CN116743285A

CN116743285A - Channel prediction method, device, apparatus and storage medium

Info

Publication number: CN116743285A
Application number: CN202210194574.1A
Authority: CN
Inventors: 郑占旗; 刘龙; 朱理辰
Original assignee: Datang Mobile Communications Equipment Co Ltd
Current assignee: Datang Mobile Communications Equipment Co Ltd
Priority date: 2022-03-01
Filing date: 2022-03-01
Publication date: 2023-09-12

Abstract

The embodiment of the application provides a channel prediction method, device and apparatus and a storage medium, wherein the method comprises the following steps: channel estimation results of P+1 moments before the moment to be predicted of a plurality of antennas of the network equipment are obtained; according to the channel estimation results at the P+1 moments, determining the prediction coefficients of the P-order autoregressive AR channel prediction model; predicting a channel estimation result of a target antenna of the network equipment at a time to be predicted according to the prediction coefficient; wherein P is an integer greater than 1. According to the channel prediction method, device and apparatus and storage medium provided by the embodiment of the application, the AR prediction coefficient is solved by utilizing the historical channel estimation results of the plurality of antennas of the network equipment, so that the historical channel estimation data quantity required to be stored on each antenna can be reduced, the data storage pressure of the network equipment is lightened, meanwhile, the stability requirement of a shorter time sequence on a time-varying channel is greatly reduced, and the robustness of a channel prediction model is improved.

Description

Channel prediction method, device, apparatus and storage medium

Technical Field

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

Background

In beam forming, a network device (e.g., a base station) equivalently solves channels into a plurality of parallel transmission streams by adjusting the amplitude-phase gain of each array element of an antenna array, so that the multipath signals at the position of a target user can be superimposed and enhanced in phase, and the orthogonal mutual noninterference of each transmission stream is ensured. When the terminal moves, the phase of each propagation multipath changes in a short time, and the transmission flows cannot be kept orthogonal to each other at the position of the terminal, so that the interference between the flows is raised, and the transmission performance is reduced. At this time, the network device needs to calculate the shaping weight again according to the uplink channel estimation, so that each stream can return to the orthogonal state again. However, each time the network device performs uplink channel estimation and downlink shaping update, there is a certain time interval, and shaping performance in this interval is drastically reduced. The faster the channel variation, the more severely the shaping performance is affected.

Channel prediction based on sounding reference signal channel estimation measurement is one way to solve the above problem, in which Auto Regressive (AR) based channel prediction is a common channel prediction method, AR prediction regards channel variation as a random process, and a time filter can be used to predict the variation of the process, i.e. the channel is regarded as a time filter, and the output value at each moment is obtained from a set of historical values through linear weighting. When AR parameter calculation is performed on the time sequence of channel estimation, firstly, the time sequence needs to be constructed into a Toeplitz matrix, namely, a group of overdetermined equation sets is constructed, which often needs to acquire historical channel estimation data far larger than AR order, and channel parameters need to be kept unchanged in a time period corresponding to the historical channel estimation data, so that the storage space requirement on the historical channel estimation is huge, the data storage pressure of network equipment is increased, and the stability of a channel is higher.

Disclosure of Invention

Aiming at the problems existing in the prior art, the embodiment of the application provides a channel prediction method, device and apparatus and a storage medium.

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

channel estimation results of P+1 moments before the moment to be predicted of a plurality of antennas of the network equipment are obtained;

according to the channel estimation results of the P+1 moments, determining the prediction coefficients of a P-order autoregressive AR channel prediction model;

predicting a channel estimation result of a target antenna of the network equipment at the moment to be predicted according to the prediction coefficient;

wherein P is an integer greater than 1.

Optionally, the determining the prediction coefficient of the P-order AR channel prediction model according to the channel estimation results of the p+1 moments includes:

constructing a P-order overdetermined equation set taking the prediction coefficient as an unknown quantity according to the channel estimation results of the P+1 moments;

and solving the P-order overdetermined equation set to determine the prediction coefficient.

Optionally, in the P-order overdetermined equation set, the number of equations is equal to the number of antennas of the network device.

Optionally, the system of P-order overdetermined equations is:

Wherein h is _M，P Representing a channel estimation result of an Mth antenna of the network equipment at a P-th moment in P+1 moments, wherein M is the number of the antennas of the network equipment, and P is the P-th moment in P+1 moments before the moment to be predicted; the subscript sequence number (M, P) of h takes different values to represent channel estimation results of different antennas of the network equipment at different moments; omega ₁ 、ω ₂ 、...、ω _P And P prediction coefficients respectively representing the P-order AR channel prediction model.

Optionally, the solving the P-order overdetermined equation set to determine the prediction coefficient includes:

and solving the P-order overdetermined equation set based on a least square method to determine the prediction coefficient.

