CN113259278A - Millimeter wave multi-antenna channel estimation method and device - Google Patents
Millimeter wave multi-antenna channel estimation method and device Download PDFInfo
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- H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
Abstract
The application discloses a millimeter wave multi-antenna channel estimation method, which comprises the following steps: splitting a matrix for channel estimation into m operation sub-matrices, wherein the operation sub-matrices are: and the ith sub-vector of the measurement vector of the observation signal is equal to the product of the ith first sub-matrix split by the first matrix and the channel estimation result to be estimated, i is a natural number which is more than or equal to 1 and less than or equal to m, the channel estimation sub-results of the m operation sub-matrices are respectively calculated in parallel to obtain m channel estimation sub-results, and the m channel estimation sub-results are averaged to obtain the channel estimation result to be estimated. The invention realizes distributed processing based on computing resources and is beneficial to reducing the running time of channel estimation. Through iterative convergence of each operation submatrix, the error between the channel estimation value and the true value is reduced, the reliability of channel estimation is improved, and the running time of the channel estimation is further reduced.
Description
Technical Field
The invention relates to the field of wireless communication, in particular to a millimeter wave multi-antenna channel estimation method.
Background
The channel estimation is an essential link for a wireless communication link, is particularly important for a large-scale multi-antenna system, and is a precondition for fully utilizing the multi-antenna gain of the system. In a large-scale multi-antenna system, operations such as precoding, signal detection, resource allocation and the like all require accurate Channel State Information (CSI).
At present, millimeter wave large-scale multi-antenna has definite application prospect in future mobile communication system, and channel estimation is a key link in millimeter wave large-scale multi-antenna scene. However, due to the increase of the antenna size, how to obtain accurate CSI in real time is a great challenge.
In millimeter wave multi-antenna channels, channel estimation simultaneously faces the challenge of channel sparsity, and the channels need to be described according to the sparsity characteristics of millimeter wave channels, limited arrival angles, departure angles and path gains.
Under non-sparse conditions, the channel estimation algorithm mainly includes a Least Square (LS) method and a Minimum Mean Square Error (MMSE) method. Due to the very complicated channel characteristics, the realization of these methods often needs to use long pilot sequences and large-scale channel coefficients, which is not practical for practical millimeter-wave large-scale multiple-input multiple-output (MIMO) systems.
In the millimeter wave massive MIMO system, since signal transmission is distributed on a part of paths, channels are sparse in time and angle, so that most of sparse channel estimation algorithms at present are based on a Compressive Sensing (CS) principle. In millimeter wave channel estimation based on compressed sensing, a multistage process can be used to avoid exhaustive search, but due to limited transmission power, the use of a wide beam is limited, and the method is difficult to apply to an actual channel. Common millimeter wave multi-antenna channel estimation is roughly of two types: one is a non-Bayesian based algorithm, such as Orthogonal Matching Pursuit (OMP), which is an iterative algorithm that finds a sub-optimal solution, and the other is a Bayesian based algorithm that applies an estimation technique to identify a desired Sparse solution, such as Sparse Bayesian Learning (SBL) and Bayesian Compressive Sensing (BCS), using Bayesian methods and appropriate statistical assumptions.
The existing channel estimation algorithm is generally a serial algorithm, because a millimeter wave large-scale antenna is provided with a large-scale antenna array, a measurement matrix and a complete dictionary matrix related to channel estimation have quite large scales, and simultaneously, because the complexity of matrix operation increases exponentially along with the increase of the matrix scale, a large amount of time is consumed by utilizing the serial algorithm, and the time complexity is high.
Disclosure of Invention
The invention provides a millimeter wave multi-antenna channel estimation method, which is used for reducing the running time of channel estimation.
The millimeter wave multi-antenna channel estimation method provided by the invention is realized as follows:
splitting a first matrix for channel estimation into m operation submatrices, wherein the operation submatrices are: the ith sub-vector of the measurement vector of the observation signal is equal to the product of the ith first sub-matrix split by the first matrix and the channel estimation result to be estimated, i is a natural number which is more than or equal to 1 and less than or equal to m,
respectively calculating the channel estimation sub-results of the m operation sub-matrixes in parallel to obtain m channel estimation sub-results,
and averaging the m channel estimation sub-results to obtain a channel estimation result to be estimated.
