CN115277315B - LMMSE channel estimation method, device and signal processing system - Google Patents

LMMSE channel estimation method, device and signal processing system Download PDF

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CN115277315B
CN115277315B CN202210860343.XA CN202210860343A CN115277315B CN 115277315 B CN115277315 B CN 115277315B CN 202210860343 A CN202210860343 A CN 202210860343A CN 115277315 B CN115277315 B CN 115277315B
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autocorrelation matrix
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autocorrelation
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CN115277315A (en
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冯雪林
孙陆宽
丁雅帅
钱蔓藜
胡金龙
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Beijing Sylincom Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0256—Channel estimation using minimum mean square error criteria
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms
    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods
    • H04L25/0244—Channel estimation channel estimation algorithms using matrix methods with inversion
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The application provides an LMMSE channel estimation method, an LMMSE channel estimation device and a signal processing system, wherein the method comprises the following steps: obtaining a revised autocorrelation matrix of the LMMSE estimation formula, wherein the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a Toeplitz matrix; calculating by adopting a recursive calculation method to obtain a last column vector of an inverse matrix of the revised autocorrelation matrix, wherein the inverse matrix of the revised autocorrelation matrix is a Toeplitz matrix, and the last column vector is a vector formed by elements of the last column of the inverse matrix of the revised autocorrelation matrix; according to the characteristics of the Toeplitz matrix and the last column vector, calculating to obtain an inverse matrix of the revised autocorrelation matrix; and calculating to obtain an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and an LMMSE estimation formula. The method solves the problem of high complexity of the LMMSE channel estimation method in the prior art.

Description

LMMSE channel estimation method, device and signal processing system
Technical Field
The present application relates to the field of channel estimation technologies, and in particular, to a LMMSE channel estimation method, apparatus, computer readable storage medium, processor, and signal processing system.
Background
The OFDM technology can provide abundant frequency diversity gain for users by utilizing the multipath effect of a wireless channel, can provide broadband high-speed data transmission capability, and is widely applied to wireless communication systems such as 4G, 5G and the like. The channel estimation is used as an important receiver algorithm of the system, and the receiving end can demodulate and recover the information sent by the sending end by acquiring the detailed information of the channel, so that the accuracy of the channel estimation directly influences the performance of the whole system.
The traditional channel estimation method has a least square method (LS) and a linear minimum mean square error algorithm (LMMSE), wherein the complexity of the LMMSE is low, but the influence of noise is not considered, and the requirements of data transmission in an actual system on reliability and high speed cannot be met; the latter calculates an LMMSE filter matrix W (as shown in formula 1) by acquiring an autocorrelation matrix and a cross correlation matrix of the channel based on a minimized estimation error criterion, and filters the channel value estimated at the pilot frequency by using W, so that superior demodulation performance is provided compared with LS algorithm due to the utilization of the second-order statistical characteristic of the wireless channel, but the complexity of operations such as matrix inversion and filtering is higher.
Wherein R is HpHp ∈C NXN Is the autocorrelation matrix of the channel response at the pilot, To estimate the noise value.
The existing LMMSE correlation coefficient solving method comprises the following steps: storing corresponding tables locally according to the root mean square value of the channel, pilot frequency setting and noise estimation results, and selecting filter coefficients according to a table look-up of real-time calculation results in receiving; reversely pushing corresponding time domain channel tap coefficients based on a channel estimation result at a pilot frequency, constructing a correlation matrix with cyclic characteristics based on the corresponding time domain channel tap coefficients, calculating the correlation coefficients by utilizing FFT/IFFT and performing filtering operation; calculating an approximation value of inverse of a related matrix by using a p-order polynomial, decomposing the related matrix into a plurality of Toeplitz matrices, and iteratively calculating the approximation value by using each matrix corresponding to a channel tap coefficient; and carrying out SVD decomposition with the dimensionality of the channel correlation matrix as the pilot frequency length, and calculating a final filter coefficient at one time by utilizing the cycle characteristic of the matrix. However, the method has certain limitation, and the scene in 5G is more complex, so that a large amount of storage space and control judgment are needed for a table storage algorithm; the length of the filtering coefficient is pilot frequency length by utilizing the FFT/IFFT algorithm, so that the complexity of the filtering operation is still higher; the complexity is greatly increased along with the improvement of the calculation precision requirement and the increase of the multipath tap coefficient of the channel by utilizing polynomial approximation or decomposition approximation; and (3) performing correlation coefficient calculation by utilizing SVD (singular value decomposition), wherein the complexity of SVD is higher, interpolation is still needed to be completed for the value at the non-pilot frequency, and certain performance loss exists.