Optionally, the calculation formula of the prediction coefficient includes:

wherein omega ₁ 、ω ₂ 、...、ω _P P prediction coefficients respectively representing the P-order AR channel prediction model; h is a _M，P The channel estimation result of the Mth antenna of the network equipment at the P-th moment in the P+1 moments is represented, M is the number of the antennas of the network equipment, and P is the P-th moment in the P+1 moments before the moment to be predicted; the subscript sequence number (M, P) of h takes different values to represent channel estimation results of different antennas of the network equipment at different moments; the superscript H of the matrix represents the conjugate transpose and the superscript-1 represents the matrix inversion.

Optionally, the predicting, according to the prediction coefficient, a channel estimation result of the target antenna of the network device at the time to be predicted includes:

and predicting the channel estimation result of the target antenna of the network equipment at the moment to be predicted according to the prediction coefficient and the channel estimation results of the target antenna of the network equipment at P moments before the moment to be predicted.

Optionally, the predicting the channel estimation result of the target antenna of the network device at the time to be predicted according to the prediction coefficient and the channel estimation results of the target antenna of the network device at P times before the time to be predicted includes:

and carrying out linear weighted summation on the channel estimation results at the P moments based on the prediction coefficients to obtain a channel estimation prediction result of the target antenna of the network equipment at the moment to be predicted.

Optionally, the calculation formula of the channel estimation prediction result of the target antenna of the network device at the time to be predicted includes:

h _m，t ＝h _m，2 *ω ₁ +h _m，3 *ω ₂ +…+h _m，P+1 *ω _P

wherein h is _m，t Representing a channel estimation prediction result of a target antenna m of the network equipment at a time t to be predicted; h is a _m，2 、h _m，3 、…、h _m，P+1 Respectively representing channel estimation results of P times before the time to be predicted of a target antenna m of the network equipment; omega ₁ 、ω ₂ 、...、ω _P And P prediction coefficients respectively representing the P-order AR channel prediction model.

In a second aspect, an embodiment of the present application further provides an electronic device, including a memory, a transceiver, and a processor, where:

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:

wherein P is an integer greater than 1.

constructing a P-order overdetermined equation set taking the prediction coefficient as an unknown quantity according to the channel estimation results of the P+i moments;

Optionally, the system of P-order overdetermined equations is:

Optionally, the calculation formula of the prediction coefficient includes:

wherein omega ₁ 、ω ₂ 、...、ω _P P prediction coefficients respectively representing the P-order AR channel prediction model; h is a _M，P The channel estimation result of the Mth antenna of the network equipment at the P-th moment in the P+1 moments is represented, M is the number of the antennas of the network equipment, and P is the P-th moment in the P+1 moments before the moment to be predicted; subscript number of h (M P) taking different values to represent channel estimation results of different antennas of the network equipment at different moments; the superscript H of the matrix represents the conjugate transpose and the superscript-1 represents the matrix inversion.

h _m，t ＝h _m，2 *ω ₁ +h _m，3 *ω ₂ +…+h _m，P+1 *ω _P

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

an obtaining unit, configured to obtain channel estimation results of p+1 times before a time to be predicted for a plurality of antennas of a network device;

the determining unit is used for determining the prediction coefficient of the P-order autoregressive AR channel prediction model according to the channel estimation results at the P+1 moments;

the prediction unit is used for predicting a channel estimation result of the target antenna of the network equipment at the moment to be predicted according to the prediction coefficient;

wherein P is an integer greater than 1.

In a fourth aspect, embodiments of the present application further provide a computer readable storage medium storing a computer program for causing the processor to execute the steps of the channel prediction method according to the first aspect as described above.

In a fifth aspect, an embodiment of the present application further provides a communication device, where a computer program is stored, where the computer program is configured to cause the communication device to perform the steps of the channel prediction method according to the first aspect as described above.

In a sixth aspect, embodiments of the present application also provide a processor-readable storage medium storing a computer program for causing a processor to perform the steps of the channel prediction method according to the first aspect as described above.

In a seventh aspect, an embodiment of the present application further provides a chip product, where a computer program is stored, where the computer program is configured to cause the chip product to perform the steps of the channel prediction method according to the first aspect as described above.

According to the channel prediction method, device and apparatus and storage medium provided by the embodiment of the application, the AR prediction coefficient is solved jointly by utilizing the historical channel estimation results of the plurality of antennas of the network equipment, so that the historical channel estimation data quantity required to be stored on each antenna can be reduced, the data storage pressure of the network equipment is lightened, meanwhile, the stability requirement of a shorter time sequence on a time-varying channel is greatly reduced, and the robustness of a channel prediction model is improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the related art, the drawings that are required to be used in the embodiments or the related technical descriptions will be briefly described, and it is apparent that the drawings in the following descriptions are some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for those skilled in the art.

