CN115706684B

CN115706684B - Channel parameter estimation method, device and storage medium

Info

Publication number: CN115706684B
Application number: CN202110887438.6A
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: 2021-08-03
Filing date: 2021-08-03
Publication date: 2024-06-07
Anticipated expiration: 2041-08-03
Also published as: CN115706684A

Abstract

The disclosure provides a method, a device and a storage medium for estimating channel parameters, wherein the method comprises the following steps: the method comprises the steps of obtaining a target sampling signal and a reference signal subspace matrix, carrying out signal subspace tracking on the target sampling signal according to the reference signal subspace matrix to obtain a target signal subspace matrix corresponding to the target sampling signal, determining a tracking spectrum matrix corresponding to the target sampling signal according to the target signal subspace matrix and the reference signal subspace matrix, and obtaining target channel parameters according to the tracking spectrum matrix, so that complexity of wireless channel parameter estimation can be effectively reduced, real-time processing requirements of a time-varying channel are met, and accordingly parameter estimation efficiency of a wireless channel is improved.

Description

Channel parameter estimation method, device and storage medium

Technical Field

The disclosure relates to the field of communication technologies, and in particular, to a method and a device for estimating channel parameters, and a storage medium.

Background

The time-varying channel parameter estimation may be applied in many aspects of the fifth generation mobile communication technology (5th Generation Mobile Communication Technology,5G), for example: for time-varying Multiple-User Multiple-Input Multiple-Output (MU-MIMO) channel prediction, continuous estimation of channel multipath delay and doppler is often required; in addition, the time-varying channel parameter estimation can be applied to the service of continuously estimating the angle of the mobile user, so as to realize high-precision positioning of the mobile user, estimation of the motion speed of the user and the like.

Under the 5G multi-user-MIMO communication scene, after receiving the uplink SRS pilot signal and completing channel estimation, the base station needs to repeatedly perform channel parameter estimation on the uplink channel estimation aiming at the time-varying channel, so as to acquire the change of the channel parameter in the time dimension. In the related art, common algorithms for channel parameters include multiple signal classification (Multiple Signal Classification, MUSIC), space-time-shift-ALTERNATING GENERALIZED EXPECTATION-maximization (SAGE), and twiddle factor invariant (ESTIMATING SIGNAL PARAMETERS VIA Rotational Invariance Techniques, ESPRIT) algorithms, which are all relatively complex and difficult to apply to real-time processing.

Disclosure of Invention

To this end, the present disclosure aims to provide a method, a device and a storage medium for estimating channel parameters.

To achieve the above object, a method for estimating channel parameters according to an embodiment of a first aspect of the present disclosure includes: acquiring a target sampling signal and a reference signal subspace matrix, wherein the reference signal subspace matrix is a signal subspace matrix corresponding to a previous sampling signal of the target sampling signal; carrying out signal subspace tracking on the target sampling signal according to the reference signal subspace matrix to obtain a target signal subspace matrix corresponding to the target sampling signal; determining a tracking spectrum matrix corresponding to the target sampling signal according to the target signal subspace matrix and the reference signal subspace matrix; and obtaining the target channel parameters according to the tracking spectrum matrix.

In some embodiments of the present disclosure, performing signal subspace tracking on a target sampling signal according to a reference signal subspace matrix to obtain a target signal subspace matrix corresponding to the target sampling signal, including: determining subspace tracking parameters corresponding to the reference signal subspace matrix according to the target sampling signals; and obtaining a target signal subspace matrix according to the subspace tracking parameters and the reference signal subspace matrix.

In some embodiments of the present disclosure, determining subspace tracking parameters corresponding to a reference signal subspace matrix from a target sample signal comprises: projecting the target sampling signal into a reference signal subspace to obtain a projection signal; and determining a signal error between the target sampling signal and the projection signal, and generating an error representation vector corresponding to the signal error, wherein the error representation vector is used as a subspace tracking parameter.

In some embodiments of the present disclosure, determining a subspace tracking parameter corresponding to a reference signal subspace matrix from a target sample signal further comprises: determining a plurality of historical sampling signals associated with the target sampling signal; determining a target compression vector corresponding to the target sampling signal, and determining a plurality of historical compression vectors corresponding to the plurality of historical sampling signals respectively; determining reference processing data according to a plurality of historical compression vectors and preset forgetting parameters; determining target processing data according to the target compression vector, a plurality of historical compression vectors and preset forgetting parameters; channel estimation is carried out in a target signal subspace to obtain a channel impulse response estimated value corresponding to the target signal subspace, wherein the target signal subspace is a signal subspace corresponding to a target sampling signal; and processing the target processing data, the reference processing data and the preset forgetting parameters according to the channel impulse response estimated value, and taking the processed result value and the error expression vector as subspace tracking parameters.

In some embodiments of the present disclosure, determining a tracking spectrum matrix corresponding to a target sampled signal from a target signal subspace matrix and a reference signal subspace matrix includes: performing matrix decomposition on the reference signal subspace matrix to obtain a first reference submatrix and a second reference submatrix; mapping the first reference sub-matrix and the second reference sub-matrix by adopting a target data processing method to obtain a first error sub-representation vector corresponding to the first reference sub-matrix and a second error sub-representation vector corresponding to the second reference sub-matrix; determining an initial reference spectrum matrix corresponding to the reference signal subspace matrix; processing the initial reference spectrum matrix according to the first error sub-expression vector and the second error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix; performing matrix decomposition on the target signal subspace matrix to obtain a third reference submatrix; and generating a tracking spectrum matrix according to the target spectrum matrix and the third reference submatrix.

In some embodiments of the present disclosure, after obtaining the first error sub-representation vector corresponding to the first reference sub-matrix and the second error sub-representation vector corresponding to the second reference sub-matrix, further comprising: optimizing the first error sub-representation vector by adopting a first reference sub-matrix to obtain a third error sub-representation vector; optimizing the second error sub-representation vector by adopting a second reference sub-matrix to obtain a fourth error sub-representation vector; the method for processing the initial reference spectrum matrix according to the first error sub-expression vector and the second error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix comprises the following steps: and carrying out iterative processing on the initial reference spectrum matrix according to the first error sub-expression vector, the second error sub-expression vector, the third error sub-expression vector and the fourth error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix.

In some embodiments of the present disclosure, obtaining the target channel parameters from the tracking spectrum matrix includes: performing feature analysis on the tracking spectrum matrix to obtain matrix features corresponding to the tracking spectrum matrix; and determining the target channel parameters according to the matrix characteristics.

In some embodiments of the present disclosure, obtaining a target sampling signal includes: determining a historical compression vector corresponding to a previous sampling signal of the target sampling signal and a feature vector corresponding to the previous sampling signal; and determining a target sampling signal according to the previous sampling signal, the characteristic vector and the corresponding historical compression vector.

