CN115941394A

CN115941394A - Channel prediction method, device, apparatus and storage medium

Info

Publication number: CN115941394A
Application number: CN202110915578.XA
Authority: CN
Inventors: 李健之; 朱理辰; 郑占旗
Original assignee: Datang Mobile Communications Equipment Co Ltd
Current assignee: Datang Mobile Communications Equipment Co Ltd
Priority date: 2021-08-10
Filing date: 2021-08-10
Publication date: 2023-04-07

Abstract

The embodiment of the application provides a channel prediction method, equipment, a device and a storage medium, wherein the method comprises the following steps: determining one or more time delay paths used for channel prediction and the time delay of each time delay path in the one or more time delay paths according to historical channel estimation information; determining a first complex amplitude time sequence of each delay path according to the delay of each delay path in one or more delay paths and historical channel estimation information; determining an AR parameter of each time delay path according to the first complex amplitude time sequence; determining a complex amplitude predicted value of each delay path at a set moment according to the AR parameters and historical channel estimation information; performing MMSE interpolation according to the first complex amplitude time sequence and the complex amplitude predicted value to obtain a second complex amplitude time sequence of each time delay diameter after MMSE interpolation; and determining a channel prediction result according to the complex amplitude value in the set time period in the second complex amplitude time sequence. Therefore, the method and the device improve the accuracy of channel prediction.

Description

Channel prediction method, device, apparatus and storage medium

Technical Field

The present application relates to the field of communications technologies, and in particular, to a channel prediction method, device, apparatus, and storage medium.

Background

In a Time Division Duplex (TDD) mobile communication system, a base station needs to acquire channel state information through uplink channel estimation and perform downlink forming by using channel reciprocity.

In the beam forming process, a base station equivalently deconstructs a channel into a plurality of parallel transmission streams by adjusting the amplitude-phase gain of each array element of an antenna array, so that the transmission streams are orthogonal and do not interfere with each other while the delay path signals at the position of a target user can be superposed and enhanced in an in-phase manner.

However, each time the uplink channel estimation and downlink forming update of the base station have a certain time interval, the forming performance in the time interval will be reduced, and the faster the channel change, the more serious the influence on the forming performance.

Disclosure of Invention

Embodiments of the present application provide a channel prediction method, device, apparatus, and storage medium, to solve the problem in the prior art that the faster the channel changes, the more severely the influence on the forming performance is, implement channel prediction between the current time and the next uplink channel estimation, improve the forming effect of a base station, and improve the transmission performance.

In a first aspect, an embodiment of the present application provides a channel prediction method, including:

determining one or more time delay paths used for channel prediction and time delay of each time delay path in the one or more time delay paths according to historical channel estimation information;

determining a first complex amplitude time sequence of each time delay path according to the time delay of each time delay path in the one or more time delay paths and the historical channel estimation information;

determining an Autoregressive (AR) parameter of each time delay path according to the first complex amplitude time sequence;

determining a complex amplitude predicted value of each time delay path at a set moment according to the AR parameters and the historical channel estimation information;

performing Minimum Mean Square Error (MMSE) interpolation according to the first complex amplitude time sequence and the complex amplitude predicted value to obtain a second complex amplitude time sequence of each time delay diameter after the MMSE interpolation;

and determining a channel prediction result according to the complex amplitude value in the set time period in the second complex amplitude time sequence.

Optionally, according to the channel prediction method in an embodiment of the present application, the historical channel estimation information includes multiple frequency domain channel estimation results;

the determining one or more delay paths for channel prediction and a delay of each of the one or more delay paths according to historical channel estimation information includes:

acquiring a first frequency domain channel estimation result from the multiple frequency domain channel estimation results, wherein the first frequency domain channel estimation result is any one of the multiple frequency domain channel estimation results;

and determining one or more time delay paths used for channel prediction and the time delay of each time delay path in the one or more time delay paths according to the first frequency domain channel estimation result.

Optionally, according to the channel prediction method according to an embodiment of the present application, the determining, according to the first frequency domain channel estimation result, one or more delay paths used for channel prediction and a delay of each of the one or more delay paths includes:

performing data conjugate rearrangement and moving average pretreatment on the first frequency domain channel estimation result to obtain a channel frequency correlation matrix;

and determining one or more time delay paths used for channel prediction and the time delay of each time delay path in the one or more time delay paths by using a twiddle factor invariant method ESPRIT according to the channel frequency correlation matrix.

Optionally, according to the channel prediction method of an embodiment of the present application, the performing data conjugate rearrangement and moving average preprocessing on the first frequency domain channel estimation result to obtain a channel frequency correlation matrix includes:

determining the channel frequency correlation matrix by using a first formula; wherein the first formula comprises:

wherein N is _f Representing the frequency point number included in the first frequency domain channel estimation result; n is a radical of _win Represents the length of the sliding window; g _j Representing a frequency domain channel estimation vector in a jth sliding window in the first frequency domain channel estimation result;

denotes g _j The conjugate transpose of (1); j is dimension N _win ×N _win The anti-diagonal element of J is 1, and the other elements are 0; Ψ represents an average correlation matrix; Ψ ^* Represents the conjugate of Ψ; r is _f Representing the channel frequency correlation matrix.

determining a first complex amplitude time series of each of the one or more delay paths according to the delay of each of the one or more delay paths and the historical channel estimation information, including:

generating a Fourier transform matrix and a pseudo-inverse matrix of the Fourier transform matrix according to the time delay of each time delay path in the one or more time delay paths;

and determining the first complex amplitude time sequence by utilizing a maximum likelihood estimation method according to the pseudo-inverse matrix and the multiple frequency domain channel estimation results.

Optionally, according to the channel prediction method of an embodiment of the present application, the generating a fourier transform matrix and the fourier transform matrix pseudo-inverse matrix according to the time delay of each of the one or more time delay paths includes:

determining the pseudo-inverse matrix by using a second formula; wherein the second formula comprises:

wherein, L represents the total number of delay paths in the channel; n is a radical of _f The frequency point number representing the channel estimation; tau. _l The time delay of the L-th time delay path is shown, and the value range of L is 1 to L; f. of _k Represents the frequency of SRS pilot frequency resource, and the value range of k is 1 to N _f ；

F represents a fourier transform matrix; f ^H Denotes the conjugate transpose of F (F) ^H F) ^-1 Pair of representations (F) ^H F) The inversion is carried out on the basis of the obtained data,

a pseudo-inverse matrix representing the Fourier transform matrix. />

Optionally, according to a channel prediction method according to an embodiment of the present application, determining the first complex amplitude time series by using a maximum likelihood estimation method according to the pseudo-inverse matrix and the multiple frequency-domain channel estimation results includes:

determining the first complex amplitude time series using a third formula; wherein the third formula comprises:

wherein, L represents the total number of delay paths in the channel; n is a radical of hydrogen _f The frequency point number representing the channel estimation;

a pseudo-inverse matrix representing a Fourier transform matrix;

N _t representing the total times of the multiple frequency domain channel estimation results;

m represents the identity of the base station antenna; n represents the identity of the user port;

representing the frequency domain channel estimation result corresponding to the sub-channel between the mth base station antenna and the nth user port in the multiple frequency domain channel estimation results;

a first complex amplitude time series representing each time delay path corresponding to a subchannel between the mth base station antenna and the nth user port.

Optionally, according to the channel prediction method of an embodiment of the present application, the determining the AR parameter of each delay path according to the first complex amplitude time sequence of each delay path includes:

and determining the AR parameters of each time delay path according to the first complex amplitude time sequence and the set iteration stop condition.

Optionally, according to a channel prediction method according to an embodiment of the present application, the setting of the iteration stop condition includes one or more of:

the iteration times reach a preset expected AR order;

and the prediction error value of the two adjacent iterations is smaller than a preset threshold value.

the set time comprises a first Sounding Reference Signal (SRS) time, the first SRS time is the next SRS time of an appointed SRS time, and the appointed SRS time is the SRS time corresponding to the last frequency domain channel estimation result in the multiple frequency domain channel estimation results;

the determining a complex amplitude prediction value of each delay path at a set time according to the AR parameters and the historical channel estimation information includes:

determining a complex amplitude predicted value of the first SRS time by using a fourth formula; wherein the fourth formula comprises:

wherein, the first and the second end of the pipe are connected with each other,

the complex amplitude of the first delay path of the previous 1 times is represented, and the previous 1 times represent the last time frequency domain channel estimation result in the multiple time frequency domain channel estimation results;

represents a pre-or pre-determined in the multiple frequency domain channel estimation results>

The complex amplitude of the second delay path;

the optimal prediction order of the first time delay path AR is shown; />

A flip of an observation vector representing a complex amplitude time series of the ith delay path;

representing the AR coefficient;

and a complex amplitude prediction value representing the first SRS time.

the set time comprises a first SRS time and a second SRS time; the first SRS time is the next SRS time of the appointed SRS time, and the appointed SRS time is the SRS time corresponding to the last time frequency domain channel estimation result in the multiple times of frequency domain channel estimation results; the second SRS time is the next SRS time of the first SRS time;

determining complex amplitude prediction values of the first SRS time and the second SRS time by using a fifth formula; wherein the fifth formula comprises:

wherein the content of the first and second substances,

the complex amplitude of the first time delay path of the first 1 times is represented, and the first 1 times represent the multiple frequency domain channel estimation nodesThe last time of frequency domain channel estimation result in the result;

representing the multiple frequency domains pre-or in the channel estimation result>

The complex amplitude of the second ith delay path;

the optimal prediction order of the first time delay path AR is represented;

a flip of an observation vector representing a first complex amplitude time series of the ith delay path;

representing the AR coefficient;

a complex amplitude prediction value representing the first SRS time;

the inversion of an observation vector of another complex amplitude time sequence of the ith delay path after the complex amplitude predicted value of the first SRS time is added;

and a complex amplitude prediction value representing the second SRS time.