The above information disclosed in the background section is only for enhancement of understanding of the background art from the technology described herein and, therefore, may contain some information that does not form the prior art that is already known in the country to a person of ordinary skill in the art.
Disclosure of Invention
The application mainly aims to provide an LMMSE channel estimation method, an LMMSE channel estimation device, a computer readable storage medium, a processor and a signal processing system, so as to solve the problem of high complexity of the LMMSE channel estimation method in the prior art.
According to an aspect of an embodiment of the present application, there is provided an LMMSE channel estimation method, including: obtaining a revised autocorrelation matrix of an LMMSE estimation formula, wherein the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a Toeplitz matrix; calculating by adopting a recursive calculation method to obtain a last column vector of an inverse matrix of the revised autocorrelation matrix, wherein the inverse matrix of the revised autocorrelation matrix is the Toeplitz matrix, and the last column vector is a vector formed by elements of the last column of the inverse matrix of the revised autocorrelation matrix; calculating an inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix and the final column vector; and calculating to obtain an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula.
Optionally, calculating by using a recursive calculation method to obtain a last column vector of an inverse matrix of the revised autocorrelation matrix, including: determining a scaling factor of an inverse of the revised autocorrelation matrix based on a signal-to-noise ratio of the channel; and calculating by adopting the scaling factor as a recursion shrinkage factor of the recursion calculation method to obtain the final column vector.
Optionally, before determining the scaling factor of the inverse of the revised autocorrelation matrix based on the signal-to-noise ratio of the channel, the method further comprises: performing RMS estimation and noise estimation on the channel to obtain an effective value of signal power and an estimated value of noise power; and calculating the signal to noise ratio according to the effective value of the signal power and the estimated value of the noise power.
Optionally, determining the scaling factor of the inverse of the revised autocorrelation matrix according to the signal-to-noise ratio of the channel comprises: fitting a historical scaling factor with a historical signal-to-noise ratio to obtain a comparison table of the scaling factor and the signal-to-noise ratio; and looking up the comparison table according to the signal-to-noise ratio of the channel to obtain the scaling factor of the inverse matrix of the revised autocorrelation matrix.
Optionally, the characteristics of the Toeplitz matrix include a persymmetric characteristic, a Hermite characteristic, and an iteration characteristic, and calculating an inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix and the last column vector includes: obtaining a first row vector according to the persymmetric characteristic and the last column vector rotation, wherein the first row vector is a vector formed by elements of the first row of the inverse matrix of the revised autocorrelation matrix; determining a partial region element according to the iteration characteristic and the first row vector, wherein the partial region element comprises all elements of two diagonals of the revised autocorrelation matrix, which are close to one side of the first row; and determining all elements of the inverse matrix of the revised autocorrelation matrix according to the persymmetric characteristic and the Hermite characteristic to obtain the inverse matrix of the revised autocorrelation matrix.
Optionally, after calculating an LMMSE filter matrix according to the inverse of the revised autocorrelation matrix and the LMMSE estimation formula, the method further includes: and calculating the operation complexity of the inversion matrix of the revision autocorrelation matrix.
According to another aspect of the embodiment of the present invention, there is also provided an LMMSE channel estimation apparatus, including: the acquisition unit is used for acquiring a revised autocorrelation matrix of the LMMSE estimation formula, wherein the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a Toeplitz matrix; a first calculation unit, configured to calculate, by using a recursive calculation method, a last column vector of an inverse matrix of the revised autocorrelation matrix, where the inverse matrix of the revised autocorrelation matrix is the toplitz matrix, and the last column vector is a vector formed by elements of a last column of the inverse matrix of the revised autocorrelation matrix; a second calculation unit, configured to calculate an inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix and the last column vector; and the third calculation unit is used for calculating an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula.
According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium including a stored program, wherein the program performs any one of the methods.
According to another aspect of the embodiment of the present invention, there is further provided a processor, where the processor is configured to execute a program, and when the program is executed, perform any one of the methods.
According to another aspect of an embodiment of the present invention, there is also provided a signal processing system including: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods.
In the method for estimating the LMMSE channel, firstly, a revised autocorrelation matrix of an LMMSE estimation formula is obtained, wherein the revised autocorrelation matrix is the autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a Toeplitz matrix; then, calculating by adopting a recursive calculation method to obtain a last column vector of an inverse matrix of the revised autocorrelation matrix, wherein the inverse matrix of the revised autocorrelation matrix is the Toeplitz matrix, and the last column vector is a vector formed by elements of the last column of the inverse matrix of the revised autocorrelation matrix; then, according to the characteristics of the Toeplitz matrix and the last column vector, calculating to obtain an inverse matrix of the revised autocorrelation matrix; and finally, calculating to obtain an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula. According to the LMMSE channel estimation method, the inverse matrix of the autocorrelation matrix is revised according to the characteristics of the Toeplitz matrix, and all elements of the inverse matrix can be obtained by correspondingly rotating the last column vector according to the symmetry and iteration relation, so that the calculation complexity is greatly reduced, and the problem of high complexity of the LMMSE channel estimation method in the prior art is solved.