In some embodiments of the present disclosure, determining a feature vector corresponding to a previous sampled signal includes: determining an initial feature vector and an initial feature value corresponding to an initial sampling signal, wherein the initial sampling signal is a sampling signal corresponding to the earliest time point in a plurality of time points respectively corresponding to a plurality of historical sampling signals; determining a plurality of historical compression vectors respectively corresponding to the plurality of historical sampling signals; sequentially carrying out iterative processing on a plurality of historical compression vectors by referring to a plurality of time points by adopting an initial feature vector and an initial feature value to obtain a feature vector corresponding to a previous compression vector, wherein the previous compression vector corresponds to a previous sampling signal; the feature vector corresponding to the previous compressed vector is taken as the feature vector corresponding to the previous sampled signal.

To achieve the above object, an apparatus for estimating channel parameters according to an embodiment of a second aspect of the present disclosure includes: the acquisition unit is used for acquiring a target sampling signal and a reference signal subspace matrix, wherein the reference signal subspace matrix is a signal subspace matrix corresponding to a previous sampling signal of the target sampling signal; the tracking unit is used for carrying out signal subspace tracking on the target sampling signal according to the reference signal subspace matrix to obtain a target signal subspace matrix corresponding to the target sampling signal; the determining unit is used for determining a tracking spectrum matrix corresponding to the target sampling signal according to the target signal subspace matrix and the reference signal subspace matrix; and the processing unit is used for obtaining the target channel parameters according to the tracking spectrum matrix.

In some embodiments of the present disclosure, the tracking unit is specifically configured to: determining subspace tracking parameters corresponding to the reference signal subspace matrix according to the target sampling signals; and obtaining a target signal subspace matrix according to the subspace tracking parameters and the reference signal subspace matrix.

In some embodiments of the present disclosure, the tracking unit is further configured to: projecting the target sampling signal into a reference signal subspace to obtain a projection signal; and determining a signal error between the target sampling signal and the projection signal, and generating an error representation vector corresponding to the signal error, wherein the error representation vector is used as a subspace tracking parameter.

In some embodiments of the present disclosure, the tracking unit is further configured to: determining a plurality of historical sampling signals associated with the target sampling signal; determining a target compression vector corresponding to the target sampling signal, and determining a plurality of historical compression vectors corresponding to the plurality of historical sampling signals respectively; determining reference processing data according to a plurality of historical compression vectors and preset forgetting parameters; determining target processing data according to the target compression vector, a plurality of historical compression vectors and preset forgetting parameters; channel estimation is carried out in a target signal subspace to obtain a channel impulse response estimated value corresponding to the target signal subspace, wherein the target signal subspace is a signal subspace corresponding to a target sampling signal; and processing the target processing data, the reference processing data and the preset forgetting parameters according to the channel impulse response estimated value, and taking the processed result value and the error expression vector as subspace tracking parameters.

In some embodiments of the present disclosure, the determining unit is specifically configured to: performing matrix decomposition on the reference signal subspace matrix to obtain a first reference submatrix and a second reference submatrix; mapping the first reference sub-matrix and the second reference sub-matrix by adopting a target data processing method to obtain a first error sub-representation vector corresponding to the first reference sub-matrix and a second error sub-representation vector corresponding to the second reference sub-matrix; determining an initial reference spectrum matrix corresponding to the reference signal subspace matrix; processing the initial reference spectrum matrix according to the first error sub-expression vector and the second error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix; performing matrix decomposition on the target signal subspace matrix to obtain a third reference submatrix; and generating a tracking spectrum matrix according to the target spectrum matrix and the third reference submatrix.

In some embodiments of the present disclosure, the determining unit is further configured to: optimizing the first error sub-representation vector by adopting a first reference sub-matrix to obtain a third error sub-representation vector; optimizing the second error sub-representation vector by adopting a second reference sub-matrix to obtain a fourth error sub-representation vector; the method for processing the initial reference spectrum matrix according to the first error sub-expression vector and the second error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix comprises the following steps: and carrying out iterative processing on the initial reference spectrum matrix according to the first error sub-expression vector, the second error sub-expression vector, the third error sub-expression vector and the fourth error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix.

In some embodiments of the present disclosure, the processing unit is specifically configured to: performing feature analysis on the tracking spectrum matrix to obtain matrix features corresponding to the tracking spectrum matrix; and determining the target channel parameters according to the matrix characteristics.

In some embodiments of the present disclosure, the obtaining unit is specifically configured to: determining a historical compression vector corresponding to a previous sampling signal of the target sampling signal and a feature vector corresponding to the previous sampling signal; and determining a target sampling signal according to the previous sampling signal, the characteristic vector and the corresponding historical compression vector.

In some embodiments of the present disclosure, the acquiring unit is further configured to: determining an initial feature vector and an initial feature value corresponding to an initial sampling signal, wherein the initial sampling signal is a sampling signal corresponding to the earliest time point in a plurality of time points respectively corresponding to a plurality of historical sampling signals; determining a plurality of historical compression vectors respectively corresponding to the plurality of historical sampling signals; sequentially carrying out iterative processing on a plurality of historical compression vectors by referring to a plurality of time points by adopting an initial feature vector and an initial feature value to obtain a feature vector corresponding to a previous compression vector, wherein the previous compression vector corresponds to a previous sampling signal; the feature vector corresponding to the previous compressed vector is taken as the feature vector corresponding to the previous sampled signal.

An estimation device for channel parameters according to an embodiment of a third aspect of the present disclosure includes: memory, transceiver, 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: acquiring a target sampling signal and a reference signal subspace matrix, wherein the reference signal subspace matrix is a signal subspace matrix corresponding to a previous sampling signal of the target sampling signal; carrying out signal subspace tracking on the target sampling signal according to the reference signal subspace matrix to obtain a target signal subspace matrix corresponding to the target sampling signal; determining a tracking spectrum matrix corresponding to the target sampling signal according to the target signal subspace matrix and the reference signal subspace matrix; and obtaining the target channel parameters according to the tracking spectrum matrix.

In some embodiments of the present disclosure, the processor is specifically configured to: determining subspace tracking parameters corresponding to the reference signal subspace matrix according to the target sampling signals; and obtaining a target signal subspace matrix according to the subspace tracking parameters and the reference signal subspace matrix.

In some embodiments of the present disclosure, the processor is specifically configured to: projecting the target sampling signal into a reference signal subspace to obtain a projection signal; and determining a signal error between the target sampling signal and the projection signal, and generating an error representation vector corresponding to the signal error, wherein the error representation vector is used as a subspace tracking parameter.