FIG. 1 is a schematic diagram of a fourth-order AR equation set construction provided by the related art;

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

FIG. 3 is a schematic diagram of a fourth-order overdetermined equation set construction provided by an embodiment of the present application;

fig. 4 is a schematic diagram of an implementation of a channel prediction method according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of a channel prediction apparatus according to an embodiment of the present application.

Detailed Description

In the embodiment of the application, the term "and/or" describes the association relation of the association objects, which means that three relations can exist, for example, a and/or B can be expressed as follows: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.

The term "plurality" in embodiments of the present application means two or more, and other adjectives are similar.

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Aiming at the problems that the traditional AR channel prediction method has high data storage pressure on network equipment and the channel parameters are difficult to be kept unchanged in a storage Time period in an actual scene, the embodiments of the application provide an optimized AR channel prediction method, namely a Space Time (STAR) algorithm. In order to facilitate a clearer understanding of the embodiments of the present application, some related technical knowledge will be described first.

AR-based channel prediction is a common method of channel prediction, which regards channel variation as a random process, and can use a time filter to predict the variation of the process, i.e., the channel as a time filter, and the output value at each moment is obtained from a set of historical values by linear weighting. When AR parameter calculation is carried out on the time sequence of the channel, the time sequence is firstly required to be constructed into a Toeplitz matrix, namely, a group of overdetermined equation sets are constructed, and then the equation sets are solved by using a least square idea to obtain AR coefficients.

Taking fourth-order AR prediction as an example, FIG. 1 is a schematic diagram of a fourth-order AR equation set construction provided by the related art, as shown in FIG. 1, wherein x is ₁ ，x ₂ ，...，x ₇₀ Representing a stored historical channel estimation time domain sequence, and subscript sequence number representing channel estimation measured at different moments, w ₁ ，w ₂ ，w ₃ ，w ₄ The AR prediction coefficients are parameters to be solved for AR prediction, and future channel estimates can be predicted based on the parameters and historical channel estimates.

However, in order to solve the unknown coefficients (i.e., the prediction coefficients) in the AR model, the conventional method needs to construct the number of equations far greater than the AR order, and an overdetermined equation is formed, so that a relatively accurate channel prediction result can be obtained. The method has huge storage space requirement on historical channel estimation, channel parameters are required to be kept unchanged in a storage time period, the method is difficult to guarantee in an actual scene, and the data storage pressure on a base station is high.

The STAR algorithm of the embodiment of the application is based on the far field effect of channel estimation of a multiple-input multiple-output (Multi Input Multi Output, MIMO) system, the channel structure seen by each antenna on a base station has the same spatial characteristics, the Doppler component of each path of the space is the same, and the time sequence of the channel estimation on each antenna can be considered to meet the same AR model. The basic idea of classical AR prediction is to artificially construct a set of AR equations of sufficient number to form an overdetermined set of equations, and solve the AR coefficients, thereby predicting the channel estimate at the future time. From the MIMO antenna system, the multiple antennas at the base station end originally see the same multipath structure and doppler components, for example, 64 groups of channel estimation sequences meeting the same channel model exist in the base station antenna with 64TR, so that for P-order AR prediction, only channel estimation time sequences at p+1 detection moments can be stored, an AR equation set consisting of 64 equations is constructed on the 64 antennas, AR coefficients are further solved based on least squares, for example, the conventional 4-order AR needs to store time sequences of about 70 groups of historical channel estimation, and the 4-order STAR algorithm only needs to store 5 groups of historical sequences, so that the data storage pressure of the base station is greatly reduced.

Fig. 2 is a flow chart of a channel prediction method according to an embodiment of the present application, as shown in fig. 2, the method includes the following steps:

step 200, obtaining channel estimation results of P+1 times before the time to be predicted of a plurality of antennas of the network equipment; wherein P is an integer greater than 1.

Specifically, the network device may be a base station, or may be other radio access network devices. When predicting the channel estimation result at a certain moment, the channel estimation results at p+1 moments before the moment of the multiple antennas of the network device can be obtained first.

The value of P may be obtained by conventional methods, such as minimum information content criteria or other means, without limitation. P may be an integer greater than 1, for example P equals 2 or 3 or 4, etc.

In the embodiments of the present application, only the channel estimation result for one port is taken as an example for prediction, and correspondingly, the channel estimation results of p+1 times before the time to be predicted of the multiple antennas of the network device are also the channel estimation results for the one port. The ports refer to antenna ports of the terminals. It can be appreciated that the method provided by the embodiments of the present application may be used to predict channel estimation for any one of the antenna ports of the terminal.