Optionally, according to the channel prediction method of an embodiment of the present application, performing MMSE interpolation according to the first complex amplitude time sequence and the complex amplitude prediction value to obtain a second complex amplitude time sequence of each delay path after MMSE interpolation includes:

determining the second complex amplitude time series using a sixth formula; wherein the sixth formula comprises:

/>

wherein m represents the identity of the base station antenna; n represents the identity of the user port;

p _max representing the maximum AR order in the AR parameters of each time delay path;

a first complex amplitude time series representing the l-th delay path;

a complex amplitude prediction value representing the first SRS time;

a complex amplitude prediction value representing the second SRS time;

a complex amplitude vector representing the l-th delay path constructed for the subchannel between the m-th base station antenna and the n-th user port;

N _R representing the total number of user ports; n is a radical of _T Representing the total number of base station antennas;

represents->

Transposing;

a second complex amplitude time sequence of the l time delay path after MMSE interpolation of a subchannel between the mth base station antenna and the nth user port;

ω _MMSE representing a matrix of MMSE interpolation coefficients.

Optionally, according to a channel prediction method of an embodiment of the present application, the method further includes:

determining an MMSE interpolation coefficient matrix using a seventh formula, wherein the seventh formula comprises:

N _x ＝p _max +2

N _y ＝(N _x -1)N _intp +1

wherein p is _max Representing the maximum AR order in the AR parameters of each time delay path; n is a radical of hydrogen _intp Expressing the interpolation multiplying power;

with a representation dimension of N _y ×N _x A matrix of (a); omega _MMSE Representing a matrix of MMSE interpolation coefficients.

the set time period includes a time period between the SRS time corresponding to the last frequency domain channel estimation result in the multiple frequency domain channel estimation results and the set time.

Optionally, according to a channel prediction method according to an embodiment of the present application, the determining a channel prediction result according to a complex amplitude value in a set time period in the second complex amplitude time series includes:

determining the channel prediction result using an eighth formula, wherein the eighth formula comprises:

wherein N is _R Representing the total number of user ports; n is a radical of _T Representing the total number of base station antennas; f represents a Fourier transform matrix;

the complex amplitude value in a set time period corresponding to a subchannel between the mth base station antenna and the nth user port is represented;

and the channel prediction result corresponding to the sub-channel between the mth base station antenna and the nth user port is shown.

In a second aspect, an embodiment of the present application provides a network device, including a memory, a transceiver, a processor:

a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the following operations:

In one possible implementation, the historical channel estimation information includes a plurality of frequency domain channel estimation results;

In a possible implementation manner, the determining, according to the first frequency domain channel estimation result, one or more delay paths used for channel prediction and a delay of each of the one or more delay paths includes:

In a possible implementation manner, the performing data conjugate rearrangement and moving average preprocessing on the first frequency domain channel estimation result to obtain a channel frequency correlation matrix includes:

wherein N is _f Representing the frequency point number included in the first frequency domain channel estimation result; n is a radical of _win Indicating the length of the sliding window; g _j Representing a frequency domain channel estimation vector in a jth sliding window in the first frequency domain channel estimation result;

denotes g _j The conjugate transpose of (1); j is dimension N _win ×N _win The anti-diagonal element of J is 1, and the other elements are 0; Ψ represents the average correlation matrix; Ψ ^* Represents the conjugate of Ψ; r _f Representing the channel frequency correlation matrix.

In a possible implementation manner, the generating a fourier transform matrix and the pseudo-inverse fourier transform matrix according to the delay of each of the one or more delay paths includes:

wherein, L represents the total number of delay paths in the channel; n is a radical of hydrogen _f The frequency point number representing the channel estimation; tau is _l The time delay of the L-th time delay path is represented, and the value range of L is 1 to L; f. of _k Represents the SRS pilot frequency resource frequency of the sounding reference signal, and the value range of k is 1 to N _f ；

F represents a Fourier transform matrix; f ^H Denotes the conjugate transpose of F (F) ^H F) ^-1 Represents a pair (F) ^H F) The inversion is carried out on the basis of the obtained data,

a pseudo-inverse matrix representing the Fourier transform matrix.

In one possible implementation manner, the determining the first complex amplitude time series by using a maximum likelihood estimation method according to the pseudo-inverse matrix and the multiple frequency-domain channel estimation results includes:

wherein, L represents the total number of delay paths in the channel; n is a radical of _f The frequency point number representing the channel estimation;

a pseudo-inverse matrix representing a Fourier transform matrix;

a first complex amplitude time series representing each delay path corresponding to a sub-channel between the mth base station antenna and the nth user port.

In a possible implementation manner, the determining the AR parameter of each delay path according to the first complex amplitude time series of each delay path includes:

and determining the AR parameters of each time delay path according to the first complex amplitude time sequence and set iteration stop conditions.

In one possible implementation, the setting of the iteration stop condition includes one or more of:

the iteration times reach a preset expected AR order;

the set time comprises a first Sounding Reference Signal (SRS) time, the first SRS time is the next SRS time of an appointed SRS time, and the appointed SRS time is the SRS time corresponding to the last time of frequency domain channel estimation results in the multiple times of frequency domain channel estimation results;

the determining the complex amplitude prediction value of each delay path at a set time according to the AR parameters and the historical channel estimation information includes:

wherein the content of the first and second substances,

the complex amplitude of the ith delay path of the previous 1 time is represented, and the previous 1 time represents the last time frequency domain channel estimation result in the multiple times of frequency domain channel estimation results;

The complex amplitude of the second delay path;

the optimal prediction order of the first time delay path AR is shown;

a flip of an observation vector representing a complex amplitude time series of the l-th delay path;

representing the AR coefficients;

and a complex amplitude prediction value representing the first SRS time.

the set time comprises a first SRS time and a second SRS time; the first SRS time is the next SRS time of the appointed SRS time, and the appointed SRS time is the SRS time corresponding to the last frequency domain channel estimation result in the multiple frequency domain channel estimation results; the second SRS time is the next SRS time of the first SRS time;

determining complex amplitude predicted values of the first SRS time and the second SRS time by using a fifth formula; wherein the fifth formula comprises:

The complex amplitude of the second delay path;

the optimal prediction order of the first time delay path AR is shown;

a flip of an observation vector representing a first complex amplitude time series of the ith delay path; />

Representing the AR coefficients;

a complex amplitude prediction value representing the first SRS time;

the method comprises the steps that after a complex amplitude predicted value at the first SRS moment is added, the observation vector of another complex amplitude time sequence of the ith delay path is turned;

and a complex amplitude prediction value representing the second SRS time.

In a possible implementation manner, the performing MMSE interpolation according to the first complex amplitude time sequence and the complex amplitude prediction value to obtain a second complex amplitude time sequence of each delay path after MMSE interpolation includes:

a first complex amplitude time series representing the l-th delay path;

a complex amplitude prediction value representing the first SRS time;

a complex amplitude prediction value representing the second SRS time;

indicating the subchannel between the mth base station antenna and the nth user portConstructing a complex amplitude vector of the first delay path;

represents->

Transposing;

ω _MMSE representing a matrix of MMSE interpolation coefficients.

In one possible implementation, the processor is further configured to:

N _x ＝p _max +2

N _y ＝(N _x -1)N _intp +1

wherein p is _max Representing the maximum AR order in the AR parameters of each time delay path; n is a radical of _intp Expressing the interpolation multiplying power;

the representation dimension is N _y ×N _x A matrix of (a); omega _MMSE Representing a matrix of MMSE interpolation coefficients.

In a possible implementation manner, the determining a channel prediction result according to a complex amplitude value in a set time period in the second complex amplitude time series includes:

In a third aspect, an embodiment of the present application provides a channel prediction apparatus, where the channel prediction apparatus includes:

a first determining unit, configured to determine, according to historical channel estimation information, one or more delay paths used for channel prediction and a delay of each of the one or more delay paths;

a second determining unit, configured to determine a first complex amplitude time sequence of each delay path according to the delay of each delay path of the one or more delay paths and the historical channel estimation information;

a third determining unit, configured to determine an autoregressive AR parameter of each delay path according to the first complex amplitude time series;

a fourth determining unit, configured to determine, according to the AR parameter and the historical channel estimation information, a complex amplitude prediction value of each delay path at a set time;

the interpolation unit is used for performing Minimum Mean Square Error (MMSE) interpolation according to the first complex amplitude time sequence and the complex amplitude predicted value to obtain a second complex amplitude time sequence of each time delay path after the MMSE interpolation;

and a fifth determining unit, configured to determine a channel prediction result according to the complex amplitude value in the set time period in the second complex amplitude time series.