In some embodiments of the present disclosure, a plurality of historical sampled signals associated with a target sampled signal are determined; determining a target compression vector corresponding to the target sampling signal, and determining a plurality of historical compression vectors corresponding to the plurality of historical sampling signals respectively; determining reference processing data according to a plurality of historical compression vectors and preset forgetting parameters; determining target processing data according to the target compression vector, a plurality of historical compression vectors and preset forgetting parameters; channel estimation is carried out in a target signal subspace to obtain a channel impulse response estimated value corresponding to the target signal subspace, wherein the target signal subspace is a signal subspace corresponding to a target sampling signal; and processing the target processing data, the reference processing data and the preset forgetting parameters according to the channel impulse response estimated value, and taking the processed result value and the error expression vector as subspace tracking parameters.

In some embodiments of the present disclosure, the processor is specifically configured to: performing matrix decomposition on the reference signal subspace matrix to obtain a first reference submatrix and a second reference submatrix; mapping the first reference sub-matrix and the second reference sub-matrix by adopting a target data processing method to obtain a first error sub-representation vector corresponding to the first reference sub-matrix and a second error sub-representation vector corresponding to the second reference sub-matrix; determining an initial reference spectrum matrix corresponding to the reference signal subspace matrix; processing the initial reference spectrum matrix according to the first error sub-expression vector and the second error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix; performing matrix decomposition on the target signal subspace matrix to obtain a third reference submatrix; and generating a tracking spectrum matrix according to the target spectrum matrix and the third reference submatrix.

In some embodiments of the present disclosure, the processor is specifically configured to: optimizing the first error sub-representation vector by adopting a first reference sub-matrix to obtain a third error sub-representation vector; optimizing the second error sub-representation vector by adopting a second reference sub-matrix to obtain a fourth error sub-representation vector; the method for processing the initial reference spectrum matrix according to the first error sub-expression vector and the second error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix comprises the following steps: and carrying out iterative processing on the initial reference spectrum matrix according to the first error sub-expression vector, the second error sub-expression vector, the third error sub-expression vector and the fourth error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix.

In some embodiments of the present disclosure, the processor is specifically configured to: performing feature analysis on the tracking spectrum matrix to obtain matrix features corresponding to the tracking spectrum matrix; and determining the target channel parameters according to the matrix characteristics.

In some embodiments of the present disclosure, the processor is specifically configured to: determining a historical compression vector corresponding to a previous sampling signal of the target sampling signal and a feature vector corresponding to the previous sampling signal; and determining a target sampling signal according to the previous sampling signal, the characteristic vector and the corresponding historical compression vector.

In some embodiments of the present disclosure, the processor is specifically configured to: determining an initial feature vector and an initial feature value corresponding to an initial sampling signal, wherein the initial sampling signal is a sampling signal corresponding to the earliest time point in a plurality of time points respectively corresponding to a plurality of historical sampling signals; determining a plurality of historical compression vectors respectively corresponding to the plurality of historical sampling signals; sequentially carrying out iterative processing on a plurality of historical compression vectors by referring to a plurality of time points by adopting an initial feature vector and an initial feature value to obtain a feature vector corresponding to a previous compression vector, wherein the previous compression vector corresponds to a previous sampling signal; the feature vector corresponding to the previous compressed vector is taken as the feature vector corresponding to the previous sampled signal.

A fourth aspect of the present disclosure provides a processor-readable storage medium storing a computer program for causing the processor to perform: the embodiment of the first aspect of the disclosure provides a method for estimating channel parameters.

An embodiment of a fifth aspect of the present disclosure provides a communication apparatus, including a processor and a memory; wherein the memory is configured to store program code, and the processor is configured to invoke the program code to execute: the embodiment of the first aspect of the disclosure provides a method for estimating channel parameters.

A computer-readable storage medium according to an embodiment of the sixth aspect of the present disclosure stores a computer program for causing the computer to execute: the embodiment of the first aspect of the disclosure provides a method for estimating channel parameters.

A fourth aspect of the present disclosure provides a computer program product, which when run on a computer causes the computer to perform: the embodiment of the first aspect of the disclosure provides a method for estimating channel parameters.

In the method, the target sampling signal and the reference signal subspace matrix are acquired, the target sampling signal is subjected to signal subspace tracking according to the reference signal subspace matrix, the target signal subspace matrix corresponding to the target sampling signal is obtained, the tracking spectrum matrix corresponding to the target sampling signal is determined according to the target signal subspace matrix and the reference signal subspace matrix, and the target channel parameter is obtained according to the tracking spectrum matrix, so that the complexity of wireless channel parameter estimation can be effectively reduced, the real-time processing requirement of a time-varying channel is met, and the parameter estimation efficiency of the wireless channel is improved.

Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.

Drawings

The foregoing and/or additional aspects and advantages of the application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:

fig. 1 is a flowchart of a method for estimating channel parameters according to an embodiment of the present disclosure;

Fig. 2 is a flow chart illustrating a method for estimating channel parameters according to another embodiment of the present disclosure;

fig. 3 is a flowchart illustrating a method for estimating channel parameters according to another embodiment of the present disclosure;

Fig. 4 is a schematic structural diagram of an estimation device for channel parameters according to another embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of an estimation device for channel parameters according to another embodiment of the present disclosure.

Detailed Description

The term "and/or" in the embodiments of the present disclosure describes an association relationship of association objects, which indicates that three relationships may exist, for example, a and/or B may indicate: 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 the embodiments of the present disclosure means two or more, and other adjectives are similar thereto.

The following description of the technical solutions in the embodiments of the present disclosure will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, and not all embodiments. Based on the embodiments in this disclosure, all other embodiments that a person of ordinary skill in the art would obtain without making any inventive effort are within the scope of protection of this disclosure.

Common algorithms for channel parameters include multiple signal classification (Multiple Signal Classification, MUSIC), space-maximization (SAGE), and twiddle factor invariant (ESTIMATING SIGNAL PARAMETERS VIA Rotational Invariance Techniques, ESPRIT) algorithms, but the algorithms in the related art are complex and difficult to apply to real-time processing.

Therefore, the present disclosure provides a method for estimating channel parameters, which can effectively reduce complexity of wireless channel parameter estimation, and meet real-time processing requirements of a time-varying channel, so as to improve parameter estimation efficiency of wireless channels.

The technical scheme provided by the embodiment of the disclosure can be suitable for various systems, in particular to a 5G system. For example, applicable 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) systems, 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.

Fig. 1 is a flowchart of a method for estimating channel parameters according to an embodiment of the disclosure.

It should be noted that, the execution body of the channel parameter estimation method in this embodiment is a channel parameter estimation device, which may be implemented in a software and/or hardware manner, and the device may be configured in a terminal device.

As shown in fig. 1, the method for estimating channel parameters includes:

S101: and acquiring a target sampling signal and a reference signal subspace matrix, wherein the reference signal subspace matrix is a signal subspace matrix corresponding to a previous sampling signal of the target sampling signal.

Wherein the sampling signal at the current time may be referred to as a target sampling signal, for example: the current time is t, the target sample signal may be denoted as x (t), and the corresponding previous sample signal may be denoted as x (t-1).

