel prediction information; the reporting module 602 is used to report the channel prediction information to the network side device, and the channel prediction information is used to determine the channel prediction configuration of the network side device; wherein , the channel prediction configuration includes a configuration of an artificial intelligence network for performing channel prediction.

channel change rate;

The level of the rate of change of the channel.

Further, the channel change rate includes at least one of the following:

the rate of change of the temporal correlation;

the rate of change of the magnitude of a particular diameter;

the rate of change of the phase of the specified diameter;

The rate of change of the time delay of the specific path;

the rate of change of the amplitude of a particular port;

the rate of change of the phase of the particular port;

Terminal movement speed;

the rotational speed of the terminal;

The rate of change of the beam.

Further, the rate of change of the time-domain correlation includes:

The rate of change of the target parameter of the U-shaped spectrum.

Further, the specific diameter includes at least one of the following:

diameter with maximum power;

Several diameters with maximum power;

Paths whose power exceeds the first threshold.

Further, the rate of change of the beam includes at least one of the following:

rate of change of target parameters for a particular beam;

The rate at which beams are switched.

Further, the specific beam includes at least one of the following:

The beam corresponding to the smallest control resource set identifier;

Control the beam corresponding to resource set CORESET 0;

The beam indicated by the network side device or base station.

the structure of said artificial intelligence network;

parameters of said artificial intelligence network;

input data to said artificial intelligence network;

forecast time span;

Configuration of the reference signal;

Configuration of channel state information;

configuration of reporting of said channel state information;

Period for reporting channel state information;

The complexity of non-AI prediction algorithms.

It can be seen from the technical solutions of the foregoing embodiments that the embodiment of the present application determines the channel prediction information; reports the channel prediction information to the network side device, and the channel prediction information is used to determine the channel prediction configuration of the network side device; wherein , the configuration of the channel prediction includes the configuration of the artificial intelligence network for performing channel prediction, so that the AI network can be reasonably configured or switched, and a more suitable AI network is used for channel prediction. When the channel changes rapidly, the use of complex The network tracks the channel and improves the accuracy of channel prediction. When the channel changes slowly, using a relatively simple network for channel prediction reduces the complexity of the network, and can also reduce RS overhead and CSI overhead.

Based on the above embodiments, further, the computing module is configured to perform at least one of the following:

determining channel prediction information according to the first indication sent by the network side device;

If the first indication is not received, determine channel prediction information according to a configured period;

Determine the channel prediction information according to the agreement.

Instruct to report or not to report channel prediction information;

A calculation method for the channel prediction information;

Time-frequency resource location for channel estimation.

radio resource control signaling;

Media access control unit signaling;

Downlink control information.

Further, the computing module is further configured to receive first configuration information sent by the network side device, where the first configuration information includes configuration for determining the channel prediction information.

A calculation method for the channel prediction information;

It can be seen from the technical solutions of the above-mentioned embodiments that the embodiments of the present application can determine and report channel prediction information according to the first indication sent by the network-side device, the configuration cycle and/or protocol agreement, so that the network-side device can determine the channel prediction information. Configuration, so that the terminal can report channel prediction information in time, so that the network side equipment can reasonably configure or switch the AI network in time, and use a more suitable AI network for channel prediction.

The channel prediction device provided by the embodiment of the present application can realize each process realized by the method embodiment in FIG. 5 and achieve the same technical effect. To avoid repetition, details are not repeated here.

Further, as shown in FIG. 7 , the embodiment of the present application also provides a communication device 700, including a processor 701, a memory 702, and programs or instructions stored in the memory 702 and executable on the processor 701, such as When the communication device 700 is a terminal, when the program or instruction is executed by the processor 701, each process of the above-mentioned embodiment of the channel prediction method can be realized, and the same technical effect can be achieved. When the communication device 700 is a network-side device, when the program or instruction is executed by the processor 701, each process of the channel prediction method embodiment described above can be achieved, and the same technical effect can be achieved. To avoid repetition, details are not repeated here.

The embodiment of the present application also provides a network side device, including a processor and a communication interface, and the processor is used to determine the channel prediction configuration according to the channel prediction information; wherein, the channel prediction configuration includes a channel prediction The configuration of the artificial intelligence network, the communication interface is used to obtain the channel prediction information from the terminal. The network-side device embodiment corresponds to the above-mentioned network-side device method embodiment, and each implementation process and implementation mode of the above-mentioned method embodiment can be applied to this network-side device embodiment, and can achieve the same technical effect.

Specifically, the embodiment of the present application also provides a network side device. As shown in FIG. 8 , the network device 800 includes: an antenna 801 , a radio frequency device 802 , and a baseband device 803 . The antenna 801 is connected to the radio frequency device 802 . In the uplink direction, the radio frequency device 802 receives information through the antenna 801, and sends the received information to the baseband device 803 for processing. In the downlink direction, the baseband device 803 processes the information to be sent and sends it to the radio frequency device 802 , and the radio frequency device 802 processes the received information and sends it out through the antenna 801 .