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The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:
FIG. 1 illustrates a flow chart of a method of LMMSE channel estimation according to one embodiment of the present application;
FIG. 2 is a schematic diagram showing the range of values of the steps of the inversion matrix without the scaling factor according to one embodiment of the application;
FIG. 3 is a schematic diagram showing the range of values of the steps of the inversion matrix with scaling factors according to one embodiment of the application;
FIG. 4 shows a comparison of the fitting values of the adjustment factors and the actual values of the stability matrix inversion results, according to one embodiment of the application;
FIG. 5 shows an inverse matrix quarter-field schematic according to one embodiment of the application;
fig. 6 shows a schematic diagram of an LMMSE channel estimation apparatus according to one embodiment of the present application.
Detailed Description
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
It will be understood that when an element such as a layer, film, region, or substrate is referred to as being "on" another element, it can be directly on the other element or intervening elements may also be present. Furthermore, in the description and in the claims, when an element is described as being "connected" to another element, the element may be "directly connected" to the other element or "connected" to the other element through a third element.
For convenience of description, the following will describe some terms or terminology involved in the embodiments of the present application:
toeplitz matrix: each matrix has the same element on the diagonal from top left to bottom right;
persymmetric characteristics: the characteristic of the matrix being diagonally symmetrical about northeast-southwest;
Hermite characteristics: the characteristic of the matrix being diagonally symmetrical about northwest-southeast;
iterative properties: each row in the matrix is a right-shifted one-bit feature of the element of the previous row.
As described in the background, in order to solve the foregoing problem, in an exemplary embodiment of the present application, an LMMSE channel estimation method, apparatus, computer readable storage medium, processor and signal processing system are provided.
According to an embodiment of the application, an LMMSE channel estimation method is provided.
Fig. 1 is a flow chart of a LMMSE channel estimation method according to an embodiment of the present application. As shown in fig. 1, the method comprises the steps of:
step S101, a revised autocorrelation matrix of an LMMSE estimation formula is obtained, wherein the revised autocorrelation matrix is the autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a Toeplitz matrix;
step S102, calculating by a recursive calculation method to obtain a last column vector of an inverse matrix of the revised autocorrelation matrix, wherein the inverse matrix of the revised autocorrelation matrix is the Toeplitz matrix, and the last column vector is a vector formed by elements of the last column of the inverse matrix of the revised autocorrelation matrix;
Step S103, calculating an inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix and the last column vector;
and step S104, calculating to obtain an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula.
In the LMMSE channel estimation method, firstly, a revised autocorrelation matrix of an LMMSE estimation formula is obtained, the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a toplitz matrix; then, calculating by adopting a recursive calculation method to obtain a last column vector of an inverse matrix of the revised autocorrelation matrix, wherein the inverse matrix of the revised autocorrelation matrix is the Toeplitz matrix, and the last column vector is a vector formed by elements of the last column of the inverse matrix of the revised autocorrelation matrix; then, according to the characteristics of the Toeplitz matrix and the last column vector, calculating to obtain an inverse matrix of the revised autocorrelation matrix; and finally, calculating to obtain an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula. According to the LMMSE channel estimation method, the inverse matrix of the autocorrelation matrix is revised according to the characteristics of the Toeplitz matrix, and all elements of the inverse matrix can be obtained by correspondingly rotating the last column vector according to the symmetry and iteration relation, so that the calculation complexity is greatly reduced, and the problem of high complexity of the LMMSE channel estimation method in the prior art is solved.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
In the method of inverting matrix without using scaling factor, let LMIn the MSE estimation equationWherein R is HpHp ∈C NXN Is the channel response H at pilot frequency p The elements of which are normalized channel correlation values of subcarrier k1 and subcarrier k2 +.>Can be abbreviated as->Easy to get->() * Represents conjugation, and is therefore a Toeplitz matrix; />To estimate the noise value, I N Is a unit diagonal array. Then T is n Also a Toeplitz matrix,wherein (1)>
T N Is the inverse of the matrix of (a)The form of (A) is as follows>Wherein r= { r 1 r 2 …r k } T ，/>Is a transfer matrix () H For conjugate transposition, a recursion calculation method is adopted to calculate and obtain a final column vector v, and a formula corresponding to the Toeplitz matrix inversion recursion calculation method is as follows：
In order to solve the last column vector v by the above recursive calculation method, firstly, the Yule-Walker equation of the complex domain needs to be solvedAnd iterative calculation method thereof>The equation set of the (k+1) order can be solved in O (k) flops, and the equation set corresponding to the Toeplitz matrix Yule-Walker equation recursion calculation method is obtained as follows:
The Toeplitz matrix inversion recursive calculation method and the Toeplitz matrix Yule-Walker equation recursive calculation method have common division operation, and the parameter beta is inverted. When (when)Is->The SNR is improved and becomes smaller rapidly, and the characteristic 1 approximately equal to |r is combined with the correlation coefficient 1 | 2 ＞|r 2 | 2 ＞…＞|r n-1 | 2 And an initial value α= -r 1 * /τ 0 First recursive reduction factor->Along with->Will greatly affect +.>Stability and difficulty in localization.