The reference signal subspace matrix is a signal subspace matrix corresponding to a previous sampling signal of the target sampling signal, for example, U (t-1) can be used, that is, U (t-1) is a signal subspace of a time band estimation parameter of t-1.

For example, taking the multipath delay estimation as an example, U (t-1) may be a signal subspace matrix of a frequency domain channel estimation correlation matrix, and x (t) may be a time-of-t frequency domain channel estimation vector.

S102: and carrying out signal subspace tracking on the target sampling signal according to the reference signal subspace matrix to obtain a target signal subspace matrix corresponding to the target sampling signal.

After the target sampling signal and the reference signal subspace matrix are obtained, further, signal subspace tracking is performed on the target sampling signal x (t) according to the reference signal subspace matrix U (t-1), so as to obtain a target signal subspace matrix, wherein the target signal subspace matrix can be expressed as: u (t) =u (t-1) +e (t) g (t) ^H. That is, the target signal subspace matrix U (t) is generated from the reference signal subspace matrix U (t-1) of the previous sample signal and the target sample signal x (t).

In some embodiments, first, a subspace tracking parameter corresponding to the reference signal subspace matrix U (t-1) may be determined according to the target sampling signal x (t), where the subspace tracking parameter may assist in determining the target signal subspace matrix, and the subspace tracking parameter may include g (t) and e (t), for example, where g (t) is an intermediate variable of the algorithm, and e (t) may represent an error vector after projection of the signal subspace U (t-1) at time t-1.

In some embodiments, in determining the subspace tracking parameter e (t), the target sample signal x (t) may be projected into the reference signal subspace U (t-1), resulting in a projection signal, which may have an error with the target sample signal x (t). Further, a signal error between the target sampling signal x (t) and the projection signal is determined, and an error expression vector e (t) corresponding to the signal error is generated, that is, e (t) may represent an error vector after the projection of the signal subspace U (t-1) at the t-1 time of the vector x (t), and the error expression vector e (t) is taken as the subspace tracking parameter. Therefore, errors can be referenced in the calculation process, and the accuracy of a tracking algorithm can be improved.

Further, a target signal subspace matrix is obtained according to subspace tracking parameters g (t) and e (t) and a reference signal subspace matrix U (t-1). Wherein the processing procedure is as follows: transpose g (t) to obtain g (t) ^H, and then sum g (t) ^H e (t) with U (t-1) to obtain target signal subspace matrix U (t) =u (t-1) +e (t) g (t) ^H. Therefore, in the process of determining the target signal subspace matrix, subspace tracking parameters such as g (t) and e (t) can be combined, and the algorithm accuracy can be improved.

S103: and determining a tracking spectrum matrix corresponding to the target sampling signal according to the target signal subspace matrix and the reference signal subspace matrix.

After determining the target signal subspace matrix U (t) and the reference signal subspace matrix U (t-1), further, according to the embodiments of the disclosure, the tracking spectrum matrix corresponding to the target sampling signal x (t) may be determined according to the target signal subspace matrix U (t) and the reference signal subspace matrix U (t-1), where the tracking spectrum matrix may be expressed asWherein ψ (t) is an intermediate variable, v (t) ^H is the last row of subspace matrix U (t)/>

S104: and obtaining the target channel parameters according to the tracking spectrum matrix.

Some embodiments may track a spectrum matrixFeature analysis is performed, for example: performing EVD decomposition on the tracking spectrum matrix Φ (t) in a dimension r×r to obtain matrix features corresponding to the tracking spectrum matrix, where the matrix features include: /(I)Further, the target channel parameters are determined based on the matrix characteristics, wherein the target channel parameters include, for example, user multipath delay, user angle, user Doppler frequency, and any other possible channel parameters, without limitation.

For example, for estimating the delay, there isWherein Δf is the subcarrier spacing, τ _m is the estimated delay of the mth multipath at time t.

In this embodiment, by acquiring the target sampling signal and the reference signal subspace matrix, and performing signal subspace tracking on the target sampling signal according to the reference signal subspace matrix, a target signal subspace matrix corresponding to the target sampling signal is obtained, and according to the target signal subspace matrix and the reference signal subspace matrix, a tracking spectrum matrix corresponding to the target sampling signal is determined, and according to the tracking spectrum matrix, a target channel parameter is obtained, so that the complexity of wireless channel parameter estimation can be effectively reduced, the real-time processing requirement of a time-varying channel is met, and the parameter estimation efficiency of the wireless channel is improved.

Fig. 2 is a flow chart of a method for estimating channel parameters according to another embodiment of the present disclosure, as shown in fig. 2, the method for estimating channel parameters includes:

S201: and acquiring a target sampling signal and a reference signal subspace matrix, wherein the reference signal subspace matrix is a signal subspace matrix corresponding to a previous sampling signal of the target sampling signal.

The description of S201 may be referred to the above embodiments, and will not be repeated here.

S202: a plurality of historical sampling signals associated with the target sampling signal is determined.

In an operation of determining a target signal subspace matrix corresponding to a target sample signal, embodiments of the present disclosure first determine a plurality of historical sample signals associated with the target sample signal, the plurality of historical sample signals such as: x (1), x (2), x (3)..x (t-1).

S203: a target compression vector corresponding to the target sampling signal is determined, and a plurality of history compression vectors corresponding to the plurality of history sampling signals, respectively, are determined.

Further, a target compression vector corresponding to the target sampling signal x (t) and a plurality of history compression vectors corresponding to the plurality of history sampling signals, respectively, are determined, wherein the target compression vector may be expressed as y (t) =u (t-1) ^H x (t), the corresponding plurality of history compression vectors may be expressed as y (i), i=1, 2..t-1, and when i=t, y (t) is referred to as the target compression vector.

S204: and determining reference processing data according to the plurality of historical compression vectors and the preset forgetting parameters.

Wherein the preset forgetting parameter can be understood as forgetting factor beta of PAST algorithm, and the smaller the value of 0< beta is less than or equal to 1, the smaller the influence of the historical sample (historical frequency domain channel estimation) which is far away from the current time t on the estimation result, the reference processing data can be determined according to y (i) and beta, and the reference processing data can be expressed as

S205: and determining target processing data according to the target compression vector, the plurality of historical compression vectors and the preset forgetting parameters.

After determining the reference processing data, determining target processing data according to the target compression vector y (t), the plurality of historical compression vectors y (i) and the preset forgetting parameters beta, wherein the target processing data can be expressed as, for example

S206: and performing channel estimation in a target signal subspace to obtain a channel impulse response estimated value corresponding to the target signal subspace, wherein the target signal subspace is a signal subspace corresponding to a target sampling signal.

After determining the target processing data, further, performing channel estimation in a target signal subspace to obtain a channel impulse response estimated value corresponding to the target signal subspace, where the target signal subspace is a signal subspace corresponding to the target sampling signal x (t), and the channel impulse response estimated value may be expressed as h (t) =z (t-1) y (t).