The present invention also provides a millimeter wave multi-antenna channel estimation apparatus, comprising,
a decomposition unit, configured to split the first matrix for channel estimation into m operator matrices, where the operator matrices are: the ith sub-vector of the measurement vector of the observation signal is equal to the product of the ith first sub-matrix split by the first matrix and the channel estimation result to be estimated, i is a natural number which is more than or equal to 1 and less than or equal to m,
m first calculation units, each for calculating the channel estimation sub-results of the m operation sub-matrices in parallel to obtain m channel estimation sub-results,
and the second calculation unit is used for averaging the m channel estimation sub-results to obtain a channel estimation result to be estimated.
According to the millimeter wave multi-antenna channel estimation method provided by the invention, a large-scale matrix for channel estimation is divided into a plurality of small-scale operation sub-matrixes, so that distributed processing can be carried out according to computing resources, channel estimation sub-results are respectively obtained through each operation sub-matrix in parallel, and the running time of channel estimation is favorably reduced. Furthermore, through iterative convergence of each operation submatrix, the error between the channel estimation value and the true value is reduced, the reliability of channel estimation is improved, and the running time of the channel estimation is further reduced. The method and the device can be suitable for various application scenes by adjusting the number of the operation submatrices, and are high in flexibility.
Drawings
Fig. 1 is a schematic flow chart of a millimeter wave multi-antenna channel estimation method.
Fig. 2 is a schematic flow chart of solving channel estimation in a parallel manner.
Fig. 3 is a diagram illustrating convergence characteristics in solving for channel estimation.
Fig. 4 is a diagram illustrating the convergence of channel estimation with different noise powers.
Fig. 5 is a schematic diagram of a probability density curve of error distribution at different iteration numbers.
Fig. 6 is a schematic diagram of the millimeter wave multi-antenna channel estimation apparatus according to the present application.
Detailed Description
For the purpose of making the objects, technical means and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings.
The millimeter wave multi-antenna channel estimation method divides a large-scale matrix for channel estimation into a plurality of small-scale operation sub-matrixes, completes operation on each operation sub-matrix in parallel through distributed computation, and carries out iterative processing on channel estimation sub-results of each operation sub-matrix, gradually converges, and reduces errors until the error is similar to a correct solution.
Referring to fig. 1, fig. 1 is a schematic flow chart of a millimeter wave multi-antenna channel estimation method. The method comprises the steps of (1) carrying out,
and 103, averaging the m channel estimation sub-results to obtain a channel estimation result to be estimated.
The channel estimation method provided by the application is based on distributed computation, the reliability of a computation result is guaranteed, the channel estimation speed can be effectively improved, and the number and scale of operation sub-matrixes can be adjusted according to computation resources, so that the method is suitable for different application scenarios and high in flexibility.
The following detailed description is provided to facilitate understanding of the invention.
The signal matrix X is transmitted from a transmitting end, passes through a precoding matrix F, is transmitted through a channel matrix H, reaches a receiving end, first passes through a combining matrix W, and is received by the receiving end to obtain an observation matrix Y. During the channel transmission, the signal will be superimposed with white gaussian noise Q. Thus, the observation matrix Y can be expressed as:
wherein the content of the first and second substances,
c is a set of plural numbers, NrFor the number of receiving antennas, NtFor the number of transmitting antennas, MrTo receive a measurement sequence, MtFor transmitting measurement sequencesColumn, signal matrix X is of scale MtWith power P. The noise matrix Q is a complex noise matrix with a power of PnThe real and imaginary matrix scales are both Mr×MtThe noise matrix is randomly generated.
After vectorizing the signal, obtaining an observation signal expression after vectorization:
wherein A isDFor a dictionary matrix, each column represents the Kronecker product:
to know
Respectively the kth and jth angles, h, corresponding to the dictionary matrixbIs the channel gain vector under the dictionary matrix; and nQ is a noise vector after the complex noise matrix is vectorized.
