CN115277315A - LMMSE channel estimation method, device and signal processing system - Google Patents

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

Info

Publication number
CN115277315A
CN115277315A CN202210860343.XA CN202210860343A CN115277315A CN 115277315 A CN115277315 A CN 115277315A CN 202210860343 A CN202210860343 A CN 202210860343A CN 115277315 A CN115277315 A CN 115277315A
Authority
CN
China
Prior art keywords
matrix
revised
autocorrelation matrix
autocorrelation
inverse
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202210860343.XA
Other languages
Chinese (zh)
Other versions
CN115277315B (en
Inventor
冯雪林
孙陆宽
丁雅帅
钱蔓藜
胡金龙
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Sylincom Technology Co ltd
Original Assignee
Beijing Sylincom Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Sylincom Technology Co ltd filed Critical Beijing Sylincom Technology Co ltd
Priority to CN202210860343.XA priority Critical patent/CN115277315B/en
Publication of CN115277315A publication Critical patent/CN115277315A/en
Application granted granted Critical
Publication of CN115277315B publication Critical patent/CN115277315B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Complex Calculations (AREA)

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: acquiring a revised autocorrelation matrix of an LMMSE estimation formula, wherein the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to the influence of noise, and the revised autocorrelation matrix is a Toeplitz matrix; calculating by adopting a recursive calculation method to obtain a final 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 final column vector is a vector formed by elements of the last column of the inverse matrix of the revised autocorrelation matrix; calculating according to the characteristics of the Toeplitz matrix and the last column vector to obtain an inverse matrix of the revised autocorrelation matrix; and calculating to obtain the LMMSE filtering matrix according to the revised inverse matrix of the autocorrelation matrix and the LMMSE estimation formula. The method solves the problem of high complexity of an 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 an LMMSE channel estimation method, apparatus, computer-readable storage medium, processor, and signal processing system.
Background
The OFDM technology can provide a rich frequency diversity gain for a user by using a multipath effect of a wireless channel, can provide a broadband high-speed data transmission capability, and is widely applied to wireless communication systems such as 4G and 5G. 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 comprises a least square method (LS) and a linear minimum mean square error algorithm (LMMSE), wherein the former has low complexity, but the influence of noise is not considered, and the requirements of data transmission on reliability and high speed in an actual system cannot be met; the LMMSE filtering matrix W (shown as formula 1) is calculated by acquiring the autocorrelation matrix and the cross-correlation matrix of the channel based on the minimum estimation error criterion, and the channel value estimated at the pilot frequency is filtered by using the W.
Figure BDA0003758113610000011
Figure BDA0003758113610000012
Wherein R isHpHp∈CNXNIs the autocorrelation matrix of the channel response at the pilot,
Figure BDA0003758113610000013
to estimate the noise value.
The existing methods for solving the correlation coefficient of the LMMSE comprise the following steps: storing a corresponding table according to the channel root mean square value, the pilot frequency setting and the noise estimation result locally, and selecting a filter coefficient according to a real-time calculation result table during receiving; reversely deducing a tap coefficient of a corresponding time domain channel based on a channel estimation result at a pilot frequency position, constructing a correlation matrix with a cyclic characteristic based on the tap coefficient, and solving a correlation coefficient by utilizing FFT/IFFT and carrying out filtering operation; calculating an approximate value of inversion of the correlation matrix by using a p-order polynomial, decomposing the correlation matrix into a plurality of Toeplitz matrixes, wherein each matrix corresponds to a channel tap coefficient, and iteratively calculating the approximate value; and carrying out SVD (singular value decomposition) with the dimensionality being the pilot frequency length on the channel correlation matrix, and calculating a final filter coefficient at one time by using the cyclic characteristic of the matrix. However, the methods have certain limitations, and the scene in 5G is more complex, so that a large amount of storage space and control judgment are needed for the table storage algorithm; the filtering coefficient length by using the algorithm of FFT/IFFT is the pilot frequency length, so 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; the SVD decomposition is utilized to calculate the correlation coefficient, wherein the complexity of the SVD decomposition is higher, and the value at the non-frequency-guide position still needs to be interpolated, so that certain performance loss exists.
The above information disclosed in this background section is only for enhancement of understanding of the background of the technology described herein and, therefore, certain information may be included in the background that does not form the prior art that is already known in this country to a person of ordinary skill in the art.
Disclosure of Invention
The present 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 invention, there is provided an LMMSE channel estimation method, including: acquiring 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 a final 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 final column vector is a vector formed by elements of the final 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 last column vector; and calculating to obtain an LMMSE filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula.
Optionally, calculating a last column vector of an inverse matrix of the revised autocorrelation matrix by using a recursive calculation method, including: determining a scaling factor for an inverse of the revised autocorrelation matrix based on the signal-to-noise ratio of the channel; and calculating by adopting the scaling factor as a recursive reduction factor of the recursive calculation method to obtain the last column vector.
Optionally, before determining the scaling factor of the inverse of the revised autocorrelation matrix according to 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 to obtain the signal-to-noise ratio according to the effective value of the signal power and the estimated value of the noise power.