The foregoing frequency band processing device may be located in the baseband device 803 , and the method performed by the network side device in the above embodiments may be implemented in the baseband device 803 , and the baseband device 803 includes a processor 804 and a memory 805 .

The baseband device 803 may include at least one baseband board, for example, a plurality of chips are arranged on the baseband board, as shown in FIG. The network device operations shown in the above method embodiments.

The baseband device 803 may further include a network interface 806 for exchanging information with the radio frequency device 802, and the interface is, for example, a common public radio interface (CPRI for short).

Specifically, the network-side device in this embodiment of the present invention also includes: instructions or programs stored in the memory 805 and operable on the processor 804, and the processor 804 calls the instructions or programs in the memory 805 to execute the modules shown in FIG. 4 To avoid duplication, the method of implementation and to achieve the same technical effect will not be repeated here.

The embodiment of the present application also provides a terminal, including a processor and a communication interface, the processor is used to calculate channel prediction information, the communication interface is used to report the channel prediction information to the network side equipment, and the channel prediction information is used to determine the The network-side device configures channel prediction; wherein, the channel prediction configuration includes configuration of an artificial intelligence network for performing channel prediction. This terminal embodiment corresponds to the above-mentioned terminal-side method embodiment, and each implementation process and implementation mode of the above-mentioned method embodiment can be applied to this terminal embodiment, and can achieve the same technical effect. Specifically, FIG. 9 is a schematic diagram of a hardware structure of a terminal implementing an embodiment of the present application.

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

Those skilled in the art can understand that the terminal 900 can also include a power supply (such as a battery) for supplying power to various components, and the power supply can be logically connected to the processor 910 through the power management system, so as to manage charging, discharging, and power consumption through the power management system. Management and other functions. The terminal structure shown in FIG. 9 does not constitute a limitation on the terminal, and the terminal may include more or fewer components than shown in the figure, or combine some components, or arrange different components, which will not be repeated here.

It should be understood that, in the embodiment of the present application, the input unit 904 may include a graphics processor (Graphics Processing Unit, GPU) 9041 and a microphone 9042, and the graphics processor 9041 is used for the image capture device ( Such as the image data of the still picture or video obtained by the camera) for processing. The display unit 906 may include a display panel 9061, and the display panel 9061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 907 includes a touch panel 9071 and other input devices 9072 . The touch panel 9071 is also called a touch screen. The touch panel 9071 may include two parts, a touch detection device and a touch controller. Other input devices 9072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, and joysticks, which will not be repeated here.

In the embodiment of the present application, the radio frequency unit 901 receives the downlink data from the network side device, and processes it to the processor 910; in addition, sends the uplink data to the network side device. Generally, the radio frequency unit 901 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier, a duplexer, and the like.

The memory 909 can be used to store software programs or instructions as well as various data. The memory 909 may mainly include a program or instruction storage area and a data storage area, wherein the program or instruction storage area may store an operating system, an application program or instructions required by at least one function (such as a sound playback function, an image playback function, etc.) and the like. In addition, the memory 909 may include a high-speed random access memory, and may also include a non-transitory memory, wherein the non-transitory memory may be a read-only memory (Read-Only Memory, ROM), a programmable read-only memory (Programmable ROM) , PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electrically erasable programmable read-only memory (Electrically EPROM, EEPROM) or flash memory. For example at least one disk storage device, flash memory device, or other non-transitory solid state storage device.

The processor 910 may include one or more processing units; optionally, the processor 910 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, application programs or instructions, etc., Modem processors mainly handle wireless communications, such as baseband processors. It can be understood that the foregoing modem processor may not be integrated into the processor 910 .

Wherein, the processor 910 is configured to determine channel prediction information.

The radio frequency unit 901 is configured to report the channel prediction information to the network side device, and the channel prediction information is used to determine the channel prediction configuration of the network side device; The configuration of the artificial intelligence network.

channel change rate;

The level of the rate of change of the channel.

Further, the channel change rate includes at least one of the following:

the rate of change of the temporal correlation;

the rate of change of the magnitude of a particular diameter;

the rate of change of the phase of the specified diameter;

The rate of change of the time delay of the specific path;

the rate of change of the amplitude of a particular port;

the rate of change of the phase of the particular port;

Terminal movement speed;

the rotational speed of the terminal;

The rate of change of the beam.

Further, the rate of change of the time-domain correlation includes:

The rate of change of the target parameter of the U-shaped spectrum.

Further, the specific diameter includes at least one of the following:

diameter with maximum power;

Several diameters with maximum power;

Paths whose power exceeds the first threshold.