Defining a first matrix
The above formula can be further rewritten as:
yv＝Ahb+nQ
thus, the channel estimation problem can be translated into a channel estimation by the first matrix A and the measurement vector yvTo estimate hbThe problem is solved by the matrix of (1). Since the channel estimation is affected by noise, the above equation is rewritten as:
in order to conveniently adopt distributed parallel solving, large-scale matrix operation is carried out
Splitting into m sub-matrix operations, wherein the number of m can be determined according to computing resources, and any operation sub-matrix i is
Each operator matrix can be calculated by a respective calculation unit, and i is a natural number equal to or greater than 1 and equal to or less than m.
It should be understood that the computing unit may be for physical devices, or may be a computing task distributed in each physical device or in the same physical device.
Thus, the large-scale matrix operation is decomposed into parallel operations of m operator matrices, wherein the operator matrices are: i-th subvector y of the measurement vector of the observed signaliEqual to the ith first sub-matrix A divided by the first matrixiAnd the product of the channel estimation result to be estimated. Expressed mathematically as:
referring to fig. 2, fig. 2 is a schematic flow chart of solving channel estimation in a parallel manner. The method comprises the steps of (1) carrying out,
Thus, the channel estimation sub-results of m operation sub-matrixes are obtained in total,
Expressed mathematically as:
one of the embodiments of the method for converging the channel estimation result calculated by the current iteration of the operation submatrix is as follows:
updating the difference between the channel estimation sub-result calculated last time of the operation sub-matrix and the channel estimation result of the last iteration by using iteration update parameters to obtain an updated value; accumulating the update value on the basis of the channel estimation sub-result calculated last time by the operation sub-matrix; wherein, the update parameter is the product of the conjugate of the first parameter and the conjugate of the second parameter, the first parameter is related to the maximum eigenvalue and the minimum eigenvalue of the set second matrix, and the second parameter is related to the first sub-matrix.
Thus, for the channel estimation sub-result of the i-th iteration calculated by the operation sub-matrix i, the following formula can be expressed as follows:
wherein, the first parameter used for iterative update is gamma, and the second parameter is the first sub-matrix A in the operation sub-matrixiZero space projection of (1), noted as betai，
I is an identity matrix and is a matrix of the identity,
l is more than or equal to 1 and less than or equal to L which is a set iteration threshold.
And step 204, carrying out weighted summation on the channel estimation sub-results of the current iteration calculated by the m operation sub-matrixes i to obtain the channel estimation result of the current iteration.
One of the weighted summation modes is as follows: taking the third parameter for weighted iteration updating as a weighting coefficient, weighting the average value of the channel estimation sub-results of the current iteration calculated by the m operation sub-matrixes i, accumulating the weighting result and the channel estimation result of the previous iteration,
preferably, the channel estimation result of the previous iteration is weighted by using the remaining weights, and the weighted channel estimation result of the previous iteration and the weighted result are accumulated.
The third parameter is related to a maximum eigenvalue and a minimum eigenvalue of the second matrix.
In this way, the channel estimation sub-results of the l iteration calculated by the m operation sub-matrices i are subjected to weighted summation to obtain the channel estimation result of the l iteration, which can be expressed by a mathematical expression:
wherein the third parameter is η.
The first parameter and the third parameter are obtained by the following method:
setting a second matrix expressed by the following mathematical formula:
obtaining the maximum eigenvalue mu of the matrix according to the second matrixmaxAnd minimum eigenvalue μminAnd constructing an equation set:
and solving the equation set to obtain a first parameter and a third parameter. Wherein the third parameter is a real number and the first parameter is within 0 to 2.
And step 205, accumulating the current iteration times, returning to step 203 until the accumulated iteration times reach a set iteration threshold, and outputting a final channel estimation result.
Referring to fig. 3 to 5, fig. 3 to 5 are schematic diagrams illustrating the performance of the preliminary demonstration method through numerical simulation. The number of F matrixes and W matrixes involved in the simulation is less than 4; number of transmitted measurement sequences MtThe number of received measurement sequences Mr is 8 respectively; number of transmitting antennas NtAnd number of receiving antennas NrIs 8; when the calculation units are divided, 8 calculation units are adopted for parallel calculation; the transmission angle is selected to be 0 degrees and 90 degrees, and the receiving angle is selected to be 45 degrees and 135 degrees.