S207: and processing the target processing data, the reference processing data and the preset forgetting parameters according to the channel impulse response estimated value, and taking the processed result value and the error expression vector as subspace tracking parameters.

Further, the target processing data Z (t), the reference processing data Z (t-1), and the preset forgetting parameter β are processed according to the channel impulse response estimation value h (t), the processing procedure is as follows: If β, Z (t-1), h (t) are known, a result value g (t) can be obtained, and the result value g (t) obtained by processing and the error expression vector e (t) are taken as subspace tracking parameters together. Therefore, the preset forgetting parameters beta and h (t) can be introduced in the process of determining the subspace tracking parameter g (t), and the influence of the historical sample on the estimation result can be adjusted by changing beta, so that the method can be flexibly applied to different scenes and the accuracy of parameter estimation is improved.

In addition, the channel impulse response estimated value h (t), the preset forgetting parameter beta and the target compression vector y (t) can be obtainedAnd g (t) =γ (t) h (t) is obtained from γ (t) and h (t).

S208: and obtaining a target signal subspace matrix according to the subspace tracking parameters and the reference signal subspace matrix.

S209: and determining a tracking spectrum matrix corresponding to the target sampling signal according to the target signal subspace matrix and the reference signal subspace matrix.

S210: and obtaining the target channel parameters according to the tracking spectrum matrix.

The descriptions of S208-S210 may be referred to the above embodiments, and are not repeated here.

It should be understood that the foregoing embodiments are merely exemplary of the flow of the channel parameter estimation method, and in practical applications, the foregoing steps may be adaptively omitted or added, or the execution sequence of the steps may be adjusted, which is not limited thereto.

In this embodiment, by acquiring the target sampling signal and the reference signal subspace matrix, and performing signal subspace tracking on the target sampling signal according to the reference signal subspace matrix, a target signal subspace matrix corresponding to the target sampling signal is obtained, and according to the target signal subspace matrix and the reference signal subspace matrix, a tracking spectrum matrix corresponding to the target sampling signal is determined, and according to the tracking spectrum matrix, a target channel parameter is obtained, so that the complexity of wireless channel parameter estimation can be effectively reduced, the real-time processing requirement of a time-varying channel is met, and the parameter estimation efficiency of the wireless channel is improved. In addition, preset forgetting parameters beta and h (t) can be introduced in the process of determining the subspace tracking parameter g (t), and the influence of the historical sample on the estimation result can be adjusted by changing beta, so that the method can be flexibly applied to different scenes and the accuracy of parameter estimation is improved.

Fig. 3 is a flow chart of a method for estimating channel parameters according to another embodiment of the present disclosure, as shown in fig. 3, the method for estimating channel parameters includes:

S301: and acquiring a target sampling signal and a reference signal subspace matrix, wherein the reference signal subspace matrix is a signal subspace matrix corresponding to a previous sampling signal of the target sampling signal.

Some embodiments may make minor modifications to the PAST algorithm, resulting in PASTd algorithm. The PASTd algorithm is to take the rank of the signal subspace in the PAST algorithm as 1, iterate out the largest eigenvalue and the corresponding eigenvector each time, then delete the largest eigenvalue and the eigenvector next time, iterate out all eigenvalues and eigenvectors of the signal subspace in sequence.

Specifically, a history compressed vector corresponding to a previous sample signal of the target sample signal and a feature vector corresponding to the previous sample signal may be first determined.

Wherein the historical compression vector may be represented asFeature vectors can be expressed as

In some embodiments, in the operation of determining the feature vector w _i (t), an initial feature vector and an initial feature value corresponding to an initial sampled signal are first determined. The initial sampling signal is a sampling signal corresponding to an earliest time point in a plurality of time points corresponding to a plurality of historical sampling signals respectively, and the initial sampling signal can be expressed as x (0). The feature vectors may represent features of the sampled signals at different times, and the feature values may represent specific feature values of the sampled signals at different times, for example: w _i (t) is the i-th eigenvector at time t, λ _i (t) is the i-th eigenvalue at time t, then the initial eigenvector may be denoted as w _i (0), the initial eigenvalue may be denoted as λ _i (0), i=1, r, and may be decomposed by standard EVD.

Further, a plurality of historical compression vectors y _i (t) corresponding to the plurality of historical sampling signals respectively are determined, and an initial feature vector w _i (0) and an initial feature value lambda _i (0) are adopted to sequentially perform iterative processing on the plurality of historical compression vectors y _i (t) with reference to a plurality of time points, so that feature vectors corresponding to a previous compression vector are obtained, and the previous compression vector corresponds to the previous sampling signal. Finally, the feature vector corresponding to the previous compressed vector is used as the feature vector corresponding to the previous sampling signal.

In practical application, the iterative process is as follows:

For i＝1,...,r，

λ_i(t)＝βλ_i(t-1)+|y_i(t)|²，

x_i+1(t)＝x_i(t)-w_i(t)y_i(t)

End

Wherein w _i (t) is the i-th eigenvector at time t, λ _i (t) is the i-th eigenvalue at time t, their initial values w _i (0) and λ _i (0), i=1.

Further, a target sample signal is determined from the previous sample signal, the feature vector, and the corresponding historical compression vector. The embodiment is based on the PAST algorithm, the largest eigenvalue and the corresponding eigenvector are iterated out each time, then the largest eigenvalue and the eigenvector are iterated out next time, and all eigenvalues and eigenvectors of the signal subspace are iterated out in sequence, so that the complexity of the algorithm can be reduced, and the vector tracking error is reduced.

S302: and carrying out signal subspace tracking on the target sampling signal according to the reference signal subspace matrix to obtain a target signal subspace matrix corresponding to the target sampling signal.

The description of S302 may be referred to the above embodiments, and will not be repeated here.

S303: and performing matrix decomposition on the reference signal subspace matrix to obtain a first reference submatrix and a second reference submatrix.

For example, the reference signal subspace matrix U (t-1) is decomposed between row 1 and row 2 to form a first reference submatrix, which may be denoted as U ₁ (t-1); the reference signal subspace matrix U (t-1) is decomposed between row 2 and row 1 to the last into a second reference submatrix, which may be denoted U ₂ (t-1).

S304: and mapping the first reference sub-matrix and the second reference sub-matrix by adopting a target data processing method to obtain a first error sub-representation vector corresponding to the first reference sub-matrix and a second error sub-representation vector corresponding to the second reference sub-matrix.

The target data processing method may be, for example, a PAST algorithm, and the PAST algorithm may be used to map the first reference submatrix U ₁ (t-1) and the second reference submatrix U ₂ (t-1) to obtain a first error submatrix vector and a second error submatrix vector. In actual operation, mapping the first reference sub-matrix and the second reference sub-matrix by using the target data processing method may be understood as obtaining the corresponding sub-vectors e ₁ (t) and e ₂ (t) according to the error vector e (t) in the PAST algorithm, where e ₁ (t) corresponds to the first error sub-representation vector and e ₂ (t) corresponds to the second error sub-representation vector.