Optionally, determining a scaling factor of an inverse matrix of the revised autocorrelation matrix according to the signal-to-noise ratio of the channel includes: 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 checking the comparison table according to the signal to noise ratio of the channel to obtain a scaling factor of an inverse matrix of the revised autocorrelation matrix.
Optionally, the characteristics of the Toeplitz matrix include persymmetric characteristics, hermite characteristics, and iterative characteristics, and the inverse of the revised autocorrelation matrix is calculated according to the characteristics of the Toeplitz matrix and the last column vector, including: obtaining a head line vector according to the persymmetric characteristic and the last column vector rotation, wherein the head line vector is a vector formed by elements of a first line of an inverse matrix of the revised autocorrelation matrix; determining partial region elements according to the iterative characteristic and the head row vector, wherein the partial region elements comprise all elements of one side of two diagonals of the revised autocorrelation matrix, which is close to 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 filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula, the method further comprises: calculating an operational complexity of an inverse matrix of the revised autocorrelation matrix.
According to another aspect of the embodiments of the present invention, there is also provided an LMMSE channel estimation apparatus, including: an obtaining unit, 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 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 Toeplitz matrix, and the last column vector is a vector formed by elements in 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 filtering 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 executes any one of the methods.
According to another aspect of the embodiments of the present invention, there is also provided a processor, configured to execute a program, where the program executes any one of the methods.
According to another aspect of the embodiments of the present invention, there is also provided a signal processing system including: one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods described herein.
In the embodiment of the invention, in the LMMSE channel estimation method, 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 the 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, calculating according to the characteristics of the Toeplitz matrix and the last column vector to obtain an inverse matrix of the revised autocorrelation matrix; and finally, calculating to obtain an LMMSE filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula. The LMMSE channel estimation method solves the inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix, and correspondingly rotates the last column vector according to the symmetry and iteration relation to obtain all elements of the inverse matrix, thereby greatly reducing the calculation complexity and solving the problem of high complexity of the LMMSE channel estimation method in the prior art.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
fig. 1 shows a flow diagram of an LMMSE channel estimation method according to an embodiment of the application;
FIG. 2 is a diagram illustrating the value ranges of steps of an inverse matrix without scaling factors according to an embodiment of the present application;
FIG. 3 is a diagram illustrating the value ranges of steps of an inverse matrix with scaling factors according to an embodiment of the present application;
FIG. 4 shows a comparison of adjustment factor fit values and actual values of the stabilization matrix inversion results according to an embodiment of the application;
FIG. 5 shows an inverse matrix quarter-area schematic according to an embodiment of the present application;
figure 6 shows a schematic diagram of an LMMSE channel estimation apparatus according to an embodiment of the present application.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. 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 forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
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. Also, in the specification and 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, some terms or expressions referred to in the embodiments of the present application are explained below:
toeplitz matrix: each matrix with the same elements on each oblique line from top left to bottom right in the matrix;
persymmetric characteristics: the characteristic that the matrix is symmetrical about the northeast-southwest diagonal;
hermite properties: the characteristic that the matrix is symmetrical about the northwest-southeast diagonal;
iterative characteristics: each row in the matrix is characterized by the right shift of the elements of the previous row by one bit.
As mentioned in the background, the LMMSE channel estimation method in the prior art is highly complex, and in order to solve the above problems, in an exemplary embodiment of the present application, an LMMSE channel estimation method, an apparatus, a computer-readable storage medium, a processor, and a signal processing system are provided.
According to an embodiment of the present application, an LMMSE channel estimation method is provided.
Fig. 1 is a flow chart of an LMMSE channel estimation method according to an embodiment of the application. As shown in fig. 1, the method comprises the steps of:
step S101, acquiring 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;
step S102, calculating a last column vector of the 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 in 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 filtering 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, wherein the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to the influence of noise, and the revised autocorrelation matrix is a Toeplitz matrix; then, calculating by adopting a recursive calculation method to obtain a last column vector of the 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, calculating according to the characteristics of the Toeplitz matrix and the last column vector to obtain an inverse matrix of the revised autocorrelation matrix; and finally, calculating to obtain an LMMSE filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula. The LMMSE channel estimation method solves the inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix, and correspondingly rotates the last column vector according to the symmetry and iteration relation to obtain all elements of the inverse matrix, thereby greatly reducing the calculation complexity and solving the problem of high complexity of the LMMSE channel estimation method in the prior art.
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 different than here.