For example, assuming that in the MIMO system, the number of antennas of the base station is M (typically M is a larger number in the macro station, e.g. 32 or 64, which is much larger than the order P of channel prediction), and the number of antennas of the terminal is N, the channel estimation result CH at the P-th time of the terminal _p P epsilon (1-P) is a matrix of M x N, and elements in the matrix represent channel estimation results of different antennas of the base station on different antenna ports of the terminal at the P-th moment. Taking the case of predicting the channel estimation result of the first port as an example, the first column may be taken out from the channel estimation CH matrices in m×n dimensions corresponding to a plurality of different times for prediction.

Step 201, determining a prediction coefficient of a P-order autoregressive AR channel prediction model according to channel estimation results at p+1 times.

Specifically, after obtaining channel estimation results of p+1 times before the time to be predicted of the multiple antennas of the network device in the previous step, a prediction coefficient of the P-order autoregressive AR channel prediction model may be determined according to the channel estimation results of p+1 times. How the prediction coefficients are solved in particular will be explained further in the following steps.

For example, the prediction coefficients of the 4-order autoregressive AR channel prediction model can be determined by acquiring channel estimation results of a plurality of antennas of the network device at 5 times before the time to be predicted.

Step 202, predicting a channel estimation result of a target antenna of the network device at a time to be predicted according to the prediction coefficient.

Specifically, after determining the prediction coefficient of the P-order autoregressive AR channel prediction model, the P-order AR channel prediction model may be used to predict the channel estimation result of the target antenna of the network device at the time to be predicted according to the prediction coefficient.

The target antenna may be one or more antennas among the plurality of antennas, or may be an antenna different from the plurality of antennas. For example, the number of antennas of the network device is 64, and assuming that the plurality of antennas is 1 st to 60 th antennas among the 64 antennas, the target antenna may be one or more of the 4 antennas other than the 60 antennas, or may be one or more of the 60 antennas. The target antennas may also be all antennas of the network device, that is, after determining the prediction coefficients, the channel estimation results of all antennas of the network device at the time to be predicted may be predicted.

According to the channel prediction method provided by the embodiment of the application, the AR prediction coefficient is solved jointly by utilizing the historical channel estimation results of the plurality of antennas of the network equipment, so that the historical channel estimation data quantity required to be stored on each antenna can be reduced, the data storage pressure of the network equipment is lightened, meanwhile, the stability requirement of a shorter time sequence on a time-varying channel is greatly reduced, and the robustness of a channel prediction model is improved.

Optionally, determining the prediction coefficient of the P-order AR channel prediction model according to the channel estimation results of the p+1 moments includes:

according to the channel estimation results at the P+1 moments, constructing a P-order overdetermined equation set taking a prediction coefficient as an unknown quantity;

Specifically, in the embodiment of the present application, a P-order overdetermined equation set with a prediction coefficient as an unknown quantity may be constructed according to channel estimation results at p+1 times, and then the P-order overdetermined equation set is solved to determine the prediction coefficient.

The number of the equations in the P-order overdetermined equation set can be flexibly set, for example, the 4-order overdetermined equation set can be used for constructing equations with the number of 20, 30, 40, 64 or the like, and theoretically, the overdetermined equation set can be formed.

The P-order overdetermined equation set taking the prediction coefficient as an unknown quantity is constructed, and then the P-order overdetermined equation set is solved to determine the prediction coefficient, so that the prediction coefficient obtained by solving is more accurate, and the accuracy of a channel estimation prediction result is improved.

Optionally, in the P-order overdetermined equation set, the number of equations may be equal to the number of antennas of the network device, so that accuracy of the channel estimation prediction result may be better ensured by forming the overdetermined equation set by using as many equation numbers as possible.

In a possible implementation manner, the channel estimation results of p+1 historical moments stored on the current ports on the M antennas of the network device are recorded as follows:

wherein h is _M，P The channel estimation result of the Mth antenna of the network equipment at the P-th moment in P+1 moments is shown, M is the number of the antennas of the network equipment, and P is the P-th moment in P+1 moments before the moment to be predicted; the subscript sequence number (M, P) of h takes different values to represent channel estimation results of different antennas of the network equipment at different moments; each column in the M (P+1) dimensional matrix is represented by a different subscript H matrix, e.g. H ₁ Representing the first column in the M x (p+1) dimensional matrix described above.

Alternatively, the system of P-th order overdetermined equations may be:

wherein h is _M，P The channel estimation result of the Mth antenna of the network equipment at the P-th moment in the P+1 moments is shown, M is the number of the antennas of the network equipment, and P is the P-th moment in the P+1 moments before the moment to be predicted; the subscript sequence number (M, P) of h takes different values to represent channel estimation results of different antennas of the network equipment at different moments; omega ₁ 、ω ₂ 、...、ω _P And P prediction coefficients respectively representing the P-order AR channel prediction model.

FIG. 3 is a schematic diagram of a fourth-order overdetermined equation set, as shown in FIG. 3, in which 4-order STAR is taken as an example to construct an overdetermined equation set, wherein w ₁ ～w ₄ Representing STARIs used for the prediction coefficients of (a).