To solve the inverse matrixIn one embodiment of the present application, a final column vector of an inverse matrix of the revised autocorrelation matrix is calculated by a recursive calculation method, including: determining a scaling factor of an inverse matrix of the revised autocorrelation matrix based on a signal-to-noise ratio of the channel; and calculating by adopting the scaling factor as a recursion shrinkage factor of the recursion calculation method to obtain the final column vector. Specifically, determining a scaling factor corresponding to the signal-to-noise ratio of the channel, and calculating by using the scaling factor as a recursion reduction factor of the recursion calculation method to obtain the final column vector v, where the formula is as follows:
as shown in FIG. 2 and FIG. 3, the average value and standard deviation range before and after adjustment by using the scaling factor can be seen that the scaling factor is not used for solving the inverse matrix, and the average value range ratio of each value in the inverse matrix solving process is changed by 10 within the range of 0-65 dB of the SNR 5 After the scaling factors are used, each value in the inverse matrix solving process only needs to be shifted according to the scaling factors, and the average value range can be reduced to 10 2 In the method, the difficulty in pointing is reduced.
TABLE 1
SNR -5dB 0dB 5dB 10dB 15dB
k 0 0 1 3 4
SNR 20dB 25dB 30dB 35dB 40dB
k 6 7 9 11 12
SNR 45dB 50dB 55dB 60dB 65dB
k 14 16 17 19 21
In one embodiment of the present application, before determining the scaling factor of the inverse of the revised autocorrelation matrix based on the signal-to-noise ratio of the channel, the method further comprises: carrying out RMS estimation and noise estimation on the channel to obtain an effective value of signal power and an estimated value of noise power; and calculating the signal to noise ratio according to the effective value of the signal power and the estimated value of the noise power. Specifically, RMS estimation is performed on the channel to obtain an effective value of the signal power, noise estimation is performed to obtain an estimated value of the noise power, and thus a ratio of the effective value of the signal power to the estimated value of the noise power is calculated to obtain the signal-to-noise ratio.
In one embodiment of the present application, determining the scaling factor of the inverse of the revised autocorrelation matrix based on the signal-to-noise ratio of the channel comprises: fitting the historical scaling factor with the historical signal-to-noise ratio to obtain a comparison table of the scaling factor and the signal-to-noise ratio; and looking up the comparison table according to the signal-to-noise ratio of the channel to obtain the scaling factor of the inverse matrix of the revised autocorrelation matrix. Specifically, a scaling factor Γ is established in relation to the SNR value, then there is C is a constant, a comparison table of SNR values and scaling factors Γ is shown in Table 1, scaling factors Γ and 2 may be used n Fitting is carried out to obtain fitting factors of the table 1, and the fitting effect is shown in fig. 4, so that the difference between the actual value and the fitting value is very small, and the fitting effect is good.
In one embodiment of the present application, the characteristics of the Toeplitz matrix include persymmetric characteristics, hermite characteristics, and iteration characteristics, and the calculating the inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix and the last column vector includes: rotating according to the persymmetric characteristic and the last column vector to obtain a first row vector, wherein the first row vector is a vector formed by elements of the first row of the inverse matrix of the revised autocorrelation matrix; determining a partial region element according to the iteration characteristic and the first line vector, wherein the partial region element comprises all elements of which two diagonals of the revised autocorrelation matrix are close to one side of the first line; and determining all elements of the inverse matrix of the revised autocorrelation matrix according to the persymmetric characteristic and the Hermite characteristic to obtain the inverse matrix of the revised autocorrelation matrix. Specifically, according to the persymmetric characteristic and the iterative characteristic of the inverse matrix, the row-by-row/column rotation obtains 1/4 area elements as shown in fig. 5, and the formula is as follows:
According to the persymmetric and Hermite characteristics of the inverse matrix, all elements of the matrix are obtained, and the formula is as followsThereby obtaining an inverse matrix->
It should be noted that, according to the inverse matrixI.e. the LMMSE filter matrix w can be calculated
In one embodiment of the present application, after calculating the LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula, the method further includes: calculating the revised auto-phaseThe computational complexity of the inverse of the correlation matrix. Specifically, the complexity statistics of the above-mentioned steps for solving the inverse matrix are shown in Table 2, and the total number of complex multiplies is about 2*N 2 Is the inversion algorithm with the lowest complexity in the prior art and is a non-approximation algorithm.