The complex noise matrix Q and the first matrix A are random matrices, so that in order to avoid the accidental of experiments, simulation results are drawn by adopting a method of calculating and averaging for many times, and the accidental errors of the experiments are reduced as much as possible. The attached simulation graphs are drawn by adopting a method of calculating 500 times of averaging.
According to the simulation diagram, the problem of large-scale multi-antenna channel estimation can be effectively solved through lower iteration times. By increasing the number of iterations, the estimated value can be gradually converged toward the true value. When the number of iterations increases to a certain extent (e.g., l > 15), the channel estimate has substantially converged, after which the number of iterations no longer plays a decisive role in reducing the error between the channel estimate and the true value, which error will mainly depend on the magnitude of the noise power.
The invention can be flexibly changed aiming at antenna arrays and channels with different scales. It is mainly characterized in that: 1. and (3) dividing the operation submatrix 2 and selecting parameters. When the size of the transmitting signal matrix and the measuring signal matrix is large, various division options are available, for example, a matrix of 16 × 16 is selected, and the matrix can be divided into 8 calculation units for operation, or can be selected to be divided into 4 machines for operation. For different application scenarios, the requirements for algorithm precision and convergence speed are different, so that different division modes can be selected for channel estimation. The same reasoning is also adopted when the parameters are selected, the selected parameters are different in the matrixes with different scales, and the convergence speed of the matrixes can be changed by changing the parameters.
Referring to fig. 6, fig. 6 is a schematic diagram of the millimeter wave multi-antenna channel estimation apparatus according to the present invention. The device comprises a plurality of devices which are connected with each other,
a decomposition unit, configured to split the first matrix for channel estimation into m operator matrices, where the operator matrices are: the ith sub-vector of the measurement vector of the observation signal is equal to the product of the ith first sub-matrix split by the first matrix and the channel estimation result to be estimated, i is a natural number which is more than or equal to 1 and less than or equal to m,
m first calculation units, each for calculating the channel estimation sub-results of the m operation sub-matrices in parallel to obtain m channel estimation sub-results,
and the second calculation unit is used for averaging the m channel estimation sub-results to obtain a channel estimation result to be estimated.
The first computing unit may include a first computing unit,
the first calculation module is used for calculating the channel estimation sub-result of the current iteration of the operation sub-matrix according to the mode of converging the channel estimation sub-result of the current iteration of the operation sub-matrix by using the channel estimation sub-result of the last iteration of the operation sub-matrix and the channel estimation result of the last iteration;
and the iteration number control module is used for controlling the iteration number of each first calculation module.
The second calculation unit comprises a second calculation unit,
and the second calculation module is used for carrying out weighted summation on the channel estimation sub-results of the current iteration of the m operation sub-matrixes to obtain the channel estimation result of the current iteration.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A millimeter wave multi-antenna channel estimation method is characterized by comprising the following steps:
splitting a first matrix for channel estimation into m operation submatrices, wherein the operation submatrices are: the ith sub-vector of the measurement vector of the observation signal is equal to the product of the ith first sub-matrix split by the first matrix and the channel estimation result to be estimated, i is a natural number which is more than or equal to 1 and less than or equal to m,
respectively calculating the channel estimation sub-results of the m operation sub-matrixes in parallel to obtain m channel estimation sub-results,
and averaging the m channel estimation sub-results to obtain a channel estimation result to be estimated.
2. The method of claim 1, wherein said calculating the channel estimation sub-results of the m operation sub-matrices in parallel, respectively, to obtain m channel estimation sub-results, comprises:
for any one of the operator matrices i,
and calculating the channel estimation sub-result of the current iteration of the operation sub-matrix according to the mode of converging the channel estimation sub-result of the current iteration of the operation sub-matrix by using the channel estimation sub-result of the last iteration of the operation sub-matrix and the channel estimation result of the last iteration.
3. The method as claimed in claim 2, wherein said averaging the m channel estimation sub-results to obtain the channel estimation result to be estimated comprises:
weighting and summing the channel estimation sub-results of the current iteration of the m operation sub-matrixes to obtain the channel estimation result of the current iteration,
and repeatedly and respectively calculating the channel estimation sub-result of the next iteration of the m operation sub-matrixes and the channel estimation result of the next iteration until the set iteration time threshold is reached.