Experience shows that the order P of AR is typically smaller than the number M of antennas of the network device, so the P-th equation set constructed above is an overdetermined equation set, which can be abbreviated as:

namely:

Hω＝H _P+1

here, bold H indicates P historical time channel estimation results H ₁ ，H ₂ ，...H _P The matrix is formed, with bold omega representing omega ₁ 、ω ₂ 、...、ω _P A matrix is formed.

After the P-order overdetermined equation set is constructed, the P-order overdetermined equation set can be solved, and the prediction coefficient is determined. The method for solving the P-order overdetermined equation set is not particularly limited herein, as long as the prediction coefficients can be solved.

Optionally, solving the P-order overdetermined equation set to determine the prediction coefficient may include: and solving the P-order overdetermined equation set based on the least square method to determine the prediction coefficient.

Specifically, the system of P-th order overdetermined equations may be solved based on a least squares method to determine the prediction coefficients.

For example, the above-mentioned overdetermined equation set hω=h _p+1 The least squares solution of (2) is:

ω＝(H ^H H) ^-1 H ^H H _P+1

wherein the superscript H of the matrix represents the conjugate transpose and the superscript-1 represents the matrix inversion.

In the embodiment of the application, the P-order overdetermined equation set can be solved based on the least square method, so that the unknown prediction coefficient can be simply and conveniently obtained.

Alternatively, the calculation formula of the prediction coefficient may include:

wherein omega ₁ 、ω ₂ 、...、ω _P P prediction coefficients respectively representing a P-order AR channel prediction model; h is a _M，P The channel estimation result of the Mth antenna of the network equipment at the P-th moment in P+1 moments is shown, M is the number of the antennas of the network equipment, and P is the P-th moment in P+1 moments before the moment to be predicted; the subscript sequence number (M, P) of h takes different values to represent channel estimation results of different antennas of the network equipment at different moments; the superscript H of the matrix represents the conjugate transpose and the superscript-1 represents the matrix inversion.

Specifically, in the embodiment of the application, the prediction coefficient can be obtained by constructing a P-order overdetermined equation set to solve the formula, or can be directly calculated and obtained according to the formula according to P+1 channel estimation results of a plurality of antennas of the network equipment before the time to be predicted, so that the solution of the prediction coefficient is faster and more accurate.

Optionally, predicting, according to the prediction coefficient, a channel estimation result of a target antenna of the network device at a time to be predicted, including: and predicting the channel estimation results of the target antenna of the network equipment at the time to be predicted according to the prediction coefficients and the channel estimation results of the target antenna of the network equipment at P times before the time to be predicted.

Specifically, in the embodiment of the application, after the prediction coefficient is determined, the channel estimation results of the target antenna of the network equipment at the time to be predicted can be predicted according to the prediction coefficient and the channel estimation results of the target antenna of the network equipment at P times before the time to be predicted, so that the storage requirement of the channel prediction data quantity is reduced, the timeliness of channel prediction is improved, and the method is favorable for coping with the channel prediction scene with rich changes.

Optionally, predicting the channel estimation result of the target antenna of the network device at the time to be predicted according to the prediction coefficient and the channel estimation results of the target antenna of the network device at P times before the time to be predicted, including: and carrying out linear weighted summation on the channel estimation results at P moments based on the prediction coefficients to obtain a channel estimation prediction result of the target antenna of the network equipment at the moment to be predicted.

Specifically, the channel estimation results of the target antenna at P times before the time to be predicted may be linearly weighted and summed according to the prediction coefficient. For example, the prediction coefficient or the correlation coefficient determined according to the prediction coefficient may be used as a weight coefficient, and the channel estimation results at the P times may be linearly weighted and summed to obtain the channel estimation prediction result of the target antenna of the network device at the time to be predicted.

Alternatively, the calculation formula of the channel estimation prediction result of the target antenna of the network device at the time to be predicted may include:

h _m，t ＝h _m，2 *ω ₁ +h _m，3 *ω ₂ +…+h _m，P+1 *ω _P

wherein h is _m，t The channel estimation prediction result of the target antenna m of the network equipment at the time t to be predicted is represented; h is a _m，2 、h _m，3 、…、h _m，P+1 Channel estimation results of P times before the time to be predicted of a target antenna m of the network equipment are respectively represented; omega ₁ 、ω ₂ 、...、ω _P And P prediction coefficients respectively representing the P-order AR channel prediction model.

