CN104836760B - A kind of channel estimation methods and device - Google Patents
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Abstract
This application provides a kind of channel estimation methods and devices, singular value decomposition is carried out to original observing matrix, result according to the singular value decomposition, reconstruct observing matrix is obtained, and degree of rarefication is estimated, the degree of rarefication is that preset channel impulse response valuation and indexed set Ω's and concentrate the number of nonzero element, wherein, Ω is generated according to the reconstruct observing matrix and preset residual vector, finally, carrying out channel estimation using the degree of rarefication.Because singular value decomposition has the characteristics that stronger noise robustness, method and device described herein, with stronger noise robustness, also, method and device described herein can carry out the estimation of degree of rarefication, without known degree of rarefication, so practicability with higher, therefore, channel estimation methods described in the present embodiment and device, performance with higher.
Description
Technical Field
The present application relates to the field of communications, and in particular, to a channel estimation method and apparatus.
Background
In an OFDM communication system, an accurate channel estimation method is the basis for demodulation and equalization at the receiving end. The channel estimation algorithm based on the compressed sensing is an advanced technology, can complete channel estimation with less pilot frequency, has high estimation accuracy and has great practical significance.
At present, in a channel estimation algorithm based on compressed sensing, a greedy reconstruction method is widely adopted, and the noise robustness of the method is not strong, and the method needs known sparsity, so the practicability is not high. As can be seen from the above two problems, the performance of the existing channel estimation algorithm is not high.
Therefore, how to improve the performance of the channel estimation algorithm becomes a problem to be solved urgently at present.
Disclosure of Invention
The application provides a channel estimation method and a channel estimation device, and aims to solve the problem of how to improve the performance of the existing channel estimation algorithm.
In order to achieve the above object, the present application provides the following technical solutions:
a method of channel estimation, comprising:
performing singular value decomposition on the original observation matrix;
obtaining a reconstructed observation matrix according to the singular value decomposition result;
estimating sparsity, wherein the sparsity is the number of non-zero elements in a union set of a preset channel impulse response estimated value and an index set omega, and omega is generated according to the reconstructed observation matrix and a preset residual vector;
and performing channel estimation by using the sparsity.
Optionally, the performing singular value decomposition on the original observation matrix includes:
performing singular value decomposition operation on the original observation matrix A:wherein the first diagonal matrix
The obtaining of the reconstructed observation matrix according to the singular value decomposition result comprises:
calculating the mean value of the elements contained in the first diagonal matrix Delta
Constructing a second diagonal matrixWherein
Constructing a reconstructed observation matrix
Optionally, the estimating sparsity comprises:
initialization residual vector r ═ YPAnd channel impulse response estimationWherein, YPIs a pilot signal of a receiving end;
selecting atoms meeting the condition to form an index set omega ═ { j: | v (j) | ≧ τ | | r | | non-conducting electricity2And (c) the step of (c) in which,representsIs the absolute value of the numerical value at the jth position in v, | r | | | y calculation2Is a second-order norm of r, and tau is a preset constant;
determining the sparsity as a channel impulse response estimateThe number of non-zero elements in the union with the index set omega.
Optionally, the performing channel estimation by using the sparsity comprises:
initializing residual vectorsr0R, and a channel tap coefficient index set
Sequentially carrying out j-th channel estimation according to the following modes: computing an index setSelecting the largest P atoms in u, and calculating updated index set gamma-lambda ∪ supp (h)j-1) (ii) a And (3) performing channel estimation by using a least square method:n is a preset iteration termination number, and N is determined according to the sparsity.
Optionally, after the performing channel estimation by using the least square method, the method further includes:
clipping is performed, preserving the largest P channel estimates: h isj＝x|P；
And updating a residual vector:
if rj||2≥||rj-1||2Then by reassigning r ═ rj-1Andre-estimating the sparsity;
and re-estimating the channel by using the re-estimated sparsity.
A channel estimation apparatus, comprising:
the decomposition module is used for carrying out singular value decomposition on the original observation matrix;
the reconstruction module is used for obtaining a reconstruction observation matrix according to the singular value decomposition result;
the sparsity estimation module is used for estimating sparsity, wherein the sparsity is the number of non-zero elements in a union set of a preset channel impulse response estimated value and an index set omega, and omega is generated according to the reconstructed observation matrix and a preset residual vector;
and the channel estimation module is used for carrying out channel estimation by utilizing the sparsity.