TABLE 2
The embodiment of the application also provides an LMMSE channel estimation device, and the LMMSE channel estimation device can be used for executing the LMMSE channel estimation method provided by the embodiment of the application. The LMMSE channel estimation device provided by the embodiment of the present application is described below.
Fig. 2 is a schematic diagram of an LMMSE channel estimation apparatus according to an embodiment of the present application. As shown in fig. 2, the apparatus includes:
an obtaining unit 10, configured to obtain a revised autocorrelation matrix of the LMMSE estimation formula, where the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a toplitz matrix;
A first calculation unit 20, configured to calculate, by using a recursive calculation method, a last column vector of an inverse matrix of the revised autocorrelation matrix, where the inverse matrix of the revised autocorrelation matrix is the Toeplitz matrix, and the last column vector is a vector formed by elements of a last column of the inverse matrix of the revised autocorrelation matrix;
a second calculation unit 30 for calculating an inverse matrix of the revised autocorrelation matrix based on the characteristics of the Toeplitz matrix and the last column vector;
a third calculation unit 40, configured to calculate an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula.
In the LMMSE channel estimation apparatus, the obtaining unit obtains a revised autocorrelation matrix of an LMMSE estimation formula, where the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a toplitz matrix; the first calculation unit calculates a last column vector of an inverse matrix of the revised autocorrelation matrix by adopting a recursive calculation method, wherein the inverse matrix of the revised autocorrelation matrix is the Toeplitz matrix, and the last column vector is a vector formed by elements of the last column of the inverse matrix of the revised autocorrelation matrix; a second calculation unit calculates an inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix and the last column vector; and a third calculation unit calculates an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula. The LMMSE channel estimation device obtains the inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix, and performs corresponding rotation on the final column vector according to the symmetry and iteration relation to obtain all elements of the inverse matrix, so that the calculation complexity is greatly reduced, and the problem of high complexity of the LMMSE channel estimation method in the prior art is solved.
In the method of inverting matrix without using scaling factor, let LMMSE estimate formulaWherein R is HpHp ∈C NXN Is the channel response H at pilot frequency p The elements of which are normalized channel correlation values of subcarrier k1 and subcarrier k2 +.>Can be abbreviated as->Easy to get->() * Represents conjugation, and is therefore a Toeplitz matrix; />To estimate the noise value, I N Is a unit diagonal array. Then T is n Also a Toeplitz matrix,wherein (1)>Its inverse matrix->The form of (c) is as follows,wherein r= { r 1 r 2 …r k } T ，/>Is a transfer matrix () H For conjugate transposition, a final column vector v can be obtained by calculation by adopting a recursion calculation method, and a formula corresponding to the Toeplitz matrix inversion recursion calculation method is as follows:
in order to solve the last column vector v by the above recursive calculation method, firstly, the Yule-Walker equation of the complex domain needs to be solvedAnd iterative calculation method thereof>The equation set of the (k+1) order can be solved in O (k) flops, and the equation set corresponding to the Toeplitz matrix Yule-Walker equation recursion calculation method is obtained as follows:
wherein, the Toeplitz matrix inversion recursive calculation method is corresponding to a formula and the Toeplitz matrix Yule-Walker equation recursive calculation method is corresponding to a formulaThere is a common division operation, which inverts the parameter β. When (when) Is->The SNR is improved and becomes smaller rapidly, and the characteristic 1 approximately equal to |r is combined with the correlation coefficient 1 | 2 ＞|r 2 | 2 ＞…＞|r n-1 | 2 And an initial value α= -r 1 * /τ 0 First recursive reduction factor->Along with->Will greatly affect +.>Stability and difficulty in localization.
To solve the inverse matrixIn one embodiment of the present application, the first calculating unit includes a determining module and a first calculating module, where the determining module is configured to determine a scaling factor of an inverse matrix of the revised autocorrelation matrix according to a signal-to-noise ratio of the channel; the first calculation module is configured to calculate using the scaling factor as a recursive scaling factor of the recursive calculation method, so as to obtain the final column vector. Specifically, determining a scaling factor corresponding to the signal-to-noise ratio of the channel, and calculating by using the scaling factor as a recursion reduction factor of the recursion calculation method to obtain the final column vector v, where the formula is as follows:
as shown in FIG. 2 and FIG. 3, the average value and standard deviation range before and after adjustment by using the scaling factor can be seen that the scaling factor is not used for solving the inverse matrix, and the average value range ratio of each value in the inverse matrix solving process is changed by 10 within the range of 0-65 dB of the SNR 5 After the scaling factors are used, each value in the inverse matrix solving process only needs to be shifted according to the scaling factors, and the average value range can be reduced to 10 2 In the method, the difficulty in pointing is reduced.