Further, the rate of change of the beam includes at least one of the following:

rate of change of target parameters for a particular beam;

The rate at which beams are switched.

Further, the specific beam includes at least one of the following:

The beam corresponding to the smallest control resource set identifier;

Control the beam corresponding to resource set 0;

The beam indicated by the network side device or base station.

the structure of said artificial intelligence network;

parameters of said artificial intelligence network;

input data to said artificial intelligence network;

forecast time span;

Configuration of the reference signal;

Configuration of channel state information;

configuration of reporting of said channel state information;

Period for reporting channel state information;

The complexity of non-AI prediction algorithms.

It can be seen from the technical solutions of the above embodiments that the embodiments of the present application can reasonably configure or switch the AI network, use a more suitable AI network for channel prediction, and use a complex network to track the channel when the channel changes rapidly to improve channel prediction. When the channel changes slowly, use a relatively simple network for channel prediction, reduce the complexity of the network, and also reduce the RS overhead and CSI overhead.

Further, the processor 910 is configured to perform at least one of the following:

A calculation method for the channel prediction information;

radio resource control signaling;

Media access control unit signaling;

Downlink control information.

Further, the radio frequency unit 901 is further configured to receive first configuration information sent by the network side device, where the first configuration information includes a configuration for determining the channel prediction information.

A calculation method for the channel prediction information;

It can be seen from the technical solutions of the above embodiments that the embodiments of the present application can report channel prediction information in time, so that the network side equipment can properly configure or switch the AI network in time, and use a more suitable AI network for channel prediction.

The embodiment of the present application also provides a readable storage medium. The readable storage medium stores programs or instructions. When the program or instructions are executed by the processor, the various processes of the above-mentioned channel prediction method embodiments can be achieved, and the same To avoid repetition, the technical effects will not be repeated here.

Wherein, the processor is the processor in the terminal described in the foregoing embodiments. The readable storage medium includes computer readable storage medium, such as computer read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.

The 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, and the processor is used to run programs or instructions to implement the above channel prediction method embodiment Each process can achieve the same technical effect, so in order to avoid repetition, it will not be repeated here.

It should be understood that the chip mentioned in the embodiment of the present application may also be called a system-on-chip, a system-on-chip, a system-on-a-chip, or a system-on-a-chip.

It should be noted that, in this document, the term "comprising", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising 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, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. Functions are performed, for example, 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.

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

The embodiments of the present application have been described above in conjunction with the accompanying drawings, but 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 Under the inspiration of this application, without departing from the purpose of this application and the scope of protection of the claims, many forms can also be made, all of which belong to the protection of this application.

Claims

A channel prediction method, comprising:

The network side device acquires channel prediction information from the terminal;

The network side device determines a configuration for performing channel prediction according to the channel prediction information; wherein the channel prediction configuration includes a configuration of an artificial intelligence network for performing channel prediction.
The method according to claim 1, wherein the channel prediction information includes at least one of the following:

channel change rate;

The level of the rate of change of the channel.
The method according to claim 2, wherein the channel change rate comprises at least one of the following:

the rate of change of the temporal correlation;

the rate of change of the magnitude of a particular diameter;

the rate of change of the phase of the specified diameter;

The rate of change of the time delay of the specific path;

the rate of change of the amplitude of a particular port;

the rate of change of the phase of the particular port;

the moving speed of the terminal;

the rotational speed of the terminal;

The rate of change of the beam.
The method according to claim 3, wherein the rate of change of the temporal correlation comprises:

The rate of change of the target parameter of the U-shaped spectrum.
The method according to claim 3, wherein the specific path comprises at least one of the following:

diameter with maximum power;

Several diameters with maximum power;

The path along which power is concentrated in the direction of line-of-sight propagation;

Paths whose power exceeds the first threshold.
The method according to claim 3, wherein the rate of change of the beam comprises at least one of the following:

rate of change of target parameters for a particular beam;

The rate at which beams are switched.
The method according to claim 6, wherein the specific beam comprises at least one of the following:

The beam corresponding to the smallest control resource set identifier;

Control the beam corresponding to resource set CORESET 0;

The beam indicated by the network side device or base station.
The method according to claim 1, wherein the channel prediction configuration includes at least one of the following:

the structure of said artificial intelligence network;

parameters of said artificial intelligence network;

input data to said artificial intelligence network;

forecast time span;

Configuration of the reference signal;

Configuration of channel state information;

configuration of reporting of said channel state information;

Period for reporting channel state information;

The complexity of non-AI prediction algorithms.
The method according to claim 1, wherein, before the acquisition of channel prediction information from the terminal, the method further comprises:

The network side device sends a first indication to the terminal, where the first indication is used to instruct the terminal to determine channel prediction information.
The method according to claim 9, wherein the content indicated by the first indication includes at least one of the following:

instructing the terminal to report or not to report channel prediction information;