In one possible implementation, the historical channel estimation information includes a plurality of frequency domain channel estimation results; the first determination unit includes:

an obtaining subunit, configured to obtain a first frequency domain channel estimation result from the multiple frequency domain channel estimation results, where the first frequency domain channel estimation result is any one of the multiple frequency domain channel estimation results;

a first determining subunit, configured to determine, according to the first frequency-domain channel estimation result, one or more delay paths used for channel prediction and a delay of each of the one or more delay paths.

In one possible implementation manner, the first determining subunit includes:

the processing module is used for carrying out data conjugate rearrangement and moving average pretreatment on the first frequency domain channel estimation result to obtain a channel frequency correlation matrix;

and the determining module is used for determining one or more time delay paths used for channel prediction and the time delay of each time delay path in the one or more time delay paths by using a twiddle factor invariant method ESPRIT according to the channel frequency correlation matrix.

In a possible implementation manner, the processing module is specifically configured to:

wherein, N _f Indicating the frequency point number included in the first frequency domain channel estimation result; n is a radical of _win Represents the length of the sliding window; g _j Representing a frequency domain channel estimation vector in a jth sliding window in the first frequency domain channel estimation result;

is represented by g _j The conjugate transpose of (1); j is dimension N _win ×N _win The anti-diagonal element of J is 1, and the other elements are 0; Ψ represents the average correlation matrix; Ψ ^* Represents the conjugate of Ψ; r _f Representing the channel frequency correlation matrix.

In one possible implementation, the historical channel estimation information includes a plurality of frequency domain channel estimation results; the second determination unit includes:

a generating subunit, configured to generate a fourier transform matrix and a pseudo-inverse matrix of the fourier transform matrix according to a time delay of each time delay path of the one or more time delay paths;

and the second determining subunit is used for determining the first complex amplitude time sequence by utilizing a maximum likelihood estimation method according to the pseudo-inverse matrix and the multiple frequency domain channel estimation results.

In a possible implementation manner, the generating subunit is specifically configured to:

wherein, L represents the total number of delay paths in the channel; n is a radical of hydrogen _f The frequency point number representing the channel estimation; tau is _l The time delay of the L-th time delay path is shown, and the value range of L is 1 to L; f. of _k Represents the frequency of SRS pilot frequency resource, and the value range of k is 1 to N _f ；

a pseudo-inverse matrix representing the Fourier transform matrix.

In a possible implementation manner, the second determining subunit is specifically configured to:

a pseudo-inverse matrix representing a Fourier transform matrix;

representing the mth base station antenna and the nth user in the multiple frequency domain channel estimation resultsFrequency domain channel estimation results corresponding to sub-channels between ports;

In a possible implementation manner, the third determining unit is specifically configured to:

the iteration times reach a preset expected AR order;

and the prediction error value of the adjacent two iterations is smaller than a preset threshold value.

the fourth determining unit is specifically configured to:

wherein the content of the first and second substances,

The complex amplitude of the second delay path;

the optimal prediction order of the first time delay path AR is represented;

representing the AR coefficient;

and a complex amplitude prediction value representing the first SRS time.

the fourth determining unit is specifically configured to:

The complex amplitude of the second delay path;

the optimal prediction order of the first time delay path AR is shown; />

representing the AR coefficient;

a complex amplitude prediction value representing the first SRS time;

and a complex amplitude prediction value representing the second SRS time.

In a possible implementation manner, the interpolation unit is specifically configured to:

determining the second complex amplitude time series using a sixth equation; wherein the sixth formula comprises:

p _max presentation instrumentThe maximum AR order in the AR parameters of each time delay path;

a first complex amplitude time series representing the l-th delay path;

a complex amplitude prediction value representing the first SRS time;

a complex amplitude prediction value representing the second SRS time;

represents->

Transposing;

ω _MMSE representing a matrix of MMSE interpolation coefficients.

In a possible implementation manner, the channel prediction apparatus further includes:

a sixth determining unit, configured to determine an MMSE interpolation coefficient matrix using a seventh formula, where the seventh formula includes:

N _x ＝p _max +2

N _y ＝(N _x -1)N _intp +1

In a possible implementation manner, the fifth determining unit is specifically configured to:

wherein, N _R Representing the total number of user ports; n is a radical of _T Representing the total number of base station antennas; f represents a fourier transform matrix;

In a fourth aspect, embodiments of the present application provide a processor-readable storage medium, which stores a computer program, and the computer program is configured to cause the processor to execute the steps of the channel prediction method according to the first aspect.

In a fifth aspect, the present application provides a computer-readable storage medium, which stores a computer program, where the computer program is used to make the computer execute the steps of the channel prediction method according to the first aspect.

In a sixth aspect, an embodiment of the present application provides a chip system, where the chip system includes at least one processor, a memory, and an interface circuit, where the memory, the interface circuit, and the at least one processor are interconnected by a line, and the at least one memory stores instructions therein; the instructions, when executed by the processor, implement the steps of the channel prediction method of the first aspect as described above.

In a seventh aspect, the present application provides a computer program product, which includes instructions for causing a computer to execute the steps of the channel prediction method according to the first aspect when the computer program product runs on the computer.

According to the channel prediction method, the device, the apparatus and the storage medium provided by the embodiment of the application, one or more time delay paths for channel prediction and the time delay of each time delay path are determined according to historical channel estimation information; determining a first complex amplitude time sequence of each time delay path according to the time delay of each time delay path and historical channel estimation information; determining an AR parameter of each time delay path according to the first complex amplitude time sequence, and determining a complex amplitude predicted value of each time delay path at a set moment according to the AR parameter and historical channel estimation information; performing MMSE interpolation according to the first complex amplitude time sequence and the complex amplitude predicted value to obtain a second complex amplitude time sequence of each time delay diameter after MMSE interpolation; and determining a channel prediction result according to the complex amplitude value in the set time period in the second complex amplitude time sequence, thereby realizing the prediction of the channel in the future time period according to the historical channel estimation information, and particularly improving the accuracy of channel prediction by means of AR parameter and MMSE interpolation, further improving the shaping effect of network equipment and improving the transmission performance.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following descriptions are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a schematic flowchart of a channel prediction method according to an embodiment of the present application;

fig. 2 is a second flowchart of a channel prediction method according to an embodiment of the present application;

fig. 3 is a schematic structural diagram of a channel prediction apparatus according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a network device according to an embodiment of the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

For the convenience of clearly describing the technical solutions of the embodiments of the present application, in the embodiments of the present application, if words such as "first" and "second" are used to distinguish the same items or similar items with basically the same functions and actions, those skilled in the art can understand that the words such as "first" and "second" do not limit the quantity and execution order.

In the embodiment of the present application, the term "and/or" describes an association relationship of associated objects, and means that there may be three relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

In the embodiments of the present application, the term "plurality" means two or more, and other terms are similar thereto.

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

In a TDD mobile communication system, a base station needs to acquire channel state information through uplink channel estimation and perform downlink forming by using channel reciprocity.

However, each time a certain time interval exists between uplink channel estimation and downlink forming update of the base station, forming performance in the time interval is reduced, and the faster the channel change, the more serious the influence on the forming performance is. Such as: when the user moves, the phase of each propagation delay path changes in a short time, and the transmission streams cannot maintain orthogonal with each other at the new position where the user is located, so that inter-stream interference occurs, and the transmission performance is reduced. At this time, the base station needs to calculate the forming weight value again according to the uplink channel estimation, so that each stream can return to the orthogonal state again. However, each time a certain time interval exists between uplink channel estimation and downlink forming update of the base station, forming performance in the time interval is reduced, and the faster the channel change, the more serious the influence on the forming performance is.

Therefore, the embodiment of the application provides a channel prediction method, device, apparatus and storage medium, which predict a channel between a current time and a next uplink channel estimation according to historical channel estimation information, thereby improving a shaping effect of a base station and improving transmission performance.

The method and the device are based on the same application concept, and because the principles of solving the problems of the method and the device are similar, the implementation of the device and the method can be mutually referred, and repeated parts are not described again.

The technical scheme provided by the embodiment of the application can be suitable for various systems, particularly 5G systems. For example, the applicable system may be a global system for mobile communication (GSM) system, a Code Division Multiple Access (CDMA) system, a Wideband Code Division Multiple Access (WCDMA) General Packet Radio Service (GPRS) system, a long term evolution (long term evolution, LTE) system, an LTE Frequency Division Duplex (FDD) system, an LTE Time Division Duplex (TDD) system, an LTE-a (long term evolution) system, a universal mobile system (universal mobile telecommunications system, UMTS), a universal internet Access (WiMAX) system, a New Radio Network (NR) system, etc. These various systems include terminal devices and network devices. The System may further include a core network portion, such as an Evolved Packet System (EPS), a 5G System (5 GS), and the like.