In one embodiment of the present application, the apparatus further includes an estimation unit, where the estimation unit includes an estimation module and a second calculation module, where the estimation module is configured to perform RMS estimation and noise estimation on the channel before determining a scaling factor of an inverse matrix of the revised autocorrelation matrix according to a signal-to-noise ratio of the channel, to obtain an effective value of signal power and an estimated value of noise power; the second calculation module is configured to calculate the signal-to-noise ratio according to the effective value of the signal power and the estimated value of the noise power. Specifically, RMS estimation is performed on the channel to obtain an effective value of the signal power, noise estimation is performed to obtain an estimated value of the noise power, and thus a ratio of the effective value of the signal power to the estimated value of the noise power is calculated to obtain the signal-to-noise ratio.
In one embodiment of the present application, the determining module includes a fitting sub-module and a determining sub-module, where the fitting sub-module is configured to fit a historical scaling factor to a historical signal-to-noise ratio to obtain a comparison table of the scaling factor and the signal-to-noise ratio; and the determination submodule is used for looking up the comparison table according to the signal-to-noise ratio of the channel to obtain the scaling factor of the inverse matrix of the revised autocorrelation matrix. Specifically, a scaling factor Γ is established in relation to the SNR value, then there is C is a constant, a comparison table of SNR values and scaling factors Γ is shown in Table 1, scaling factors Γ and 2 may be used n Fitting was performed to obtain the fitting factors of Table 1, and the fitting effect is shown in FIG. 4, and it can be seen that there is realityThe difference between the marginal value and the fitting value is very small, and the fitting effect is good.
In one embodiment of the present application, the characteristics of the Toeplitz matrix include persymmetric characteristics, hermite characteristics, and iteration characteristics, and the calculating the inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix and the last column vector includes: rotating according to the persymmetric characteristic and the last column vector to obtain a first row vector, wherein the first row vector is a vector formed by elements of the first row of the inverse matrix of the revised autocorrelation matrix; determining a partial region element according to the iteration characteristic and the first line vector, wherein the partial region element comprises all elements of which two diagonals of the revised autocorrelation matrix are close to one side of the first line; and determining all elements of the inverse matrix of the revised autocorrelation matrix according to the persymmetric characteristic and the Hermite characteristic to obtain the inverse matrix of the revised autocorrelation matrix. Specifically, according to the persymmetric characteristic and the iterative characteristic of the inverse matrix, the row-by-row/column rotation obtains 1/4 area elements as shown in fig. 5, and the formula is as follows:
According to the persymmetric and Hermite characteristics of the inverse matrix, all elements of the matrix are obtained, and the formula is as followsThereby obtaining an inverse matrix->
It should be noted that, according to the inverse matrixI.e. calculate the LMMSE filter matrix +.>
In an embodiment of the present application, the apparatus further includes a third calculation unit, where the third calculation unit is configured to calculate an operational complexity of an inversion matrix of the revised autocorrelation matrix after calculating an LMMSE filter matrix according to an inversion matrix of the revised autocorrelation matrix and the LMMSE estimation formula. Specifically, the complexity statistics of the above-mentioned steps for solving the inverse matrix are shown in Table 2, and the total number of complex multiplies is about 2*N 2 Is the inversion algorithm with the lowest complexity in the prior art and is a non-approximation algorithm.
The application also provides a signal processing system, comprising: the apparatus comprises one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods described above.
In the signal processing system, firstly, a revised autocorrelation matrix of an LMMSE estimation formula is obtained, wherein the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a Toeplitz matrix; then, calculating by adopting a recursive calculation method to obtain a last column vector of an inverse matrix of the revised autocorrelation matrix, wherein the inverse matrix of the revised autocorrelation matrix is the Toeplitz matrix, and the last column vector is a vector formed by elements of the last column of the inverse matrix of the revised autocorrelation matrix; then, according to the characteristics of the Toeplitz matrix and the last column vector, calculating to obtain an inverse matrix of the revised autocorrelation matrix; and finally, calculating to obtain an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula. According to the system, the inverse matrix of the autocorrelation matrix is revised according to the characteristics of the Toeplitz matrix, and all elements of the inverse matrix can be obtained by correspondingly rotating the last column vector according to the symmetry and iteration relation, so that the calculation complexity is greatly reduced, and the problem of high complexity of an LMMSE channel estimation method in the prior art is solved.