Specifically, taking the prediction coefficient matrix ω obtained by the above solution as an example, for the stored channel estimation results [ H ] at p+1 times before the time to be predicted ₁ ，H ₂ ，...H _p ，H _P+1 ]Can take out the P [ H ] ₂ ，...H _p ，H _P+1 ]Calculating the predicted coefficient obtained by the solution, and predicting the channel estimation H at the moment to be predicted _P+2 The method comprises the following steps:

H _P+2 ＝[H ₂ ，...H _p ，...，H _P+1 ]ω

fig. 4 shows a channel according to an embodiment of the present applicationAs shown in fig. 4, the following describes the implementation procedure of the channel prediction method: firstly, obtaining channel estimation results CH of P+1 time before to-be-predicted time of multiple antennas of network equipment ₁ ，CH ₂ ，...，CH _P+1 Wherein the channel estimation result at any time p is CH _p A representation; then, constructing a P-order STAR model equation set (containing M equations), solving the equation set by using a least square method, and calculating to obtain a STAR prediction coefficient; finally, based on the solved STAR prediction coefficients and the channel estimation results at the 2 nd to P+1 th moments, the channel estimation result CH at the P+2 th moment is predicted _P+2 Channel estimation results CH at future times (e.g., p+3 time, p+4 time, p+5 time, etc.) _P+3 ，CH _P+4 ，CH _P+5 ,. the calculation can be analogized.

After predicting the channel estimation result at the time to be predicted, the network device can perform subsequent operations such as beamforming, channel precoding and the like.

It should be noted that, in the embodiments of the present application, only the channel prediction is performed for one frequency point in the channel estimation, and those skilled in the art should understand that other frequency points are the same, and the same channel prediction process may also be performed.

The method and the device provided by the embodiments of the present application are based on the same application conception, and because the principles of solving the problems by the method and the device are similar, the implementation of the device and the method can be referred to each other, and the repetition is not repeated.

Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 5, the electronic device includes a memory 520, a transceiver 510, and a processor 500; wherein the processor 500 and the memory 520 may also be physically separate.

A memory 520 for storing a computer program; a transceiver 510 for transceiving data under the control of the processor 500.

In particular, the transceiver 510 is used to receive and transmit data under the control of the processor 500.

Wherein in fig. 5, a bus architecture may comprise any number of interconnected buses and bridges, and in particular one or more processors represented by processor 500 and various circuits of memory represented by memory 520, linked together. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., all as are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. The transceiver 510 may be a number of elements, including a transmitter and a receiver, providing a means for communicating with various other apparatus over transmission media, including wireless channels, wired channels, optical cables, and the like. The processor 500 is responsible for managing the bus architecture and general processing, and the memory 520 may store data used by the processor 500 in performing operations.

The processor 500 may be a central processing unit (Central Processing Unit, CPU), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a Field programmable gate array (Field-Programmable Gate Array, FPGA), or a complex programmable logic device (Complex Programmable Logic Device, CPLD), or the processor may employ a multi-core architecture.

The processor 500 is configured to execute any of the methods provided by the embodiments of the present application according to the obtained executable instructions by calling a computer program stored in the memory 520, for example: channel estimation results of P+1 moments before the moment to be predicted of a plurality of antennas of the network equipment are obtained; according to the channel estimation results at the P+1 moments, determining the prediction coefficients of the P-order autoregressive AR channel prediction model; predicting a channel estimation result of a target antenna of the network equipment at a time to be predicted according to the prediction coefficient; wherein P is an integer greater than 1.

and solving the P-order overdetermined equation set to determine a prediction coefficient.

Optionally, the system of P-th order overdetermined equations is:

wherein h is _M，P The channel estimation result of the Mth antenna of the network equipment at the P-th moment in P+1 moments is shown, M is the number of the antennas of the network equipment, and P is the P-th moment in P+1 moments before the moment to be predicted; the subscript sequence number (M, P) of h takes different values to represent channel estimation results of different antennas of the network equipment at different moments; omega ₁ 、ω ₂ 、...、ω _P And P prediction coefficients respectively representing the P-order AR channel prediction model.

Optionally, solving the P-order overdetermined equation set to determine a prediction coefficient includes: and solving the P-order overdetermined equation set based on the least square method to determine the prediction coefficient.

Optionally, the calculation formula of the prediction coefficient includes:

wherein omega ₁ 、ω ₂ 、...、ω _P P prediction coefficients respectively representing a P-order AR channel prediction model; h is a _M，P The channel estimation result of the Mth antenna of the network equipment at the P-th moment in P+1 moments is shown, M is the number of the antennas of the network equipment, and P is the P-th moment in P+1 moments before the moment to be predicted; the subscript sequence number (M, P) of h takes different values to represent channel estimation results of different antennas of the network equipment at different moments; the superscript H of the matrix represents the conjugate transpose and the superscript-1 represents the matrix inversion。

Optionally, predicting, according to the prediction coefficient, a channel estimation result of a target antenna of the network device at a time to be predicted, including:

and predicting the channel estimation results of the target antenna of the network equipment at the time to be predicted according to the prediction coefficients and the channel estimation results of the target antenna of the network equipment at P times before the time to be predicted.

h _m，t ＝h _m，2 *ω ₁ +h _m，3 *ω ₂ +…+h _m，P+1 *ω _P

It should be noted that, the electronic device provided in the embodiment of the present application can implement all the method steps implemented in the method embodiment and achieve the same technical effects, and the parts and beneficial effects that are the same as those of the method embodiment in the embodiment are not described in detail herein.