The network device according to the embodiment of the present application may be a base station, and the base station may include a plurality of cells for providing services to a terminal. A base station may also be referred to as an access point, or a device in an access network that communicates over the air-interface, through one or more sectors, with wireless terminal devices, or by other names, depending on the particular application. The network device may be configured to exchange received air frames and Internet Protocol (IP) packets with one another as a router between the wireless terminal device and the rest of the access network, which may include an Internet Protocol (IP) communications network. The network device may also coordinate attribute management for the air interface. For example, the network device according to the embodiment of the present application may be a Base Transceiver Station (BTS) in a Global System for Mobile communications (GSM) or a Code Division Multiple Access (CDMA), a network device (NodeB) in a Wideband Code Division Multiple Access (WCDMA), an evolved Node B (eNB) or an e-NodeB) in a Long Term Evolution (LTE) System, a 5G Base Station (gNB) in a 5G network architecture (next generation System), a Home evolved Node B (HeNB), a relay Node (relay Node), a Home Base Station (femto), a pico Base Station (pico), and the like, which are not limited in the embodiments of the present application. In some network architectures, a network device may include a Centralized Unit (CU) node and a Distributed Unit (DU) node, which may also be geographically separated.

Fig. 1 is a schematic flowchart of a channel prediction method provided in an embodiment of the present application, where the channel prediction method may be applied to a network device, for example: and a base station. As shown in fig. 1, the channel prediction method may include the steps of:

step 101, determining one or more delay paths for channel prediction and a delay of each of the one or more delay paths according to historical channel estimation information.

Specifically, before performing channel prediction, the network device needs to use a First-in First-out (FIFO) structure to store a certain number of sets of historical channel estimation information. Such as: and storing 60 or 70 sets of historical channel estimation information, wherein each set of historical channel estimation information refers to a frequency domain channel estimation result obtained by one-time uplink channel estimation, namely the historical channel estimation information comprises 60 or 70-time frequency domain channel estimation results.

In making the channel prediction, one or more of the delay paths used for channel prediction, and the delay for each delay path, may be determined using the stored historical channel estimate information.

Such as: the number of delay paths is L; when determining the L delay paths, the L delay paths may be determined by using a eigenvalue comparison method; in determining the time delay of each of the L delay paths, a Signal parameter Estimation (ESPRIT) algorithm based on a rotation Invariance Technique may be used to determine the time delay.

And step 102, determining a first complex amplitude time sequence of each time delay path according to the time delay of each time delay path in the one or more time delay paths and historical channel estimation information.

Specifically, the network device may determine the first complex amplitude time series for each delay path using a maximum likelihood estimation algorithm based on a stored set of number of sets of historical channel estimation information.

Step 103, determining Auto Regression (AR) parameters of each delay path according to the first complex amplitude time sequence.

Specifically, the network device may calculate an AR parameter for the first complex amplitude time series of each delay path, where the AR parameter may include an AR coefficient and an AR order. The specific implementation process comprises the following steps:

(1) First complex amplitude time sequence matrix based on each time delay path

Extracting one of the sub-channels, e.g. </or > in-device', from the channel estimation results corresponding to all antennas of the base station and the subscriber>

(2) Calculate each epoch using the Burg algorithmAnd (5) extended AR parameters. Such as: computing AR coefficient of the ith delay path by using Burg algorithm

And AR order->

L =1, 2, 3, \8230;, L. The AR parameters are applied to the complex amplitude time series matrix (i.e., the first complex amplitude time series) of the time delays of all antenna subchannels.

And step 104, determining a complex amplitude predicted value of each time delay path at a set time according to the AR parameters and the historical channel estimation information.

Specifically, the network device may determine the complex amplitude prediction value of each delay path at a set time according to the calculated AR parameter and a certain set number of sets of stored historical channel estimation information. Wherein, the set time can be the 1 st future SRS time; or a future 1 st SRS time and a future 2 nd SRS time; and so on, future 1 st SRS moment, future 2 nd SRS moment, \8230, and future nth SRS moment, etc. Wherein n is an integer greater than 2. The present application is not limited to specific values for setting the time.

Such as: if the set time is the future 1 st SRS time, the complex amplitude predicted value of each delay path at the future 1 st SRS time can be calculated according to the AR parameters and the historical channel estimation information.

For another example: the set time is the 1 st SRS time and the 2 nd SRS time in the future, in order to enable the interpolation of the subsequent Minimum Mean Square Error (MMSE) to be more accurate, the complex amplitude predicted value of the 1 st SRS time obtained by AR prediction can be used for recursion to obtain the complex amplitude predicted value of the 2 nd SRS time, and by analogy, the complex amplitude predicted value of the nth SRS time can be obtained by recursion. Wherein n is an integer greater than 2.

And 105, performing MMSE interpolation according to the first complex amplitude time sequence and the complex amplitude predicted value to obtain a second complex amplitude time sequence of each time delay diameter after MMSE interpolation.

Specifically, if the set time is the 1 st SRS time in the future, MMSE interpolation may be performed according to the first complex amplitude time sequence and the complex amplitude predicted value at the 1 st SRS time, so as to obtain a second complex amplitude time sequence of each delay path after MMSE interpolation.

If the set time is the 1 st SRS time in the future and the 2 nd SRS time in the future, MMSE interpolation may be performed according to the first complex amplitude time sequence, the 1 st SRS time, and the 2 nd SRS time, so as to obtain a second complex amplitude time sequence of each delay path after MMSE interpolation. In MMSE interpolation, interpolation can be performed according to MMSE interpolation coefficients.

In the MMSE interpolation, the interpolation may be performed according to an MMSE interpolation coefficient. Wherein, the MMSE interpolation coefficient is suitable for all antenna sub-channels of the same user.

And 106, determining a channel prediction result according to the complex amplitude value in the set time period in the second complex amplitude time sequence.

Specifically, the set time period corresponds to the set time. Such as: if the set time is the future 1 st SRS time, setting a time period between the current time and the future 1 st SRS time; if the set time is the 1 st SRS time and the 2 nd SRS time in the future, setting the time period as the time period from the current time to the 2 nd SRS time in the future; by analogy, the set time period may be a time period from the current time to the nth SRS time in the future. Wherein n is an integer greater than 2.

When determining the channel prediction result, the fourier transform matrix F may be used to obtain the channel prediction result. The specific process comprises the following steps:

(1) And determining the complex amplitude value of the second complex amplitude time sequence of each time delay path after MMSE interpolation in a set time period. Such as: n before interpolation _x Obtaining N after MMSE interpolation of each time delay path complex amplitude sample point (including 2 times of complex amplitude predicted values) _y Complex amplitude sample points. The section of elements from the current SRS time to the 2 nd SRS time predicted by the AR are taken out to obtain

I.e. based on>

Is the second complex amplitude time series->

To (N) of _y -N _intp + 1) to Nth _y A value. Wherein N is _intp Indicates the interpolation magnification of MMSE interpolation.

(2) Using time-delay path complex amplitude matrix after prediction and interpolation

And obtaining a channel prediction result. I.e. using the fourier transform matrix F and the delay-path complex amplitude matrix after prediction and interpolation->

And obtaining a channel prediction result.

The above process of determining the channel prediction result may be applied to all sub-channels and sub-carriers between m base station transmit antennas and n user ports (antennas), to obtain channel prediction results on all antennas and all sub-carriers.

It should be noted that, the above steps 101 to 106 may form a technical solution for implementing the channel prediction function, but the steps 101 to 106 are only an implementation example, and not every step in the steps 101 to 106 needs to be executed to implement the technical solution, but some steps of the steps 101 to 106 are optional, that is, these optional steps do not need to be executed to implement the technical solution. Wherein, these optional steps can be one or more steps from step 101 to step 106.

Such as: step 103 of steps 101 to 106 is optional.

For another example:

steps

102 and 103 of steps 101 to 106 are optional.

As can be seen from the above embodiments, one or more delay paths for channel prediction and the delay of each delay path are determined according to historical channel estimation information; determining a first complex amplitude time sequence of each time delay path according to the time delay of each time delay path and historical channel estimation information; determining an AR parameter of each time delay path according to the first complex amplitude time sequence, and determining a complex amplitude predicted value of each time delay path at a set moment according to the AR parameter and historical channel estimation information; performing MMSE interpolation according to the first complex amplitude time sequence and the complex amplitude predicted value to obtain a second complex amplitude time sequence of each time delay diameter after MMSE interpolation; and determining a channel prediction result according to the complex amplitude value in the set time period in the second complex amplitude time sequence, thereby realizing the prediction of the channel in the future time period according to the historical channel estimation information, and particularly improving the accuracy of channel prediction by means of AR parameter and MMSE interpolation, further improving the shaping effect of network equipment and improving the transmission performance.