Optionally, the decomposing module is configured to perform singular value decomposition on the original observation matrix, and includes:
the decomposition module is specifically configured to perform singular value decomposition operation on the original observation matrix a:wherein the first diagonal matrix
The reconstruction module is used for obtaining a reconstruction observation matrix according to the singular value decomposition result, and comprises:
the reconstruction module is specifically configured to calculate a mean value of elements included in the first diagonal matrix ΔConstructing a second diagonal matrixWhereinConstructing a reconstructed observation matrix
Optionally, the sparseness estimating module is configured to estimate sparseness and includes:
the sparsity estimation module is specifically configured to initialize a residual vector r ═ YPAnd channel impulse response estimationWherein, YPIs a pilot signal of a receiving end; selecting atoms meeting the condition to form an index set omega ═ { j: | v (j) | ≧ τ | | r | | non-conducting electricity2And (c) the step of (c) in which,representsIs the absolute value of the numerical value at the jth position in v, | r | | | y calculation2Is a second-order norm of r, and tau is a preset constant; determining the sparsity as a channel impulse response estimateThe number of non-zero elements in the union with the index set omega.
Optionally, the channel estimation module is configured to perform channel estimation using the sparsity, and includes:
the channel estimation module is specifically configured to initialize a residual vector r0R, and a channel tap coefficient index setSequentially carrying out j-th channel estimation according to the following modes: computing an index setSelectinguThe maximum P atoms in the index set are lambda-supp (u | P), and the updated index set gamma- ∪ supp (h)j-1) (ii) a And (3) performing channel estimation by using a least square method:wherein j ═1. N, N is a preset number of iteration stops, and N is determined according to the sparsity.
Optionally, the method further comprises:
an accuracy control module for clipping after the channel estimation using least squares, preserving the largest P channel estimates: h isj＝x|P(ii) a And updating a residual vector:if rj||2≥||rj-1||2Then by reassigning r ═ rj-1Andre-estimating the sparsity; and re-estimating the channel by using the re-estimated sparsity.
According to the channel estimation method and device, singular value decomposition is carried out on an original observation matrix, a reconstructed observation matrix is obtained according to the result of the singular value decomposition, and sparsity is estimated, wherein the sparsity is the number of nonzero elements in a union set of a preset channel impulse response estimated value and an index set omega, omega is generated according to the reconstructed observation matrix and a preset residual vector, and finally, the sparsity is utilized for channel estimation. The singular value decomposition has the characteristic of stronger noise robustness, so the method and the device have stronger noise robustness, and the method and the device can estimate the sparsity without knowing the sparsity, so the method and the device have higher practicability, and therefore, the channel estimation method and the device have higher performance.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of the sparsity of a channel in an OFDM system in the time domain;
fig. 2 is a flowchart of a channel estimation method disclosed in an embodiment of the present application;
fig. 3 is a flowchart of another channel estimation method disclosed in the embodiments of the present application;
fig. 4 is a flowchart of another channel estimation method disclosed in the embodiments of the present application;
fig. 5 is a schematic structural diagram of a channel estimation device disclosed in an embodiment of the present application.
Detailed Description
The embodiment of the application discloses a channel estimation method and a channel estimation device, which can be applied to an OFDM system and aims to improve the channel estimation performance in the OFDM system. As shown in fig. 1, a channel to be estimated in an OFDM system has a sparse characteristic in a time domain.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The channel estimation method disclosed in the embodiment of the present application, as shown in fig. 2, includes the following steps:
s201: performing singular value decomposition on the original observation matrix;
the original observation matrix is directly determined by the pattern of the pilot inserted at the transmitting end, and usually, a random pilot pattern is selected to meet the accuracy of channel estimation.
It is assumed that the vector formed by the received pilot symbols is the transmitted pilot symbolsNumber isThe observation matrix isNoise vector η, channel impulseThe response vector is h, NpA partial DFT matrix of x L. The output signal to input signal relationship is:
s202: obtaining a reconstructed observation matrix according to the singular value decomposition result;
s203: estimating sparsity, wherein the sparsity is the number of non-zero elements in a union set of a preset channel impulse response estimated value and an index set omega, and omega is generated according to the reconstructed observation matrix and a preset residual vector;
s204: and performing channel estimation by using the sparsity.