Fig. 6 is a schematic structural diagram of a channel prediction apparatus according to an embodiment of the present application, as shown in fig. 6, where the apparatus includes:

an obtaining unit 600, configured to obtain channel estimation results of p+1 times before a time to be predicted for a plurality of antennas of a network device;

a determining unit 610, configured to determine a prediction coefficient of the P-order autoregressive AR channel prediction model according to channel estimation results at p+1 times;

a prediction unit 620, configured to predict a channel estimation result of a target antenna of the network device at a time to be predicted according to the prediction coefficient;

wherein P is an integer greater than 1.

Optionally, the system of P-th order overdetermined equations is:

Optionally, solving the P-order overdetermined equation set to determine the prediction coefficient includes:

and solving the P-order overdetermined equation set based on the least square method to determine the prediction coefficient.

Optionally, the calculation formula of the prediction coefficient includes:

Optionally, predicting the channel estimation result of the target antenna of the network device at the time to be predicted according to the prediction coefficient and the channel estimation results of the target antenna of the network device at P times before the time to be predicted, including:

and carrying out linear weighted summation on the channel estimation results at P moments based on the prediction coefficients to obtain a channel estimation prediction result of the target antenna of the network equipment at the moment to be predicted.

h _m，t ＝h _m，2 *ω ₁ +h _m，3 *ω ₂ +…+h _m，P+1 *ω _P

It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice. In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a processor-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

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

In another aspect, embodiments of the present application further provide a computer-readable storage medium storing a computer program for causing a computer to execute the channel prediction method provided in each of the above embodiments.

It should be noted that, the computer readable storage medium provided in the embodiment of the present application can implement all the method steps implemented in the above method embodiment and achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those in the method embodiment in this embodiment are omitted.

The computer-readable storage medium can be any available medium or data storage device that can be accessed by a computer, including, but not limited to, magnetic storage (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical storage (e.g., CD, DVD, BD, HVD, etc.), and semiconductor storage (e.g., ROM, EPROM, EEPROM, nonvolatile storage (NAND FLASH), solid State Disk (SSD)), etc.

The technical scheme provided by the embodiment of the application can be suitable for various systems, in particular to a 5G system. For example, suitable systems may be global system for mobile communications (global system of mobile communication, GSM), code division multiple access (code division multiple access, CDMA), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA) universal packet Radio service (general packet Radio service, GPRS), long term evolution (long term evolution, LTE), LTE frequency division duplex (frequency division duplex, FDD), LTE time division duplex (time division duplex, TDD), long term evolution-advanced (long term evolution advanced, LTE-a), universal mobile system (universal mobile telecommunication system, UMTS), worldwide interoperability for microwave access (worldwide interoperability for microwave access, wiMAX), 5G New air interface (New Radio, NR), and the like. Terminal devices and network devices are included in these various systems. Core network parts such as evolved packet system (Evloved Packet System, EPS), 5G system (5 GS) etc. may also be included in the system.

The network device according to the embodiment of the present application may be a base station, where the base station may include a plurality of cells for providing services for the terminal. A base station may also be called an access point or may be a device in an access network that communicates over the air-interface, through one or more sectors, with wireless terminal devices, or other names, depending on the particular application. The network device may be operable to exchange received air frames with internet protocol (Internet Protocol, IP) packets as a router between the wireless terminal device and the rest of the access network, which may include an Internet Protocol (IP) communication 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 network device (Base Transceiver Station, BTS) in a global system for mobile communications (Global System for Mobile communications, GSM) or code division multiple access (Code Division Multiple Access, CDMA), a network device (NodeB) in a wideband code division multiple access (Wide-band Code Division Multiple Access, WCDMA), an evolved network device (evolutional Node B, eNB or e-NodeB) in a long term evolution (long term evolution, LTE) system, a 5G base station (gNB) in a 5G network architecture (next generation system), a home evolved base station (Home evolved Node B, heNB), a relay node (relay node), a home base station (femto), a pico base station (pico), etc., which are not limited in the embodiment of the present application. In some network structures, the network device may include a Centralized Unit (CU) node and a Distributed Unit (DU) node, which may also be geographically separated.