Optionally, the historical channel estimation information includes multiple frequency domain channel estimation results;

Specifically, if the network device stores multiple sets of historical channel estimation information, each set of historical channel estimation information refers to a frequency domain channel estimation result obtained by one-time uplink channel estimation, one or more time delay paths for channel prediction and a time delay of each time delay path may be determined according to any set of historical channel estimation information.

It can be seen from the above embodiments that the determination of the delay path and the delay of the delay path can be determined by a primary frequency domain channel estimation result in the historical channel estimation information, thereby improving the flexibility of determining the delay path and the delay of the delay path.

Optionally, the determining, according to the first frequency domain channel estimation result, one or more delay paths used for channel prediction and a delay of each of the one or more delay paths includes:

determining one or more delay paths for channel prediction and a delay of each of the one or more delay paths using ESPRIT based on the channel frequency correlation matrix.

Specifically, if the first frequency domain channel estimation result includes N _f (e.g., N) _f 512) frequency points, for the N _f The channel estimation vector of each frequency point uses N _win (e.g., N) _win 256), and sequentially calculating correlation matrixes corresponding to the frequency domain channel estimation vectors of the sliding windows. Averaging the correlation matrices of all the sliding windows to obtain an average correlation matrix Ψ. Performing matrix conjugate rearrangement according to the average correlation matrix psi to obtain an average correlation matrix R after conjugate rearrangement _f Namely: average correlation matrix R after conjugate rearrangement _f Is a channel frequency correlation matrix.

Based on the calculated channel frequency correlation matrix, using the time delay vector tau of the ESPRIT accurate time delay path _path And the number L of delay paths in the channel. The number L of delay paths may be determined by using a eigenvalue comparison method.

It can be seen from the above embodiments that, when determining the delay path and the delay of the delay path, a channel frequency correlation matrix can be obtained through data conjugate rearrangement and moving average preprocessing, and then the delay path and the delay of the delay path are determined through an ESPRIT algorithm, thereby improving the accuracy of determining the delay path and the delay of the delay path.

Optionally, the performing data conjugate rearrangement and moving average preprocessing on the first frequency domain channel estimation result to obtain a channel frequency correlation matrix includes:

wherein N is _f Indicating the frequency point number included in the first frequency domain channel estimation result; n is a radical of _win Represents the length of the sliding window; g _j Representing a frequency domain channel estimation vector in a jth sliding window in the first frequency domain channel estimation result;

Such as: n is a radical of hydrogen _f Is 512,N _win For 256, the first formula may be used to perform data conjugate rearrangement and moving average preprocessing, and finally calculate the channel frequency correlation matrix.

As can be seen from the foregoing embodiments, in determining the channel frequency correlation matrix, the channel frequency correlation matrix can be calculated by using the first formula, so that the efficiency of determining the channel frequency correlation matrix is improved.

determining a first complex amplitude time sequence of each of the one or more delay paths according to the delay of each of the one or more delay paths and the historical channel estimation information, including:

Specifically, if the network device stores multiple sets of historical channel estimation information, each set of historical channel estimation information refers to a frequency domain channel estimation result obtained by one-time uplink channel estimation, and at this time, the first complex amplitude time sequence of each time delay path can be obtained based on a maximum likelihood estimation method by constructing a fourier transform matrix and a pseudo-inverse matrix thereof. The implementation process comprises the following steps:

(1) SRS pilot frequency resource vector f configured according to system _SRS And a delay vector tau of the delay path _path Generating a Fourier transform matrix F and a pseudo-inverse matrix thereof

(2) Let H be the historical channel estimation vector of the sub-channel between the mth base station transmit antenna and the nth user port (antenna) ^n,m I.e. it is a historical channel estimation matrix of

Wherein N is _R And N _T Representing the number of user and base station antennas, N, respectively _f And N _t Respectively representing the number of frequency points of channel estimation and the number of historical channel groups (i.e. the total number of frequency domain channel estimation results). Extracting the elements in the n-th row and the m-th column (extracting all the elements in the 3-dimensional domain, namely selecting all the data in the frequency domain), and extracting H ^n,m Deformation to one N _f ×N _t Is based on a matrix of->

(i.e. is->

Representing the multiple frequency domain channel estimation resultsFrequency domain channel estimation results corresponding to sub-channels between the mth base station antenna and the nth user port).

(3) Obtaining complex amplitude time series matrix (i.e. complex amplitude time series matrix) of each time delay path by maximum likelihood estimation algorithm

). Wherein the complex amplitude time series matrix (i.e., </or >>

) The L-th row in (L =1, 2, 3, \8230;, L) represents the first complex amplitude time series of the L-th delay path.

As can be seen from the foregoing embodiments, when determining the first complex amplitude time series of each delay path, the channel estimation method may further include performing channel prediction based on the time delay of each delay path and historical channel estimation information, and in particular, the historical channel estimation information may include multiple frequency domain channel estimation results.

Optionally, the generating a fourier transform matrix and the fourier transform matrix pseudo-inverse matrix according to the time delay of each time delay path includes:

wherein, L represents the total number of delay paths in the channel; n is a radical of _f The frequency point number representing the channel estimation; tau is _l The time delay of the L-th time delay path is shown, and the value range of L is 1 to L; f. of _k Represents the frequency of SRS pilot frequency resource, and the value range of k is 1 to N _f ；

F represents a Fourier transform matrix; f ^H Denotes the conjugate transpose of F (F) ^H F) ^-1 Pair of representations (F) ^H F) The inversion is carried out on the basis of the inversion,

a pseudo-inverse matrix representing the Fourier transform matrix.

It can be seen from the above embodiments that when the fourier transform matrix and the pseudo-inverse matrix are generated, the second formula can be used for calculation, so that the efficiency of generating the fourier transform matrix and the pseudo-inverse matrix is improved.

Optionally, the determining the first complex amplitude time series by using a maximum likelihood estimation method according to the pseudo-inverse matrix and the multiple frequency domain channel estimation results includes:

/>

a pseudo-inverse matrix representing a fourier transform matrix;

represents the m-th groupA first complex amplitude time series for each delay path corresponding to a subchannel between the station antenna and the nth user port.

As can be seen from the foregoing embodiments, when determining the first complex amplitude time series, the first complex amplitude time series can be calculated by using the third formula, so that the efficiency of determining the first complex amplitude time series is improved.

Optionally, the determining the AR parameter of each delay path according to the first complex amplitude time series of each delay path includes:

and determining the AR parameter of each time delay path according to the first complex amplitude time sequence and the set iteration stop condition.

Specifically, the set iteration stop condition may be an iteration stop condition of the Burg algorithm.

As can be seen from the above embodiments, based on the first complex amplitude time series of each delay path, the Burg algorithm may be used to obtain the AR parameters, which reduces the complexity of determining the AR parameters.

Optionally, the setting of the iteration stop condition includes one or more of:

the iteration times reach a preset expected AR order;

Specifically, the iteration stop condition of the Burg algorithm may be set to the following two:

firstly, the iteration times reach a preset expected AR order;

and the second prediction error change of 2 times of iteration (namely two adjacent iterations) is smaller than a preset threshold value.

It can be seen from the above embodiment that the Burg algorithm sets two different iteration stop conditions, the flexibility is high, and meanwhile, the calculation process only involves multiplication and addition operations, and the calculation complexity is low.

wherein the content of the first and second substances,

the complex amplitude of the ith delay path of the previous 1 time is represented, and the previous 1 time represents the last time frequency domain channel estimation result in the multiple times of frequency domain channel estimation results; />

The complex amplitude of the second ith delay path;

the optimal prediction order of the first time delay path AR is shown;

complex amplitude time of the l-th delay pathTurning over observation vectors of the inter-sequence;

representing the AR coefficient;

and a complex amplitude prediction value representing the first SRS time.

Specifically, when determining the complex amplitude prediction value of each delay path at the first SRS time (i.e. the 1 st SRS time in the future), the amplitude time series matrix of the sub-channel corresponding to the user port n may be determined for all base station antennas m

And performing AR prediction to obtain a complex amplitude predicted value at the 1 st future SRS moment.

To be provided with

For example, the complex amplitude predicted value of the 1 st SRS time delay path in the future is ^ greater than or equal to the fourth formula>

It can be seen from the above embodiments that the complex amplitude prediction value of each delay path at the future 1 st SRS time can be determined according to the AR parameters and the historical channel estimation information, so that the complex amplitude of each delay path is directly predicted, the robustness of channel prediction can be improved, the prediction accuracy and the equivalent signal-to-noise ratio of a predicted channel are improved, and the forming performance is ensured.

wherein the content of the first and second substances,

The complex amplitude of the second ith delay path;

the optimal prediction order of the first time delay path AR is shown;

a flip of an observation vector of the first complex amplitude time series representing the l-th delay path; />

Representing the AR coefficient;

a complex amplitude prediction value representing the first SRS time;

and a complex amplitude prediction value representing the second SRS time.

And performing AR prediction to obtain a predicted value of the future 1 st SRS time.