4. The method as claimed in claim 3, wherein said calculating the channel estimation sub-result of the current iteration of the operator matrix according to the way of converging the channel estimation sub-result of the current iteration of the operator matrix by using the channel estimation sub-result of the last iteration of the operator matrix and the channel estimation result of the last iteration of the operator matrix comprises:
updating the difference between the channel estimation sub-result of the current iteration of the operation sub-matrix i and the channel estimation result of the current iteration by using iteration update parameters to obtain an updated value;
accumulating the updated value on the basis of the channel estimation sub-result of the current iteration of the operation sub-matrix i;
the weighted summation of the channel estimation sub-results of the current iteration calculated by the m operation sub-matrices i to obtain the channel estimation result of the current iteration includes:
taking the third parameter for updating the weighted iteration as a weighting coefficient, weighting the average value of the channel estimation sub-results of the current iteration of the m operation sub-matrixes to obtain a first weighting result,
weighting the channel estimation result of the last iteration by the residual weights to obtain a second weighting result,
and accumulating the first weighting result and the second weighting result to obtain a channel estimation result of the iteration.
5. The method as claimed in claim 4, wherein said updating the difference between the sub-result of channel estimation of the current iteration of said operator matrix and the result of channel estimation of the current iteration by using an iteration update parameter to obtain an updated value comprises:
multiplying the conjugate of the first parameter for iterative update and the conjugate of the second parameter corresponding to the operator sub-matrix by the difference between the channel estimation sub-result of the current iteration of the operator sub-matrix and the channel estimation result of the current iteration,
wherein the content of the first and second substances,
the first parameter is related to the maximum eigenvalue and the minimum eigenvalue of the set second matrix,
the second parameter is associated with a first sub-matrix of the operator sub-matrices.
6. The method of claim 5 wherein the second parameter is a null-space projection of a first one of the operator submatrices,
the first parameter and the third parameter are obtained through the following method:
constructing a second matrix by using a first sub-matrix in the operation sub-matrices, wherein the second matrix is as follows:
wherein A isiIs the first sub-matrix of the operator sub-matrix i,
is the first in the operator matrix iA transposed matrix of the sub-matrices, m being the number of the operation sub-matrices;
obtaining the maximum eigenvalue and the minimum eigenvalue of the matrix according to the second matrix,
and constructing the following equation set by using the maximum characteristic value and the minimum characteristic value:
wherein, mumaxIs the maximum eigenvalue, muminIs the minimum eigenvalue, η is the third parameter, γ is the first parameter,
and solving to obtain a first parameter and a third parameter according to the equation set.
7. The method of claim 6, wherein the zero-space projection of the first one of the operator submatrices is:
wherein, I is an identity matrix,
the first matrix is:
wherein, P is the power of the transmitted signal,
is a transposed matrix of the precoding matrix at the transmitting end,
combining the conjugates of the matrices for the transmitting end, ADIs a dictionary matrix.
8. A millimeter wave multi-antenna channel estimation apparatus, comprising,
a decomposition unit, configured to split the first matrix for channel estimation into m operator matrices, where the operator matrices are: the ith sub-vector of the measurement vector of the observation signal is equal to the product of the ith first sub-matrix split by the first matrix and the channel estimation result to be estimated, i is a natural number which is more than or equal to 1 and less than or equal to m,
m first calculation units, each for calculating the channel estimation sub-results of the m operation sub-matrices in parallel to obtain m channel estimation sub-results,
and the second calculation unit is used for averaging the m channel estimation sub-results to obtain a channel estimation result to be estimated.
9. The apparatus of claim 8, wherein the first computing unit comprises,
the first calculation module is used for calculating the channel estimation sub-result of the current iteration of the operation sub-matrix according to the mode of converging the channel estimation sub-result of the current iteration of the operation sub-matrix by using the channel estimation sub-result of the last iteration of the operation sub-matrix and the channel estimation result of the last iteration;
and the iteration number control module is used for controlling the iteration number of each first calculation module.
10. The apparatus of claim 8, wherein the second computing unit comprises,
and the second calculation module is used for carrying out weighted summation on the channel estimation sub-results of the current iteration of the m operation sub-matrixes to obtain the channel estimation result of the current iteration.
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