The LMMSE channel estimation device includes a processor and a memory, where the acquiring unit, the first calculating unit, the second calculating unit, the third calculating unit, and the like are stored as program units in the memory, and the processor executes the program units stored in the memory to implement corresponding functions.
The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel can be provided with one or more than one kernel, and the problem of high complexity of the LMMSE channel estimation method in the prior art is solved by adjusting kernel parameters.
The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.
An embodiment of the present invention provides a computer-readable storage medium having stored thereon a program which, when executed by a processor, implements the above-described method.
The embodiment of the invention provides a processor, which is used for running a program, wherein the method is executed when the program runs.
The embodiment of the invention provides equipment, which comprises a processor, a memory and a program stored in the memory and capable of running on the processor, wherein the processor realizes at least the following steps when executing the program:
Step S101, a revised autocorrelation matrix of an LMMSE estimation formula is obtained, wherein the revised autocorrelation matrix is the autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a Toeplitz matrix;
step S102, calculating by a recursive calculation method to obtain a last column vector of an inverse matrix of the revised autocorrelation matrix, wherein the inverse matrix of the revised autocorrelation matrix is the Toeplitz matrix, and the last column vector is a vector formed by elements of the last column of the inverse matrix of the revised autocorrelation matrix;
step S103, calculating an inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix and the last column vector;
and step S104, calculating to obtain an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula.
The device herein may be a server, PC, PAD, cell phone, etc.
The application also provides a computer program product adapted to perform, when executed on a data processing device, a program initialized with at least the following method steps:
step S101, a revised autocorrelation matrix of an LMMSE estimation formula is obtained, wherein the revised autocorrelation matrix is the autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a Toeplitz matrix;
Step S102, calculating by a recursive calculation method to obtain a last column vector of an inverse matrix of the revised autocorrelation matrix, wherein the inverse matrix of the revised autocorrelation matrix is the Toeplitz matrix, and the last column vector is a vector formed by elements of the last column of the inverse matrix of the revised autocorrelation matrix;
step S103, calculating an inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix and the last column vector;
and step S104, calculating to obtain an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula.
In the foregoing embodiments of the present application, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units may be a logic function division, and there may be another division manner when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention 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 described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present invention 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 computer readable storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned methods of the various embodiments of the present invention. And the aforementioned computer-readable storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:
1) In the LMMSE channel estimation method, firstly, a revised autocorrelation matrix of an LMMSE estimation formula is obtained, wherein the revised autocorrelation matrix is the autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a Toeplitz matrix; then, calculating by adopting a recursive calculation method to obtain a last column vector of an inverse matrix of the revised autocorrelation matrix, wherein the inverse matrix of the revised autocorrelation matrix is the Toeplitz matrix, and the last column vector is a vector formed by elements of the last column of the inverse matrix of the revised autocorrelation matrix; then, according to the characteristics of the Toeplitz matrix and the last column vector, calculating to obtain an inverse matrix of the revised autocorrelation matrix; and finally, calculating to obtain an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula. According to the LMMSE channel estimation method, the inverse matrix of the autocorrelation matrix is revised according to the characteristics of the Toeplitz matrix, and all elements of the inverse matrix can be obtained by correspondingly rotating the last column vector according to the symmetry and iteration relation, so that the calculation complexity is greatly reduced, and the problem of high complexity of the LMMSE channel estimation method in the prior art is solved.
2) In the LMMSE channel estimation device of the present application, the obtaining unit obtains a revised autocorrelation matrix of the LMMSE estimation formula, where the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a Toeplitz matrix; the first calculation unit calculates a last column vector of an inverse matrix of the revised autocorrelation matrix by adopting a recursive calculation method, wherein the inverse matrix of the revised autocorrelation matrix is the Toeplitz matrix, and the last column vector is a vector formed by elements of the last column of the inverse matrix of the revised autocorrelation matrix; a second calculation unit calculates an inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix and the last column vector; and a third calculation unit calculates an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula. The LMMSE channel estimation device obtains the inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix, and performs corresponding rotation on the final column vector according to the symmetry and iteration relation to obtain all elements of the inverse matrix, so that the calculation complexity is greatly reduced, and the problem of high complexity of the LMMSE channel estimation method in the prior art is solved.