To be provided with

For example, the first delay path may have a complex amplitude at the future SRS time 1Measured value is obtained according to the fifth formula>

In order to make the subsequent MMSE interpolation more accurate, the complex amplitude predicted value at the 1 st SRS time obtained by the AR prediction can be used to perform recursion to obtain the complex amplitude predicted value at the 2 nd SRS time, that is, the ≥ obtained by the fifth formula>

It can be seen from the above embodiments that the complex amplitude prediction value of each delay path at the future SRS time 1 can be determined according to the AR parameters and the historical channel estimation information, and then the complex amplitude prediction value at the future SRS time 2 can be obtained by AR recursion, so that the complex amplitude of each delay path can be directly predicted, the robustness of channel prediction can be improved, the prediction accuracy and the equivalent signal-to-noise ratio of a predicted channel can be improved, and the forming performance can be ensured.

Optionally, the performing MMSE interpolation according to the first complex amplitude time sequence and the complex amplitude prediction value to obtain a second complex amplitude time sequence of each delay path after MMSE interpolation includes:

a first complex amplitude time series representing the l-th delay path;

a complex amplitude prediction value representing the first SRS time;

a complex amplitude prediction value representing the second SRS time;

represents->

Transposing;

ω _MMSE representing a matrix of MMSE interpolation coefficients.

It can be seen from the foregoing embodiment that, when determining the second complex amplitude time series, the second complex amplitude time series can be calculated by using the sixth equation, which improves the efficiency of determining the second complex amplitude time series.

Optionally, the channel prediction method further includes:

N _x ＝p _max +2

N _y ＝(N _x -1)N _intp +1

It can be seen from the foregoing embodiment that, when an MMSE interpolation coefficient matrix is determined, the MMSE interpolation coefficient matrix can be calculated by using the seventh equation, so that efficiency of determining the MMSE interpolation coefficient matrix is improved.

Specifically, the set time period is relative to the set time. Such as: if the set time is the future 1 st SRS time, setting a time period between the current time and the future 1 st SRS time; if the set time is the 1 st SRS time and the 2 nd SRS time in the future, setting the time period as the time period from the current time to the 2 nd SRS time in the future; by analogy, the set time period may be a time period from the current time to the nth SRS time in the future. Wherein n is an integer greater than 2.

Optionally, the determining a channel prediction result according to the complex amplitude value in the set time period in the second complex amplitude time sequence includes:

the complex amplitude value in a set time period corresponding to a sub-channel between the mth base station antenna and the nth user port is represented;

It can be seen from the above embodiments that, when determining the channel prediction result, the channel prediction result can be obtained through a fourier transform matrix, which reduces the complexity of determining the channel prediction result.

Fig. 2 is a second flowchart of a channel prediction method provided in the embodiment of the present application, where the channel prediction method can be applied to network devices, such as: and a base station. As shown in fig. 2, the implementation process of the channel prediction method specifically includes:

(1) A channel frequency correlation matrix is calculated.

Specifically, the channel frequency correlation matrix is determined by a first formula; wherein the first formula comprises:

wherein N is _f Representing the first frequency domain channel estimation result (the first frequency domain channel estimation)The result is any frequency domain channel estimation result in the historical channel estimation information); n is a radical of _win Represents the length of the sliding window; g _j Representing a frequency domain channel estimation vector in a jth sliding window in the first frequency domain channel estimation result;

is represented by g _j The conjugate transpose of (1); j is dimension N _win ×N _win The anti-diagonal element of J is 1, and the other elements are 0; Ψ represents an average correlation matrix; Ψ ^* Represents the conjugate of Ψ; r is _f Representing the channel frequency correlation matrix.

(2) And estimating the time delay of the L time delay paths by an ESPRIT algorithm based on the channel frequency correlation matrix. Wherein, L is the number of delay paths in the channel.

(3) A first complex amplitude time series of L delay paths is calculated.

Specifically, first, according to the delay of each delay path, a fourier transform matrix and a pseudo-inverse matrix of the fourier transform matrix are generated by using a second formula. Wherein the second formula comprises:

wherein, L represents the total number of delay paths in the channel; n is a radical of _f The frequency point number representing the channel estimation; tau is _l The time delay of the L-th time delay path is represented, and the value range of L is 1 to L; f. of _k Represents the SRS pilot frequency resource frequency of the sounding reference signal, and the value range of k is 1 to N _f ；

a pseudo-inverse matrix representing a fourier transform matrix.

And then, determining a first complex amplitude time sequence of each time delay path by using a third formula according to the pseudo-inverse matrix and a plurality of frequency domain channel estimation results in the historical channel estimation information. Wherein the third formula comprises:

a pseudo-inverse matrix representing a Fourier transform matrix;

(4) And aiming at the L-th time delay path in the L time delay paths, extracting the sub-channel. Wherein L is in the range of 1 to L.

Specifically, a first complex amplitude time series matrix based on each time delay path

(5) And determining the AR order of the ith delay path.

(6) And determining the AR coefficient of the ith delay path.

When the AR order and the AR coefficient of the first delay path are determined, the AR order and the AR coefficient can be obtained by using a Burg algorithm, so that the complexity of determining the AR order and the AR coefficient is reduced.

(7) Judging whether L is smaller than L, if so, updating L to L plus 1 (L = L + 1), and continuing to execute the step (4); if not, executing (8).

(8) And outputting the AR order and the AR coefficient of the ith delay path.

(9) And calculating a complex amplitude predicted value at a future moment.

Specifically, the complex amplitude prediction value at the first SRS timing may be determined using the fourth formula, and the complex amplitude prediction value at the second SRS timing may be determined using the fifth formula by recursion.

(10) And performing MMSE interpolation on the complex amplitude predicted value at the future moment.

Specifically, the first complex amplitude time sequence of each delay path and the predicted complex amplitude value at a future time may be used to form a predicted complex amplitude time sequence of each delay path, and then the sixth formula is used to perform MMSE interpolation on the predicted complex amplitude time sequence to obtain a second complex amplitude time sequence of each delay path after MMSE interpolation.

(11) And generating a predicted channel matrix (namely obtaining a channel prediction result).

Specifically, the second complex amplitude time series may be transformed by a fourier transform matrix using the eighth formula to generate a predicted channel matrix (i.e., a channel prediction result).

(12) And outputting the AR parameters and the channel prediction result.

The embodiment shows that the robustness of channel prediction can be greatly improved by predicting the complex amplitude of the delay path, the prediction precision and the equivalent signal-to-noise ratio of a predicted channel are improved, and the forming performance is ensured; the AR parameters are calculated through a Burg algorithm, two different iteration stop schemes can be set in the algorithm, the flexibility is high, meanwhile, the calculation process only involves multiplication and addition operation, and the calculation complexity is low; and the small-time granularity estimation is carried out on the channel at the corresponding moment of the AR prediction channel by using an MMSE interpolation method, so that the accuracy of channel prediction is improved.

In addition, based on the AR prediction result, MMSE interpolation is used for performing small-time granularity interpolation on the historical channel and the predicted channel sequence, so that the network equipment can accurately predict the channels at different moments in the SRS period and update the forming weight. And simulation results show that the AR-MMSE-based channel prediction method can greatly improve the forming performance of network equipment to mobile users and improve the transmission rate.

The channel prediction apparatus provided in this embodiment is specifically configured to execute the process of the foregoing method embodiment, as shown in fig. 3 below, and please refer to the content of the foregoing channel prediction method embodiment in detail, which is not described herein again.

Fig. 3 is a schematic structural diagram of a channel prediction apparatus according to an embodiment of the present disclosure, where the channel prediction apparatus may be used to perform the channel prediction method shown in fig. 1 or fig. 2; as shown in fig. 3, the channel prediction apparatus may include:

a first determining unit 31, configured to determine one or more delay paths used for channel prediction and a delay of each of the one or more delay paths according to historical channel estimation information;

a second determining unit 32, configured to determine, according to the time delay of each of the one or more time delay paths and the historical channel estimation information, a first complex amplitude time series of each of the time delay paths;

a third determining unit 33, configured to determine an autoregressive AR parameter of each delay path according to the first complex amplitude time series;

a fourth determining unit 34, configured to determine a complex amplitude prediction value of each delay path at a set time according to the AR parameter and the historical channel estimation information;

an interpolation unit 35, configured to perform minimum mean square error MMSE interpolation according to the first complex amplitude time sequence and the complex amplitude prediction value, to obtain a second complex amplitude time sequence of each delay path after MMSE interpolation;

a fifth determining unit 36, configured to determine a channel prediction result according to the complex amplitude value in the set time period in the second complex amplitude time series.

It should be noted that the functional units may form a technical solution for implementing the channel prediction function, but the functional units are only an implementation example, and not every functional unit needs to be equipped to implement the technical solution, but some of the functional units are optional, that is, the optional functional units do not need to be equipped to implement the technical solution. These optional units may be one or more units of the first determining unit 31, the second determining unit 32, the third determining unit 33, the fourth determining unit 34, the interpolating unit 35, and the fifth determining unit 36.

Such as: the fourth determination unit 34, the interpolation unit 35 are optional.