In some embodiments, the first reference submatrix U ₁ (t-1) may also be used to optimize the first error submatrix e ₁ (t) to obtain a third error submatrix, where the third error submatrix may be represented as e ₃(t)＝U₁(t-1)^He₂ (t).

Further, the second error sub-representation vector e ₂ (t) is optimized by using the second reference sub-matrix U ₂ (t-1), so as to obtain a fourth error sub-representation vector, which may be represented as e ₄(t)＝U₂(t-1)^He₁ (t).

S305: an initial reference spectrum matrix corresponding to the reference signal subspace matrix is determined.

Wherein the initial reference spectrum matrix may be denoted as ψ (t-1).

S306: and processing the initial reference spectrum matrix according to the first error sub-representation vector and the second error sub-representation vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix.

Further, according to the first error sub-expression vector e ₁ (t) and the second error sub-expression vector e ₂ (t), the initial reference spectrum matrix is processed to obtain the target spectrum matrix ψ (t) =ψ (t-1) +e ₃(t)g(t)^H+g(t)e₅(t)^H.

In some embodiments, e ₅(t)＝e₄(t)+g(t)[e₂(t)^He₁ (t) may be determined according to the first error sub-expression vector e ₁ (t), the second error sub-expression vector e ₂ (t), and the fourth error sub-expression vector e ₄ (t), and the initial reference spectrum matrix ψ (t-1) may be iteratively processed according to the third error sub-expression vectors e ₃ (t) and e ₅ (t), to obtain a target spectrum matrix ψ (t) =ψ (t-1) +e ₃(t)g(t)^H+g(t)e₅(t)^H corresponding to the target signal subspace matrix. Therefore, the calculation by adopting the error sub-representation vector after the optimization processing can improve the accuracy of the algorithm and reduce the complexity of the algorithm.

S307: and performing matrix decomposition on the target signal subspace matrix to obtain a third reference submatrix.

Further, the target signal subspace matrix U (t) is matrix decomposed, for example: decomposing the last row of the target signal subspace matrix U (t) to obtain a third reference submatrix, wherein the third reference submatrix can be expressed as upsilon (t) ^H, and

S308: and generating a tracking spectrum matrix according to the target spectrum matrix and the third reference submatrix.

After the target spectrum matrix ψ (t) and the third reference submatrix v (t) ^H are determined, a tracking spectrum matrix may be generated according to the target spectrum matrix and the third reference submatrixThus, the tracking spectrum matrix can be generated by combining various parameters, which is beneficial to the auxiliary determination of the target channel parameters.

S309: and obtaining the target channel parameters according to the tracking spectrum matrix.

The description of S309 may be referred to the above embodiments, and will not be repeated here.

In this embodiment, by acquiring the target sampling signal and the reference signal subspace matrix, and performing signal subspace tracking on the target sampling signal according to the reference signal subspace matrix, a target signal subspace matrix corresponding to the target sampling signal is obtained, and according to the target signal subspace matrix and the reference signal subspace matrix, a tracking spectrum matrix corresponding to the target sampling signal is determined, and according to the tracking spectrum matrix, a target channel parameter is obtained, so that the complexity of wireless channel parameter estimation can be effectively reduced, the real-time processing requirement of a time-varying channel is met, and the parameter estimation efficiency of the wireless channel is improved. In addition, the largest eigenvalue and the corresponding eigenvector are iterated out each time, then the largest eigenvalue and the eigenvector are iterated out next time, and all eigenvalues and eigenvectors of the signal subspace are iterated out in sequence, so that the algorithm complexity can be reduced, and the vector tracking error is reduced.

Fig. 4 is a schematic structural diagram of an estimation device for channel parameters according to another embodiment of the present disclosure.

As shown in fig. 4, the channel parameter estimating apparatus 40 includes:

An obtaining unit 401, configured to obtain a target sampling signal and a reference signal subspace matrix, where the reference signal subspace matrix is a signal subspace matrix corresponding to a previous sampling signal of the target sampling signal;

a tracking unit 402, configured to perform signal subspace tracking on the target sampling signal according to the reference signal subspace matrix, so as to obtain a target signal subspace matrix corresponding to the target sampling signal;

a determining unit 403, configured to determine a tracking spectrum matrix corresponding to the target sampling signal according to the target signal subspace matrix and the reference signal subspace matrix;

and a processing unit 404, configured to obtain the target channel parameter according to the tracking spectrum matrix.

In some embodiments of the present disclosure, the tracking unit 402 is specifically configured to: determining subspace tracking parameters corresponding to the reference signal subspace matrix according to the target sampling signals; and obtaining a target signal subspace matrix according to the subspace tracking parameters and the reference signal subspace matrix.

In some embodiments of the present disclosure, the tracking unit 402 is further configured to: projecting the target sampling signal into a reference signal subspace to obtain a projection signal; and determining a signal error between the target sampling signal and the projection signal, and generating an error representation vector corresponding to the signal error, wherein the error representation vector is used as a subspace tracking parameter.

In some embodiments of the present disclosure, the tracking unit 402 is further configured to: determining a plurality of historical sampling signals associated with the target sampling signal; determining a target compression vector corresponding to the target sampling signal, and determining a plurality of historical compression vectors corresponding to the plurality of historical sampling signals respectively; determining reference processing data according to a plurality of historical compression vectors and preset forgetting parameters; determining target processing data according to the target compression vector, a plurality of historical compression vectors and preset forgetting parameters; channel estimation is carried out in a target signal subspace to obtain a channel impulse response estimated value corresponding to the target signal subspace, wherein the target signal subspace is a signal subspace corresponding to a target sampling signal; and processing the target processing data, the reference processing data and the preset forgetting parameters according to the channel impulse response estimated value, and taking the processed result value and the error expression vector as subspace tracking parameters.

In some embodiments of the present disclosure, the determining unit 403 is specifically configured to: performing matrix decomposition on the reference signal subspace matrix to obtain a first reference submatrix and a second reference submatrix; mapping the first reference sub-matrix and the second reference sub-matrix by adopting a target data processing method to obtain a first error sub-representation vector corresponding to the first reference sub-matrix and a second error sub-representation vector corresponding to the second reference sub-matrix; determining an initial reference spectrum matrix corresponding to the reference signal subspace matrix; processing the initial reference spectrum matrix according to the first error sub-expression vector and the second error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix; performing matrix decomposition on the target signal subspace matrix to obtain a third reference submatrix; and generating a tracking spectrum matrix according to the target spectrum matrix and the third reference submatrix.