3) In the signal processing system of the present application, firstly, a revised autocorrelation matrix of an LMMSE estimation formula is obtained, the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a toplitz matrix; then, calculating by adopting a recursive calculation method to obtain a last column vector of an inverse matrix of the revised autocorrelation matrix, wherein the inverse matrix of the revised autocorrelation matrix is the Toeplitz matrix, and the last column vector is a vector formed by elements of the last column of the inverse matrix of the revised autocorrelation matrix; then, according to the characteristics of the Toeplitz matrix and the last column vector, calculating to obtain an inverse matrix of the revised autocorrelation matrix; and finally, calculating to obtain an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula. According to the system, the inverse matrix of the autocorrelation matrix is revised according to the characteristics of the Toeplitz matrix, and all elements of the inverse matrix can be obtained by correspondingly rotating the last column vector according to the symmetry and iteration relation, so that the calculation complexity is greatly reduced, and the problem of high complexity of an LMMSE channel estimation method in the prior art is solved.
The above description is only of the preferred embodiments of the present application and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (9)

1. An LMMSE channel estimation method, comprising:
obtaining a revised autocorrelation matrix of an LMMSE estimation formula, wherein the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a Toeplitz matrix;
calculating by adopting a recursive calculation method to obtain a last column vector of an inverse matrix of the revised autocorrelation matrix, wherein the inverse matrix of the revised autocorrelation matrix is the Toeplitz matrix, and the last column vector is a vector formed by elements of the last column of the inverse matrix of the revised autocorrelation matrix;
calculating an inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix and the final column vector;
calculating to obtain an LMMSE filter matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula, and calculating to obtain the final column vector of the inverse matrix of the revised autocorrelation matrix by adopting a recursive calculation method, wherein the method comprises the following steps: determining a scaling factor of an inverse of the revised autocorrelation matrix based on a signal-to-noise ratio of the channel; and calculating by adopting the scaling factor as a recursion shrinkage factor of the recursion calculation method to obtain the final column vector.
2. The method of claim 1, wherein prior to determining the scaling factor for the inverse of the revised autocorrelation matrix based on the signal-to-noise ratio of the channel, the method further comprises:
performing RMS estimation and noise estimation on the channel to obtain an effective value of signal power and an estimated value of noise power;
and calculating the signal to noise ratio according to the effective value of the signal power and the estimated value of the noise power.
3. The method of claim 1, wherein determining the scaling factor for the inverse of the revised autocorrelation matrix based on the signal-to-noise ratio of the channel comprises:
fitting a historical scaling factor with a historical signal-to-noise ratio to obtain a comparison table of the scaling factor and the signal-to-noise ratio; and looking up the comparison table according to the signal-to-noise ratio of the channel to obtain the scaling factor of the inverse matrix of the revised autocorrelation matrix.
4. A method according to any one of claims 1 to 3, wherein the characteristics of the Toeplitz matrix include persymmetric characteristics, hermite characteristics and iteration characteristics, and wherein calculating the inverse of the modified autocorrelation matrix from the characteristics of the Toeplitz matrix and the final column vector comprises:
Obtaining a first row vector according to the persymmetric characteristic and the last column vector rotation, wherein the first row vector is a vector formed by elements of the first row of the inverse matrix of the revised autocorrelation matrix;
determining a partial region element according to the iteration characteristic and the first row vector, wherein the partial region element comprises all elements of two diagonals of the revised autocorrelation matrix, which are close to one side of the first row;
and determining all elements of the inverse matrix of the revised autocorrelation matrix according to the persymmetric characteristic and the Hermite characteristic to obtain the inverse matrix of the revised autocorrelation matrix.
5. The method of claim 1, wherein after computing an LMMSE filter matrix from the inverse of the revised autocorrelation matrix and the LMMSE estimation formula, the method further comprises:
and calculating the operation complexity of the inversion matrix of the revision autocorrelation matrix.
6. An LMMSE channel estimation apparatus, comprising:
the acquisition unit is used for acquiring a revised autocorrelation matrix of the LMMSE estimation formula, wherein the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to noise influence, and the revised autocorrelation matrix is a Toeplitz matrix;
A first calculation unit, configured to calculate, by using a recursive calculation method, a last column vector of an inverse matrix of the revised autocorrelation matrix, where the inverse matrix of the revised autocorrelation matrix is the toplitz matrix, and the last column vector is a vector formed by elements of a last column of the inverse matrix of the revised autocorrelation matrix;
a second calculation unit, configured to calculate an inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix and the last column vector;
a third calculation unit, configured to calculate an LMMSE filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula, where the first calculation unit includes a determination module and a first calculation module, where the determination module is configured to determine a scaling factor of the inverse matrix of the revised autocorrelation matrix according to a signal-to-noise ratio of the channel; the first calculation module is configured to calculate by using the scaling factor as a recursive scaling factor of the recursive calculation method, so as to obtain the final column vector.
7. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored program, wherein the program performs the method of any one of claims 1 to 5.
8. A processor for running a program, wherein the program when run performs the method of any one of claims 1 to 5.
9. A signal processing system, comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing the method of any of claims 1-5.
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