For another example: the third determination unit 33 is optional.

the first determination unit includes:

a first determining subunit, configured to determine, according to the first frequency domain channel estimation result, one or more delay paths used for channel prediction and a delay of each of the one or more delay paths.

In one possible implementation manner, the first determining subunit includes:

the processing module is used for carrying out data conjugate rearrangement and moving average preprocessing on the first frequency domain channel estimation result to obtain a channel frequency correlation matrix;

denotes g _j The conjugate transpose of (1); j is dimension N _win ×N _win The anti-diagonal element of J is 1, and the other elements are 0; Ψ represents the average correlation matrix; Ψ ^* Represents the conjugate of Ψ; r is _f Representing the channel frequency correlation matrix.

wherein, L represents the total number of delay paths in the channel; n is a radical of _f The frequency point number representing the channel estimation; tau is _l The time delay of the L-th time delay path is represented, and the value range of L is 1 to L; f. of _k Represents the frequency of SRS pilot frequency resource, and the value range of k is 1 to N _f ；

a pseudo-inverse matrix representing the Fourier transform matrix.

wherein, L represents the total number of delay paths in the channel; n is a radical of hydrogen _f Representing channel estimatesCounting the frequency points;

a pseudo-inverse matrix representing a Fourier transform matrix;

the iteration times reach a preset expected AR order;

the fourth determining unit is specifically configured to:

/>

representing the multiple frequency domains Pre-pre/pre in channel estimation results>

The complex amplitude of the second ith delay path;

the optimal prediction order of the first time delay path AR is represented;

representing the AR coefficient;

and a complex amplitude prediction value representing the first SRS time.

the fourth determining unit is specifically configured to:

wherein the content of the first and second substances,

representing the complex amplitude of the first delay path of the previous 1 times, wherein the previous 1 times represent the last time of frequency domain channel estimation in the frequency domain channel estimation results of the multiple timesCounting the result;

The complex amplitude of the second delay path;

the optimal prediction order of the first time delay path AR is shown;

representing the AR coefficient;

a complex amplitude prediction value representing the first SRS time;

and a complex amplitude prediction value representing the second SRS time.

a first complex amplitude time series representing the l-th delay path;

a complex amplitude prediction value representing the first SRS time;

a complex amplitude prediction value representing the second SRS time;

represents->

Transposing;

a second complex amplitude time sequence of the l time delay path after MMSE interpolation is carried out on a subchannel between the mth base station antenna and the nth user port;

ω _MMSE representing a matrix of MMSE interpolation coefficients.

a sixth determining unit, configured to determine an MMSE interpolation coefficient matrix by using a seventh formula, where the seventh formula includes:

N _x ＝p _max +2

N _y ＝(N _x -1)N _intp +1

It should be noted that, in the embodiment of the present application, the division of the unit is schematic, and is only one logic function division, and when the actual implementation is realized, another division manner may be provided. In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented as a software functional unit and sold or used as a stand-alone product, may be stored in a processor readable storage medium. Based on such understanding, the technical solutions of the present application, which are essential or contributing to the prior art, or all or part of the technical solutions may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.

It should be noted that the apparatus provided in this embodiment of the present application can implement all the method steps implemented by the embodiment of the channel prediction method, and can achieve the same technical effect, and details of the same parts and beneficial effects as those of the embodiment of the method are not described herein again.

The network device provided in the embodiment of the present application is specifically configured to execute the process of the foregoing method embodiment as shown in fig. 4, and please refer to the content of the foregoing channel prediction method embodiment in detail, which is not described herein again.

Fig. 4 is a schematic structural diagram of a network device according to an embodiment of the present application; the network device may be configured to perform the channel prediction method shown in fig. 1 or fig. 2. As shown in fig. 4, a transceiver 400 for receiving and transmitting data under the control of a processor 410.

Where in fig. 4, the bus architecture may include any number of interconnected buses and bridges, with one or more processors represented by processor 410 and various circuits of memory represented by memory 420 being linked together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface. The transceiver 400 may be a number of elements including a transmitter and a receiver that provide a means for communicating with various other apparatus over a transmission medium including wireless channels, wired channels, fiber optic cables, and the like. The processor 410 is responsible for managing the bus architecture and general processing, and the memory 420 may store data used by the processor 410 in performing operations.

The processor 410 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or a complex 4 Programmable Logic Device (CPLD), and may also have a multi-core architecture.

The processor 410 is configured to read the computer program in the memory 420 and perform the following operations:

performing Minimum Mean Square Error (MMSE) interpolation according to the first complex amplitude time sequence and the complex amplitude predicted value to obtain a second complex amplitude time sequence of each time delay path after the MMSE interpolation;

It should be noted that the above operation steps may constitute a technical solution for implementing the channel prediction function, but the above operation steps are only an implementation example, and not every operation step in the above operation needs to be executed to implement the technical solution, but some of the above operation steps are optional, that is, these optional operation steps do not need to be executed to implement the technical solution. Wherein, these optional operation steps can be one operation step or a plurality of operation steps in the above operation steps.

Such as: the operation step comprises the steps of conducting Minimum Mean Square Error (MMSE) interpolation according to the first complex amplitude time sequence and the complex amplitude predicted value to obtain a second complex amplitude time sequence of each time delay path after the MMSE interpolation; "is optional.

For another example: the operation step is that a complex amplitude predicted value of each time delay path at a set moment is determined according to the AR parameters and the historical channel estimation information; "is optional.

and determining one or more time delay paths used for channel prediction and the time delay of each time delay path in the one or more time delay paths by using a twiddle factor invariant ESPRIT according to the channel frequency correlation matrix.

is represented by g _j The conjugation transpose of (1); j is dimension N _win ×N _win The anti-diagonal element of J is 1, and the other elements are 0; Ψ represents the average correlation matrix; Ψ ^* Represents the conjugate of Ψ; r _f Representing the channel frequency correlation matrix.

F represents a fourier transform matrix; f ^H Denotes the conjugate transpose of F (F) ^H F) ^-1 Represents a pair (F) ^H F) The inversion is carried out on the basis of the inversion,

a pseudo-inverse matrix representing the Fourier transform matrix.

a pseudo-inverse matrix representing a Fourier transform matrix;

the iteration times reach a preset expected AR order;

In one possible implementation of the method according to the invention,

the historical channel estimation information comprises a plurality of frequency domain channel estimation results;

wherein the content of the first and second substances,

The complex amplitude of the second delay path;

the optimal prediction order of the first time delay path AR is represented;

representing the AR coefficient;

and a complex amplitude prediction value representing the first SRS time.

In one possible implementation of the method according to the invention,

wherein the content of the first and second substances,

The complex amplitude of the second delay path; />

the optimal prediction order of the first time delay path AR is shown;

representing the AR coefficient;

a complex amplitude prediction value representing the first SRS time;

and a complex amplitude prediction value representing the second SRS time.

a first complex amplitude time series representing the l-th delay path;

a complex amplitude prediction value representing the first SRS time;

a complex amplitude prediction value representing the second SRS time;

a complex amplitude vector representing the l-th delay path constructed for the sub-channel between the m-th base station antenna and the n-th user port;

represents->

Transposing;

ω _MMSE representing a matrix of MMSE interpolation coefficients.

In one possible implementation, the processor is further configured to:

determining an MMSE interpolation coefficient matrix by using a seventh formula, wherein the seventh formula comprises:

N _x ＝p _max +2

N _y ＝(N _x -1)N _intp +1

In one possible implementation form of the method,

determining a channel prediction result according to the complex amplitude value in the set time period in the second complex amplitude time sequence, including:

It should be noted that the network device provided in the embodiment of the present application can implement all the method steps implemented in the embodiment of the channel prediction method, and can achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those in the embodiment of the method are omitted here.

On the other hand, an embodiment of the present application further provides a processor-readable storage medium, where the processor-readable storage medium stores a computer program, where the computer program is configured to cause the processor to execute the method provided in each of the above embodiments, and the method includes:

determining one or more time delay paths used for channel prediction and the time delay of each time delay path according to historical channel estimation information;

determining a first complex amplitude time sequence of each time delay path according to the time delay of each time delay path and the historical channel estimation information;

The processor-readable storage medium can be any available medium or data storage device that can be accessed by a processor, including, but not limited to, magnetic memory (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical memory (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor memory (e.g., ROMs, EPROMs, EEPROMs, non-volatile memories (NAND FLASH), solid State Disks (SSDs)), etc.

On the other hand, an embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, where the computer program is configured to enable the computer to execute the steps of the channel prediction method, and for details, details of the content of the embodiment of the channel prediction method are described in detail, and are not repeated here.

In another aspect, an embodiment of the present application provides a chip system, where the chip system includes at least one processor, a memory, and an interface circuit, where the memory, the interface circuit, and the at least one processor are interconnected by a line, and the at least one memory stores instructions therein; when the instruction is executed by the processor, the steps of the channel prediction method are implemented, for details, the contents of the embodiments of the channel prediction method are described in detail, and are not repeated herein.