In some embodiments of the present disclosure, the determining unit 403 is further configured to: optimizing the first error sub-representation vector by adopting a first reference sub-matrix to obtain a third error sub-representation vector; optimizing the second error sub-representation vector by adopting a second reference sub-matrix to obtain a fourth error sub-representation vector; the method for processing the initial reference spectrum matrix according to the first error sub-expression vector and the second error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix comprises the following steps: and carrying out iterative processing on the initial reference spectrum matrix according to the first error sub-expression vector, the second error sub-expression vector, the third error sub-expression vector and the fourth error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix.

In some embodiments of the present disclosure, the processing unit 404 is specifically configured to: performing feature analysis on the tracking spectrum matrix to obtain matrix features corresponding to the tracking spectrum matrix; and determining the target channel parameters according to the matrix characteristics.

In some embodiments of the present disclosure, the obtaining unit 401 is specifically configured to: determining a historical compression vector corresponding to a previous sampling signal of the target sampling signal and a feature vector corresponding to the previous sampling signal; and determining a target sampling signal according to the previous sampling signal, the characteristic vector and the corresponding historical compression vector.

In some embodiments of the present disclosure, the obtaining unit 401 is further configured to: determining an initial feature vector and an initial feature value corresponding to an initial sampling signal, wherein the initial sampling signal is a sampling signal corresponding to the earliest time point in a plurality of time points respectively corresponding to a plurality of historical sampling signals; determining a plurality of historical compression vectors respectively corresponding to the plurality of historical sampling signals; sequentially carrying out iterative processing on a plurality of historical compression vectors by referring to a plurality of time points by adopting an initial feature vector and an initial feature value to obtain a feature vector corresponding to a previous compression vector, wherein the previous compression vector corresponds to a previous sampling signal; the feature vector corresponding to the previous compressed vector is taken as the feature vector corresponding to the previous sampled signal.

It should be noted that, the above device provided in the embodiment of the present disclosure can implement all the method steps implemented in the embodiment of the 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 herein.

It should be noted that, in the embodiment of the present disclosure, the division of the units is schematic, which is merely a logic function division, and other division manners may be actually implemented. In addition, each functional unit in each embodiment of the present disclosure 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 disclosure 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 to cause a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the present disclosure. 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.

Referring to fig. 5, the channel parameter estimation device 50 includes a memory 501, a transceiver 502, a processor 503 and a user interface 504: a memory 501 for storing a computer program; a transceiver 502 for transceiving data under the control of the processor 503; a processor 503 for reading the computer program in the memory 501 and performing the following operations:

Acquiring a target sampling signal and a reference signal subspace matrix, wherein the reference signal subspace matrix is a signal subspace matrix corresponding to a previous sampling signal of the target sampling signal;

Carrying out signal subspace tracking on the target sampling signal according to the reference signal subspace matrix to obtain a target signal subspace matrix corresponding to the target sampling signal;

Determining a tracking spectrum matrix corresponding to the target sampling signal according to the target signal subspace matrix and the reference signal subspace matrix;

And obtaining the target channel parameters according to the tracking spectrum matrix.

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 503 and various circuits of memory represented by memory 501, linked together. The bus architecture may also link together various other circuits such as peripheral devices, voltage regulators, power management circuits, etc., which are well known in the art and, therefore, will not be described further herein. The bus interface provides an interface. The transceiver 502 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, etc. The user interface 504 may also be an interface capable of interfacing with an inscribed desired device for a different user device, including but not limited to a keypad, display, speaker, microphone, joystick, etc.

The processor 503 is responsible for managing the bus architecture and general processing, and the memory 501 may store data used by the processor 600 in performing operations.

Alternatively, the processor 503 may be a CPU (central processing unit), an ASIC (Application SPECIFIC INTEGRATED Circuit), an FPGA (Field-Programmable gate array) or a CPLD (Complex Programmable Logic Device ), and the processor may also employ a multi-core architecture.

The processor is operable to perform any of the methods provided by the embodiments of the present disclosure in accordance with the obtained executable instructions by invoking a computer program stored in a memory. The processor and the memory may also be physically separate.

In some embodiments of the present disclosure, the processor 503 is specifically configured to:

determining subspace tracking parameters corresponding to the reference signal subspace matrix according to the target sampling signals;

And obtaining a target signal subspace matrix according to the subspace tracking parameters and the reference signal subspace matrix.

Projecting the target sampling signal into a reference signal subspace to obtain a projection signal;

And determining a signal error between the target sampling signal and the projection signal, and generating an error representation vector corresponding to the signal error, wherein the error representation vector is used as a subspace tracking parameter.

Determining a plurality of historical sampling signals associated with the target sampling signal;

Determining a target compression vector corresponding to the target sampling signal, and determining a plurality of historical compression vectors corresponding to the plurality of historical sampling signals respectively;

Determining reference processing data according to a plurality of historical compression vectors and preset forgetting parameters;

determining target processing data according to the target compression vector, a plurality of historical compression vectors and preset forgetting parameters;

channel estimation is carried out in a target signal subspace to obtain a channel impulse response estimated value corresponding to the target signal subspace, wherein the target signal subspace is a signal subspace corresponding to a target sampling signal;

And processing the target processing data, the reference processing data and the preset forgetting parameters according to the channel impulse response estimated value, and taking the processed result value and the error expression vector as subspace tracking parameters.

Performing matrix decomposition on the reference signal subspace matrix to obtain a first reference submatrix and a second reference submatrix;

mapping the first reference sub-matrix and the second reference sub-matrix by adopting a target data processing method to obtain a first error sub-representation vector corresponding to the first reference sub-matrix and a second error sub-representation vector corresponding to the second reference sub-matrix;

Determining an initial reference spectrum matrix corresponding to the reference signal subspace matrix;

Processing the initial reference spectrum matrix according to the first error sub-expression vector and the second error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix;

Performing matrix decomposition on the target signal subspace matrix to obtain a third reference submatrix;

and generating a tracking spectrum matrix according to the target spectrum matrix and the third reference submatrix.

optimizing the first error sub-representation vector by adopting a first reference sub-matrix to obtain a third error sub-representation vector;

Optimizing the second error sub-representation vector by adopting a second reference sub-matrix to obtain a fourth error sub-representation vector;

The method for processing the initial reference spectrum matrix according to the first error sub-expression vector and the second error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix comprises the following steps:

And carrying out iterative processing on the initial reference spectrum matrix according to the first error sub-expression vector, the second error sub-expression vector, the third error sub-expression vector and the fourth error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix.

performing feature analysis on the tracking spectrum matrix to obtain matrix features corresponding to the tracking spectrum matrix;

And determining the target channel parameters according to the matrix characteristics.

Determining a historical compression vector corresponding to a previous sampling signal of the target sampling signal and a feature vector corresponding to the previous sampling signal;

and determining a target sampling signal according to the previous sampling signal, the characteristic vector and the corresponding historical compression vector.