In the method of this embodiment, the characteristic that singular value decomposition has strong noise robustness is utilized, the singular value decomposition is utilized to decompose the original observation matrix, and channel estimation is performed by utilizing the result after the singular value decomposition, so that the result of channel estimation has strong noise robustness, and meanwhile, sparsity is estimated without depending on known sparsity, so that the practicability is improved.
Another channel estimation method disclosed in the embodiment of the present application focuses on explaining a specific implementation process of steps in the embodiment shown in fig. 2.
As shown in fig. 3, the method of this embodiment includes the following steps:
s301: performing singular value decomposition operation on the original observation matrix A:
wherein the first diagonal matrixU and VHIs an odd of AAnd (5) obtaining a matrix in the process of singular value decomposition.
S302: calculating the mean value of the elements contained in the first diagonal matrix Delta
S303: constructing a second diagonal matrix therein
S304: constructing a reconstructed observation matrix
S305: initialization residual vector r ═ YPAnd channel impulse response estimation
Wherein, YPIs a pilot signal at the receiving end.
S306: selecting atoms meeting the condition to form an index set omega ═ { j: | v (j) | ≧ τ | | r | | non-conducting electricity2}；
Wherein,representsIs the absolute value of the numerical value at the jth position in v, | r | | | y calculation2Is a second-order norm of r, τ is a preset constant, and in this embodiment, τ may have a value range of [2.5, 3%]。
In this embodiment, the condition may be: | v (j) | ≧ τ | | | r | | non-woven phosphor2。
S307: determining sparsity as channel impulse response estimateNumber of non-zero elements in union with index set omega, i.e. sparsity
S308: initializing an iterative loop residual vector r0R, and a channel tap coefficient index set
S309: sequentially carrying out j-th channel estimation according to the following modes:
1. computing an index set for a jth channel estimateThe largest P atoms in u are selected: Λ = supp (u | P);
2. calculate the updated index set Γ ═ Λ ∪ supp (h)j-1)；
3. And (3) performing channel estimation by using a least square method:
n is a preset iteration termination number, and N is determined according to the sparsity, where N may be 2 times the sparsity in this embodiment.
It can be seen from the above process that, in this embodiment, the original observation matrix is optimized by using singular value decomposition, and then the adaptive matching tracking algorithm is used to perform sparse channel estimation, which not only does not require known sparsity, but also has strong noise robustness and high estimation accuracy.
Compared with the embodiment, the channel estimation algorithm disclosed by the embodiment of the application can detect whether the sparsity estimation value is accurate or not, and adopts a corresponding processing means.
As shown in fig. 4, the method of this embodiment includes the following specific steps:
s401: performing singular value decomposition operation on the original observation matrix A:
wherein the first diagonal matrixU and VHIs an odd of AAnd (5) obtaining a matrix in the process of singular value decomposition.
S402: calculating the mean value of the elements contained in the first diagonal matrix Delta
S403: constructing a second diagonal matrix therein
S404: constructing a reconstructed observation matrix
S405: initialization residual vector r ═ YPAnd channel impulse response estimation
Wherein, YPIs a pilot signal at the receiving end.
S406: selecting the non-woven belts satisfying the condition | v (j) | ≧ τ | | | r |2The index set omega ═ j: | | v (j) | ≧ τ | | | r | | non-calculation2}；
Wherein,representsIs the absolute value of the numerical value at the jth position in v, | r | | | y calculation2Is a second-order norm of r, τ is a preset constant, and in this embodiment, τ may have a value range of [2.5, 3%]。
S407: determining the sparsity as a channel impulse response estimateNumber of non-zero elements in union with index set omega, i.e. sparsity
S408: number of initialization iterations j equals 1, residual vector r0R, and a channel tap coefficient index set
S409: computing the index set for the jth iteration
S410: the largest P atoms in u are selected, i.e.: Λ = supp (u | P);
s411: by merging sets Λ and supp (h)j-1) The updated index set, i.e., Γ ═ Λ ∪ supp (h) is obtainedj-1)；
S412: channel estimation using least squares
S413: clipping the initial estimate x, keeping the largest P channel estimates: h isj＝x|P；
S414: and updating a residual vector:
s415: judge rj||2≥||rj-1||2If yes, executing S416, otherwise executing S417;
s416: re-assigning r ═ rj-1Andreturning to execute S407;
that is, the residual becomes large, which indicates that the sparsity estimation is not accurate, and the sparsity needs to be re-estimated.