The network device may use Multiple antennas for Multiple-input Multiple-output (Multi Input Multi Output, MIMO) transmission, which may be Single-User MIMO (SU-MIMO) or multi-User MIMO (MU-MIMO). The MIMO transmission may be 2D-MIMO, 3D-MIMO, FD-MIMO, or massive-MIMO, or may be diversity transmission, precoding transmission, beamforming transmission, or the like, depending on the form and number of the root antenna combinations.

It will be appreciated by those skilled in the art that 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, magnetic 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 modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. A method of channel prediction, comprising:

wherein P is an integer greater than 1.

2. The channel prediction method according to claim 1, wherein determining the prediction coefficients of the P-order AR channel prediction model according to the channel estimation results of the p+1 time instants comprises:

3. The channel prediction method according to claim 2, wherein the number of equations in the P-th order overdetermined equation set is equal to the number of antennas of the network device.

4. A channel prediction method according to claim 3, wherein the system of P-th order overdetermined equations is:

Wherein h is _M,P Representing a channel estimation result of an Mth antenna of the network equipment at a P-th moment in P+1 moments, wherein M is the number of the antennas of the network equipment, and P is the P-th moment in P+1 moments before the moment to be predicted; the subscript sequence number (M, P) of h takes different values to represent channel estimation results of different antennas of the network equipment at different moments; omega ₁ 、ω ₂ 、…、ω _P And P prediction coefficients respectively representing the P-order AR channel prediction model.

5. The channel prediction method according to claim 2, wherein said solving the system of P-th order overdetermined equations to determine the prediction coefficients comprises:

6. The channel prediction method according to any one of claims 1 to 5, wherein the calculation formula of the prediction coefficient includes:

wherein omega ₁ 、ω ₂ 、…、ω _P P prediction coefficients respectively representing the P-order AR channel prediction model; h is a _M,P The channel estimation result of the Mth antenna of the network equipment at the P-th moment in the P+1 moments is represented, M is the number of the antennas of the network equipment, and P is the P-th moment in the P+1 moments before the moment to be predicted; the subscript sequence number (M, P) of h takes different values to represent the network configuration Channel estimation results of different antennas at different moments are prepared; the superscript H of the matrix represents the conjugate transpose and the superscript-1 represents the matrix inversion.

7. The channel prediction method according to claim 1, wherein predicting the channel estimation result of the target antenna of the network device at the time to be predicted according to the prediction coefficient includes:

8. The channel prediction method according to claim 7, wherein predicting the channel estimation result of the target antenna of the network device at the time to be predicted according to the prediction coefficient and the channel estimation results of the target antenna of the network device at P times before the time to be predicted, comprises:

9. The channel prediction method according to claim 8, wherein the calculation formula of the channel estimation prediction result of the target antenna of the network device at the time to be predicted includes:

h _m，t ＝h _m，2 *ω ₁ +h _m，3 *ω ₂ +…+h _m，P+1 *ω _P

10. An electronic device comprising a memory, a transceiver, and a processor:

Wherein P is an integer greater than 1.

11. The electronic device of claim 10, wherein the determining the prediction coefficients of the P-order AR channel prediction model according to the channel estimation results of the p+1 time instants comprises:

12. The electronic device of claim 11, wherein the number of equations in the set of P-th order overdetermined equations is equal to the number of antennas of the network device.

13. The electronic device of claim 12, wherein the set of P-th order overdetermined equations is:

14. The electronic device of claim 11, wherein solving the system of P-th order overdetermined equations to determine the prediction coefficients comprises:

15. The electronic device of any one of claims 10 to 14, wherein the calculation formula of the prediction coefficient includes:

wherein omega ₁ 、ω ₂ 、...、ω _P P prediction coefficients respectively representing the P-order AR channel prediction model; h is a _M，P The channel estimation result of the Mth antenna of the network equipment at the P-th moment in the P+1 moments is represented, M is the number of the antennas of the network equipment, and P is the P-th moment in the P+1 moments before the moment to be predicted; the subscript sequence number (M, P) of h takes different values to represent the networkChannel estimation results of different antennas of the device at different moments; the superscript H of the matrix represents the conjugate transpose and the superscript-1 represents the matrix inversion.

16. The electronic device of claim 10, wherein predicting the channel estimation result of the target antenna of the network device at the time to be predicted according to the prediction coefficient comprises:

17. The electronic device of claim 16, wherein predicting the channel estimation result of the target antenna of the network device at the time to be predicted based on the prediction coefficients and the channel estimation results of the target antenna of the network device at P times before the time to be predicted comprises:

18. The electronic device of claim 17, wherein the calculation formula of the channel estimation prediction result of the target antenna of the network device at the time to be predicted comprises:

h _m，t ＝h _m，2 *ω ₁ +h _m，3 *ω ₂ +…+h _m，P+1 *ω _P

19. A channel prediction apparatus, the apparatus comprising:

wherein P is an integer greater than 1.

20. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for causing a computer to execute the method of any one of claims 1 to 9.