The corresponding relationship between the channel prediction information and the channel prediction configuration;

A calculation method for the channel prediction information;

The location of the time-frequency resource used by the terminal for channel estimation.
The method according to claim 9, wherein, before the network side device sends the first indication to the terminal, the method further comprises:

The network-side device sends first configuration information to the terminal, where the first configuration information includes configuration for determining the channel prediction information.
The method according to claim 11, wherein the first configuration information includes at least one of the following:

The corresponding relationship between the channel prediction information and the channel prediction configuration;

A calculation method for the channel prediction information;

The location of the time-frequency resource used by the terminal for channel estimation.
A channel prediction device, comprising:

A transceiver module, configured to obtain channel prediction information from the terminal;

A configuration module, configured to determine a channel prediction configuration according to the channel prediction information; wherein, the channel prediction configuration includes a configuration of an artificial intelligence network for performing channel prediction.
A channel prediction method, comprising:

The terminal determines channel prediction information;

The terminal reports the channel prediction information to the network side device, and the channel prediction information is used to determine the channel prediction configuration of the network side device; wherein, the channel prediction configuration includes artificial intelligence for performing channel prediction Network configuration.
The method according to claim 14, wherein the channel prediction information includes at least one of the following:

channel change rate;

The level of the rate of change of the channel.
The method according to claim 15, wherein the channel change rate comprises at least one of the following:

the rate of change of the temporal correlation;

the rate of change of the magnitude of a particular diameter;

the rate of change of the phase of the specified diameter;

The rate of change of the time delay of the specific path;

the rate of change of the amplitude of a particular port;

the rate of change of the phase of the particular port;

Terminal movement speed;

the rotational speed of the terminal;

The rate of change of the beam.
The method of claim 16, wherein the rate of change of the temporal correlation comprises:

The rate of change of the target parameter of the U-shaped spectrum.
The method according to claim 16, wherein the specified path comprises at least one of the following:

diameter with maximum power;

Several diameters with maximum power;

The path along which power is concentrated in the direction of line-of-sight propagation;

Paths whose power exceeds the first threshold.
The method of claim 16, wherein the rate of change of the beam comprises at least one of the following:

rate of change of target parameters for a particular beam;

The rate at which beams are switched.
The method of claim 19, wherein the particular beam comprises at least one of the following:

The beam corresponding to the smallest control resource set identifier;

Control the beam corresponding to resource set 0;

The beam indicated by the network side device or base station.
The method according to claim 14, wherein the channel prediction configuration includes at least one of the following:

the structure of said artificial intelligence network;

parameters of said artificial intelligence network;

input data to said artificial intelligence network;

forecast time span;

Configuration of the reference signal;

Configuration of channel state information;

configuration of reporting of said channel state information;

Period for reporting channel state information;

The complexity of non-AI prediction algorithms.
The method according to claim 14, wherein the terminal determines channel prediction information, including at least one of the following:

The terminal determines channel prediction information according to the first indication sent by the network side device;

If the terminal does not receive the first indication, determine channel prediction information according to the configured period;

The terminal determines the channel prediction information according to the agreement.
The method according to claim 22, wherein the content indicated by the first indication includes at least one of the following:

instructing the terminal to report or not to report channel prediction information;

The corresponding relationship between the channel prediction information and the channel prediction configuration;

A calculation method for the channel prediction information;

The location of the time-frequency resource used by the terminal for channel estimation.
The method according to claim 22, wherein, before the terminal determines the channel prediction information according to the first indication sent by the network side device, the method further comprises:

The terminal receives first configuration information sent by the network side device, where the first configuration information includes configuration for determining the channel prediction information.
The method according to claim 24, wherein the first configuration information includes at least one of the following:

The corresponding relationship between the channel prediction information and the channel prediction configuration;

A calculation method for the channel prediction information;

The location of the time-frequency resource used by the terminal for channel estimation.
A channel prediction device, comprising:

A calculation module, configured to determine channel prediction information;

A reporting module, configured to report the channel prediction information to the network side device, where the channel prediction information is used to determine the channel prediction configuration of the network side device; wherein, the channel prediction configuration includes a channel prediction configuration Configuration of artificial intelligence network.
A network side device, comprising a processor, a memory, and a program or instruction stored on the memory and operable on the processor, when the program or instruction is executed by the processor, it realizes the following claims 1 to 1: Steps of the channel prediction method described in any one of 12.
A terminal, comprising a processor, a memory, and a program or instruction stored in the memory and operable on the processor, when the program or instruction is executed by the processor, any of claims 14 to 25 can be realized. A step of the channel prediction method described in one item.
A readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the channel prediction method according to any one of claims 1-12 is realized, or the channel prediction method according to any one of claims 1-12 is realized, or the method according to claim 1 is realized. The steps of the channel prediction method described in any one of 14 to 25 are required.