S417: judging whether j is true or not, if yes, executing S419, and if not, executing S418;
and N is a preset iteration termination frequency and is determined according to the sparsity.
S418: j equals j +1, and the step returns to execute S409;
s419: a channel response estimate is output.
In this embodiment, if the estimated sparsity is inaccurate, re-estimation is performed, and therefore, the accuracy of channel estimation can be improved.
Corresponding to the above method embodiment, the embodiment of the present application further discloses a channel estimation apparatus, as shown in fig. 5, including:
a decomposition module 501, configured to perform singular value decomposition on the original observation matrix;
a reconstruction module 502, configured to obtain a reconstructed observation matrix according to the singular value decomposition result;
a sparsity estimation module 503, configured to estimate a sparsity, where the sparsity is a number of non-zero elements in a union of a preset channel impulse response estimate and an index set Ω, where Ω is generated according to the reconstructed observation matrix and a preset residual vector;
a channel estimation module 504, configured to perform channel estimation by using the sparsity.
Specifically, the specific implementation manner of the decomposition module performing singular value decomposition on the original observation matrix may be: to the originalPerforming singular value decomposition operation on the initial observation matrix A:wherein the first diagonal matrix
Specifically, the reconstruction module obtains a specific implementation method of the reconstructed observation matrix according to the singular value decomposition resultThe formula can be: calculating the mean value of the elements contained in the first diagonal matrix DeltaStructure of the deviceSecond diagonal matrixWhereinConstructing a reconstructed observation matrix
The specific implementation manner of the sparsity estimation module for estimating sparsity may be as follows: initialization residual vector r ═ YPAnd channel impulse response estimationWherein, YPIs a pilot signal of a receiving end; selecting atoms meeting the condition to form an index set omega ═ { j: | v (j) | ≧ τ | | r | | non-conducting electricity2Therein ofRepresentsIs the absolute value of the numerical value at the jth position in v, | r | | | y calculation2Is a second-order norm of r, and tau is a preset constant; determining the sparsity as a channel impulse response estimateThe number of non-zero elements in the union with the index set omega.
The specific implementation manner of the channel estimation module using the sparsity to perform channel estimation may be: initializing a residual vector r0R, and a channel tap coefficient index setSequentially carrying out j-th channel estimation according to the following modes: computing an index setThe largest P atoms in u are selected: Λ = supp (u | P); after calculation updateIndex set of (h) ═ Λ ∪ supp (h)j-1) (ii) a And (3) performing channel estimation by using a least square method:n is a preset iteration termination number, and N is determined according to the sparsity.
Optionally, the apparatus in this embodiment may further include:
an accuracy control module 505, configured to perform clipping after the channel estimation by using the least square method, and retain the maximum P channel estimates: h isj＝x|P(ii) a And updating a residual vector:if rj||2≥||rj-1||2Then by reassigning r ═ rj-1Andre-estimating the sparsity; and re-estimating the channel by using the re-estimated sparsity.
The device of the embodiment optimizes the original observation matrix by using singular value decomposition, and then estimates the sparse channel by using the adaptive matching tracking algorithm, so that the known sparsity is not needed, and the device has stronger noise robustness and higher estimation precision.
The functions described in the method of the embodiment of the present application, if implemented in the form of software functional units and sold or used as independent products, may be stored in a storage medium readable by a computing device. Based on such understanding, part of the contribution to the prior art of the embodiments of the present application or part of the technical solution may be embodied in the form of a software product stored in a storage medium and including several instructions for causing a computing device (which may be a personal computer, a server, a mobile computing device or a network device) to execute all or part of the steps of the method described in 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 (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A method of channel estimation, comprising:
performing singular value decomposition on the original observation matrix; wherein, the original observation matrix is directly determined by the pattern of the pilot frequency inserted by the sending end;
obtaining a reconstructed observation matrix according to the singular value decomposition result;
estimating sparsity, wherein the sparsity is the number of non-zero elements in a union set of a preset channel impulse response estimated value and an index set omega, and omega is generated according to the reconstructed observation matrix and a preset residual vector;
performing channel estimation by using the sparsity;
the channel estimation by using the sparsity comprises:
initializing a residual vector r0R, and a channel tap coefficient index set
Sequentially carrying out j-th channel estimation according to the following modes: computing an index setSelecting the largest P atoms in u, and calculating updated index set gamma-lambda ∪ supp (h)j-1) (ii) a And (3) performing channel estimation by using a least square method:n is a preset iteration termination number, and N is determined according to the sparsity; y isPIs a pilot signal of a receiving end; a is an observation matrix.