On the other hand, an embodiment of the present application provides a computer program product, where the computer program product includes instructions, and when the computer program product runs on a computer, the computer executes the steps of the channel prediction method, for details, see the contents of the embodiment of the channel prediction method, and are not described herein again.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These processor-executable instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims

1. A method for channel prediction, comprising:

2. The channel prediction method of claim 1, wherein the historical channel estimation information comprises a plurality of frequency domain channel estimation results;

3. The channel prediction method of claim 2, wherein the determining one or more delay paths for channel prediction and the delay of each of the one or more delay paths according to the first frequency domain channel estimation result comprises:

4. The channel prediction method of claim 3, wherein the performing data conjugate rearrangement and moving average preprocessing on the first frequency domain channel estimation result to obtain a channel frequency correlation matrix comprises:

wherein, N _f Indicating the frequency point number included in the first frequency domain channel estimation result; n is a radical of hydrogen _win Represents the length of the sliding window; g _j Representing a frequency domain channel estimation vector in a jth sliding window in the first frequency domain channel estimation result;

is represented by g _j The conjugation transpose of (1); j is dimension N _win ×N _win The anti-diagonal element of J is 1, and the other elements are 0; Ψ represents the average correlation matrix; psi ^* Represents the conjugate of Ψ; r _f Representing the channel frequency correlation matrix.

5. The channel prediction method of claim 1, wherein the historical channel estimation information comprises a plurality of frequency domain channel estimation results;

6. The channel prediction method of claim 5, wherein the generating a fourier transform matrix and a pseudo-inverse of the fourier transform matrix according to the time delay of each of the one or more time delay paths comprises:

wherein, L represents the total number of delay paths in the channel; n is a radical of _f The frequency point number representing the channel estimation; tau is _l The time delay of the L-th time delay path is shown, and the value range of L is 1 to L; f. of _k Represents the SRS pilot frequency resource frequency of the sounding reference signal, and the value range of k is 1 to N _f ；

a pseudo-inverse matrix representing the Fourier transform matrix.

7. The channel prediction method of claim 5, wherein the determining the first complex amplitude time series by maximum likelihood estimation based on the pseudo-inverse matrix and the multiple frequency domain channel estimation results comprises:

a pseudo-inverse matrix representing a Fourier transform matrix;

8. The channel prediction method of claim 1, wherein the determining the AR parameter for each delay path according to the first complex amplitude time series for each delay path comprises:

9. The channel prediction method of claim 8, wherein the setting of the iteration stop condition comprises one or more of:

the iteration times reach a preset expected AR order;

10. The channel prediction method of claim 1, wherein the historical channel estimation information comprises a plurality of frequency domain channel estimation results;

wherein the content of the first and second substances,

The complex amplitude of the second ith delay path;

the optimal prediction order of the first time delay path AR is represented;

representing the AR coefficients;

and a complex amplitude prediction value representing the first SRS time.

11. The channel prediction method of claim 1, wherein the historical channel estimation information comprises a plurality of frequency domain channel estimation results;

wherein the content of the first and second substances,

The complex amplitude of the second delay path;

the optimal prediction order of the first time delay path AR is shown;

a flip of an observation vector of the first complex amplitude time series representing the l-th delay path;

representing the AR coefficient;

a complex amplitude prediction value representing the first SRS time;

indicating an increase in the complex amplitude prediction value at the first SRS timeThen, turning the observation vector of another complex amplitude time sequence of the ith delay path;

and a complex amplitude prediction value representing the second SRS time.

12. The channel prediction method of claim 11, wherein the performing MMSE interpolation according to the first complex amplitude time sequence and the complex amplitude prediction value to obtain a second complex amplitude time sequence of each delay path after MMSE interpolation comprises:

a first complex amplitude time series representing the l-th delay path;

a complex amplitude prediction value representing the first SRS time;

a complex amplitude prediction value representing the second SRS time;

N _R representing the total number of user ports; n is a radical of hydrogen _T Representing the total number of base station antennas;

represents->

Transposing;

ω _MMSE representing a matrix of MMSE interpolation coefficients.

13. The channel prediction method according to claim 1 or 12, wherein the method further comprises:

N _x ＝p _max +2

N _y ＝(N _x -1)N _intp +1

14. The channel prediction method according to claim 1, 10 or 11, wherein the historical channel estimation information comprises a plurality of frequency domain channel estimation results;

15. The method according to claim 14, wherein said determining a channel prediction result according to the complex amplitude value in a set time period in the second complex amplitude time sequence comprises:

wherein, N _R Representing the total number of user ports; n is a radical of hydrogen _T Representing the total number of base station antennas; f represents a Fourier transform matrix;

16. A network device comprising a memory, a transceiver, a processor:

17. The network device of claim 16, wherein the historical channel estimation information comprises a plurality of frequency domain channel estimation results;

18. The network device of claim 17, wherein the determining one or more delay paths for channel prediction and a delay for each of the one or more delay paths according to the first frequency domain channel estimation result comprises:

19. The network device of claim 18, wherein the performing data conjugate rearrangement and moving average preprocessing on the first frequency domain channel estimation result to obtain a channel frequency correlation matrix comprises:

wherein N is _f Representing the frequency point number included in the first frequency domain channel estimation result; n is a radical of _win Represents the length of the sliding window; g is a radical of formula _j Representing a frequency domain channel estimation vector in a jth sliding window in the first frequency domain channel estimation result;

20. The network device of claim 16, wherein the historical channel estimation information comprises a plurality of times of frequency domain channel estimation results;

21. The network device of claim 20, wherein the generating a fourier transform matrix and the pseudo-inverse fourier transform matrix based on the delay of each of the one or more delay paths comprises:

wherein L represents the total number of delay paths in the channel；N _f The frequency point number representing the channel estimation; tau is _l The time delay of the L-th time delay path is shown, and the value range of L is 1 to L; f. of _k Represents the frequency of SRS pilot frequency resource, and the value range of k is 1 to N _f ；

a pseudo-inverse matrix representing the Fourier transform matrix.

22. The network device of claim 20, wherein determining the first complex amplitude time series using maximum likelihood estimation based on the pseudo-inverse matrix and the plurality of frequency domain channel estimation results comprises:

a pseudo-inverse matrix representing a Fourier transform matrix;

representing the mth base station antenna and the nth user in the multiple frequency domain channel estimation resultsEstimating the frequency domain channel corresponding to the sub-channel between the user ports;

23. The network device of claim 16, wherein the determining the AR parameter for each delay path according to the first complex amplitude time series for each delay path comprises:

24. The network device of claim 23, wherein the setting of the iteration stop condition comprises one or more of:

the iteration times reach a preset expected AR order;

25. The network device of claim 16, wherein the historical channel estimation information comprises a plurality of frequency domain channel estimation results;

The complex amplitude of the second ith delay path;

the optimal prediction order of the first time delay path AR is shown;

representing the AR coefficient; />

And a complex amplitude prediction value representing the first SRS time.

26. The network device of claim 16, wherein the historical channel estimation information comprises a plurality of times of frequency domain channel estimation results;

wherein the content of the first and second substances,

complex oscillation of the first delay path representing the first 1The previous 1 time represents the last time frequency domain channel estimation result in the multiple times of frequency domain channel estimation results;

The complex amplitude of the second delay path;

the optimal prediction order of the first time delay path AR is shown;

representing the AR coefficient;

a complex amplitude prediction value representing the first SRS time;

and a complex amplitude prediction value representing the second SRS time.

27. The network device of claim 26, wherein the performing MMSE interpolation according to the first complex amplitude time sequence and the complex amplitude prediction value to obtain a second complex amplitude time sequence of each delay path after MMSE interpolation comprises:

a first complex amplitude time series representing the l-th delay path;

a complex amplitude prediction value representing the first SRS time;

a complex amplitude prediction value representing the second SRS time;

represents->

Transposing;

ω _MMSE representing a matrix of MMSE interpolation coefficients.

28. The network device of claim 16 or 27, wherein the processor is further configured to:

N _x ＝p _max +2

N _y ＝(N _x -1)N _intp +1

with a representation dimension of N _y ×N _x A matrix of (a); omega _MMSE To representAnd (5) MMSE interpolation coefficient matrixes.

29. The network device of claim 16, 26 or 27, wherein the historical channel estimation information comprises a plurality of frequency domain channel estimation results;

the set time period comprises a time period between the SRS time corresponding to the last time frequency domain channel estimation result in the multiple times of frequency domain channel estimation results and the set time.

30. The network device of claim 29, wherein the determining a channel prediction result according to the complex amplitude value in the second complex amplitude time sequence within a set time period comprises:

31. A channel prediction apparatus, comprising:

a first determining unit, configured to determine one or more delay paths used for channel prediction and a delay of each of the one or more delay paths according to historical channel estimation information;

a second determining unit, configured to determine, according to the time delay of each of the one or more time delay paths and the historical channel estimation information, a first complex amplitude time series of each of the time delay paths;

32. A processor-readable storage medium, characterized in that the processor-readable storage medium stores a computer program for causing a processor to perform the method of any one of claims 1 to 15.