Determining an initial feature vector and an initial feature value corresponding to an initial sampling signal, wherein the initial sampling signal is a sampling signal corresponding to the earliest time point in a plurality of time points respectively corresponding to a plurality of historical sampling signals;

Determining a plurality of historical compression vectors respectively corresponding to the plurality of historical sampling signals;

Sequentially carrying out iterative processing on a plurality of historical compression vectors by referring to a plurality of time points by adopting an initial feature vector and an initial feature value to obtain a feature vector corresponding to a previous compression vector, wherein the previous compression vector corresponds to a previous sampling signal;

The feature vector corresponding to the previous compressed vector is taken as the feature vector corresponding to the previous sampled signal.

Another embodiment of the present disclosure also proposes a processor-readable storage medium storing a computer program for causing the processor to perform a method of estimating channel parameters.

Another embodiment of the present disclosure also provides a communication device including a processor and a memory; wherein the memory is configured to store program code, and the processor is configured to invoke the program code to execute: the embodiment of the first aspect of the disclosure provides a method for estimating channel parameters.

Another embodiment of the present disclosure also proposes a computer program product, characterized in that, when the computer program product is run on a computer, the computer is caused to perform: the embodiment of the first aspect of the disclosure provides a method for estimating channel parameters.

It will be apparent to those skilled in the art that embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure 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 disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. 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 disclosure without departing from the spirit or scope of the disclosure. Thus, the present disclosure is intended to 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 estimating channel parameters, the method comprising:

And obtaining target channel parameters according to the tracking spectrum matrix.

2. The method of claim 1, wherein said performing signal subspace tracking on said target sample signal according to said reference signal subspace matrix to obtain a target signal subspace matrix corresponding to said target sample signal, comprises:

Determining subspace tracking parameters corresponding to the reference signal subspace matrix according to the target sampling signal;

and obtaining the target signal subspace matrix according to the subspace tracking parameter and the reference signal subspace matrix.

3. The method of claim 2, wherein the determining subspace tracking parameters corresponding to the reference signal subspace matrix from the target sample signal comprises:

projecting the target sampling signal into the reference signal subspace to obtain a projection signal;

and determining a signal error between the target sampling signal and the projection signal, and generating an error representation vector corresponding to the signal error, wherein the error representation vector is used as the subspace tracking parameter.

4. The method of claim 3, wherein said determining subspace tracking parameters corresponding to said reference signal subspace matrix from said target sample signal further comprises:

Determining a plurality of historical sampled signals associated with the target sampled signal;

determining reference processing data according to the plurality of historical compression vectors and preset forgetting parameters;

Determining target processing data according to the target compression vector, the plurality of historical compression vectors and the preset forgetting parameters;

Channel estimation is carried out in a target signal subspace to obtain a channel impulse response estimated value corresponding to the target signal subspace, wherein the target signal subspace is a signal subspace corresponding to the target sampling signal;

And processing the target processing data, the reference processing data and the preset forgetting parameters according to the channel impulse response estimated value, and taking the processed result value and the error expression vector as the subspace tracking parameters together.

5. The method of claim 1, wherein the determining a tracking spectrum matrix corresponding to the target sampled signal based on the target signal subspace matrix and the reference signal subspace matrix comprises:

processing the initial reference spectrum matrix according to the first error sub-representation vector and the second error sub-representation vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix;

And generating the tracking spectrum matrix according to the target spectrum matrix and the third reference submatrix.

6. The method of claim 5, wherein after said deriving a first error sub-representation vector corresponding to said first reference sub-matrix and a second error sub-representation vector corresponding to said second reference sub-matrix, said method further comprises:

optimizing the first error sub-representation vector by adopting the first reference sub-matrix to obtain a third error sub-representation vector;

optimizing the second error sub-representation vector by adopting the second reference sub-matrix to obtain a fourth error sub-representation vector;

The processing the initial reference spectrum matrix according to the first error sub-expression vector and the second error sub-expression vector to obtain a target spectrum matrix corresponding to the target signal subspace matrix, including:

7. The method of claim 1, wherein said deriving target channel parameters from said tracking spectrum matrix comprises:

8. The method of claim 1, wherein the acquiring the target sample signal comprises:

Determining a historical compression vector corresponding to a previous sample signal of the target sample signal and a feature vector corresponding to the previous sample signal;

and determining the target sampling signal according to the previous sampling signal, the characteristic vector and the corresponding historical compression vector.

9. The method of claim 8, wherein the determining a feature vector corresponding to a previous sample signal of the target sample signal comprises:

sequentially carrying out iterative processing on the plurality of historical compression vectors by adopting the initial feature vectors and the initial feature values and referring to the plurality of time points to obtain feature vectors corresponding to a previous compression vector, wherein the previous compression vector corresponds to a previous sampling signal of the target sampling signal;

And taking the characteristic vector corresponding to the previous compression vector as the characteristic vector corresponding to the previous sampling signal.

10. An apparatus for estimating channel parameters, the apparatus comprising:

An obtaining unit, configured to obtain a target sampling signal and a reference signal subspace matrix, where the reference signal subspace matrix is a signal subspace matrix corresponding to a previous sampling signal of the target sampling signal;

The tracking unit is used for carrying out signal subspace tracking on the target sampling signal according to the reference signal subspace matrix to obtain a target signal subspace matrix corresponding to the target sampling signal;

a determining unit, configured to determine a tracking spectrum matrix corresponding to the target sampling signal according to the target signal subspace matrix and the reference signal subspace matrix;

And the processing unit is used for obtaining the target channel parameters according to the tracking spectrum matrix.

11. The apparatus according to claim 10, wherein the tracking unit is configured to:

12. The apparatus of claim 11, wherein the tracking unit is further configured to:

13. The apparatus of claim 12, wherein the tracking unit is further configured to:

14. The apparatus according to claim 10, wherein the determining unit is specifically configured to:

15. The apparatus of claim 14, wherein the determining unit is further for:

16. The apparatus according to claim 10, wherein the processing unit is specifically configured to:

17. The apparatus according to claim 10, wherein the acquisition unit is specifically configured to:

18. The apparatus of claim 17, wherein the acquisition unit is further configured to:

Sequentially carrying out iterative processing on the plurality of historical compression vectors by adopting the initial feature vector and the initial feature value and referring to the plurality of time points to obtain a feature vector corresponding to a previous compression vector, wherein the previous compression vector corresponds to the previous sampling signal;

19. An apparatus for estimating channel parameters, comprising a memory, a transceiver, and 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:

20. The apparatus of claim 19, wherein the processor is configured to:

21. The apparatus of claim 20, wherein the processor is configured to:

22. The apparatus of claim 21, wherein the processor is configured to:

23. The apparatus of claim 19, wherein the processor is configured to:

24. The apparatus of claim 23, wherein the processor is configured to:

25. The apparatus of claim 19, wherein the processor is configured to:

26. The apparatus of claim 19, wherein the processor is configured to:

27. The apparatus of claim 26, wherein the processor is configured to:

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