2. The method of claim 1, wherein the performing a singular value decomposition of the original observation matrix comprises:
performing singular value decomposition operation on the original observation matrix A:wherein the first diagonal matrix
The obtaining of the reconstructed observation matrix according to the singular value decomposition result comprises:
calculating the mean value of the elements contained in the first diagonal matrix DeltaNpThe number of the non-zero singular values of the original observation matrix A is taken as the number;
constructing a second diagonal matrixWherein
Constructing a reconstructed observation matrix
3. The method of claim 2, wherein estimating sparsity comprises:
initialization residual vector r ═ YPAnd channel impulse response estimationWherein, YPIs a pilot signal of a receiving end;
selecting atoms meeting the condition to form an index set omega ═ { j: | v (j) | ≧ τ | | r | | non-conducting electricity2And (c) the step of (c) in which, representsIs the absolute value of the numerical value at the jth position in v, | r | | | y calculation2Is a second-order norm of r, and tau is a preset constant;
determining the sparsity as a channel impulse response estimateThe number of non-zero elements in the union with the index set omega.
4. The method of claim 1, further comprising, after said performing channel estimation using least squares,:
clipping is performed, preserving the largest P channel estimates: h isj＝x|P(ii) a x is an initial estimate;
and updating a residual vector:
if rj||2≥||rj-1||2Then by reassigning r ═ rj-1Andre-estimating the sparsity;
and re-estimating the channel by using the re-estimated sparsity.
5. A channel estimation device, comprising:
the decomposition module is used for carrying out singular value decomposition on the original observation matrix; wherein, the original observation matrix is directly determined by the pattern of the pilot frequency inserted by the sending end;
the reconstruction module is used for obtaining a reconstruction observation matrix according to the singular value decomposition result;
the sparsity estimation module is used for estimating sparsity, wherein the sparsity is the number of non-zero elements in a union set of a preset channel impulse response estimated value and an index set omega, and omega is generated according to the reconstructed observation matrix and a preset residual vector;
a channel estimation module for performing channel estimation by using the sparsity;
the channel estimation module is configured to perform channel estimation using the sparsity, and includes:
the channel estimation module is specifically configured to initialize the residualsDifference vector r0R, and a channel tap coefficient index setSequentially carrying out j-th channel estimation according to the following modes: computing an index setSelecting the largest P atoms in u, and calculating updated index set gamma-lambda ∪ supp (h)j-1) (ii) a And (3) performing channel estimation by using a least square method:n is a preset iteration termination number, and N is determined according to the sparsity; y isPIs a pilot signal of a receiving end; a is an observation matrix.
6. The apparatus of claim 5, wherein the decomposition module configured to perform singular value decomposition on the original observation matrix comprises:
the decomposition module is specifically configured to perform singular value decomposition operation on the original observation matrix a:wherein the first diagonal matrix
The reconstruction module is used for obtaining a reconstruction observation matrix according to the singular value decomposition result, and comprises:
the reconstruction module is specifically configured to calculate a mean value of elements included in the first diagonal matrix ΔNpThe number of the non-zero singular values of the original observation matrix A is taken as the number; constructing a second diagonal matrixWhereinConstructing a reconstructed observation matrix
7. The apparatus of claim 6, wherein the sparsity estimation module is configured to estimate sparsity comprising:
the sparsity estimation module is specifically configured to initialize a residual vector r ═ YPAnd channel impulse response estimationWherein, YPIs a pilot signal of a receiving end; selecting atoms meeting the condition to form an index set omega ═ { j: | v (j) | ≧ τ | | r | | non-conducting electricity2And (c) the step of (c) in which, representsIs the absolute value of the numerical value at the jth position in v, | r | | | y calculation2Is a second-order norm of r, and tau is a preset constant; determining the sparsity as a channel impulse response estimateThe number of non-zero elements in the union with the index set omega.
8. The apparatus of claim 5, further comprising:
an accuracy control module for clipping after the channel estimation using least squares, preserving the largest P channel estimates: h isj＝x|P(ii) a x is an initial estimate; and updating a residual vector:if rj||2≥||rj-1||2Then by reassigning r ═ rj-1Andre-estimating the sparsity; and re-estimating the channel by using the re-estimated sparsity.
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