In the method of inverse matrix without scaling factor, let LMMSE estimate formula
Figure BDA0003758113610000051
Wherein R isHpHp∈CNXNIs the channel response H at the pilotpThe elements of the autocorrelation matrix are normalized channel correlation values of the subcarriers k1 and k2
Figure BDA0003758113610000052
Can be abbreviated as
Figure BDA0003758113610000053
Is easily obtained
Figure BDA0003758113610000054
()*Represents a conjugate and is therefore a Toeplitz matrix;
Figure BDA0003758113610000055
to estimate the noise value, INIs a unit diagonal matrix. Then TnAlso a matrix of Toeplitz,
Figure BDA0003758113610000056
wherein,
Figure BDA0003758113610000057
TNinverse matrix of
Figure BDA0003758113610000058
In the form of (a) a (b) b,
Figure BDA0003758113610000059
wherein r = { r =1r2…rk}T，
Figure BDA00037581136100000510
Is a rotation matrix, ()HFor conjugate transposition, a recursive calculation method is adopted to calculate the last column vector v, and a formula corresponding to the Toeplitz matrix inversion recursive calculation method is as follows:
Figure BDA00037581136100000511
to solve the last column vector v by the above recursive computation method, first, the Yule-Walker equation of the complex field needs to be solved
Figure BDA0003758113610000061
And iterative calculation method thereof
Figure BDA0003758113610000062
An equation set with (k + 1) order can be solved in O (k) flops to obtain a formula corresponding to the recursive calculation method of the Toeplitz matrix Yule-Walker equation, wherein the formula is as follows:
Figure BDA0003758113610000063
the formula corresponding to the Toeplitz matrix inversion recursive calculation method and the formula corresponding to the Toeplitz matrix Yule-Walker equation recursive calculation method have common division operation, and the reciprocal of the parameter beta is calculated. When in use
Figure BDA0003758113610000064
In
Figure BDA0003758113610000065
The signal-to-noise ratio SNR is improved and rapidly becomes smaller, and the characteristic of the correlation coefficient is 1 to be approximately equal to r1|2＞|r2|2＞…＞|rn-1|2And an initial value α = -r1 */τ0First recursive reduction factor
Figure BDA0003758113610000066
With following
Figure BDA0003758113610000067
Will greatly influence
Figure BDA0003758113610000068
Stability and difficulty of spotting.
To solve the inverse matrix
Figure BDA0003758113610000069
In an embodiment of the present application, the calculating, by using a recursive calculation method, a last column vector of an inverse matrix of the revised autocorrelation matrix includes: determining a scaling factor of an inverse matrix of the revised autocorrelation matrix based on the signal-to-noise ratio of the channel; and calculating by adopting the scaling factor as a recursive reduction factor of the recursive calculation method to obtain the last column vector. Specifically, a scaling factor corresponding to the signal-to-noise ratio of the channel is determined, and the scaling factor is used as a recursive reduction factor of the recursive calculation method to perform calculation, so as to obtain the last column vector v, where the formula is as follows:
Figure BDA00037581136100000610
the ranges of the mean value and the standard deviation before and after the adjustment by the scaling factor are shown in fig. 2 and fig. 3, and it can be seen that the inverse matrix is not used, and the inverse matrix is within the range of 0-65 dB of the SNR valueThe proportion change of the mean value range of each value in the solving process is 105In the above, after the scaling factor is used, all values in the inverse matrix solving process only need to be shifted according to the scaling factor, and the average value range can be reduced to 102And the difficulty of fixed point 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 an embodiment of the present application, before determining the scaling factor of the inverse of the revised autocorrelation matrix according to the signal-to-noise ratio of the channel, the method further includes: 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. 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 an embodiment of the present application, determining a scaling factor of an inverse matrix of the revised autocorrelation matrix according to the signal-to-noise ratio of the channel includes: 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.In particular, a scaling factor Γ is established in relation to the SNR value, then
Figure BDA0003758113610000071
C is constant, the SNR value and the scale factor F are compared as shown in Table 1, and the scale factor F can be compared with 2nFitting is performed to obtain fitting factors of table 1, and fitting effects are shown in fig. 4, so that the difference between an actual value and a fitting value is extremely small, and the fitting effect is good.
In an embodiment of the application, the characteristic of the Toeplitz matrix includes a persymmetric characteristic, a Hermite characteristic, and an iteration characteristic, and the inverse matrix of the revised autocorrelation matrix is obtained by calculation according to the characteristic of the Toeplitz matrix and the last column vector, including: obtaining a head line vector by rotating according to the persymmetric characteristic and the last column vector, wherein the head line vector is a vector formed by elements of a first line of an inverse matrix of the revised autocorrelation matrix; determining partial region elements according to the iterative characteristic and the head row vector, wherein the partial region elements comprise all elements of two diagonals of the revised autocorrelation matrix, which are close to one side of the first row; 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 persymetric property and the iterative property of the inverse matrix, the 1/4 region elements obtained by row/column rotation are shown in fig. 5, and the formula is as follows:
Figure BDA0003758113610000072
Figure BDA0003758113610000081
according to the persymmetric and Hermite characteristics of the inverse matrix, all elements of the matrix are obtained, and the formula is as follows
Figure BDA0003758113610000082
Thereby obtaining an inverse matrix
Figure BDA0003758113610000083
It should be noted that the inverse matrix is used
Figure BDA0003758113610000084
The LMMSE filtering matrix w can be calculated
Figure BDA0003758113610000086
In an embodiment of the present application, after the LMMSE filtering matrix is calculated according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula, the method further includes: the computational complexity of calculating the inverse of the revised autocorrelation matrix. Specifically, the step complexity statistics of solving the inverse matrix are shown in table 2, and the total number of complex multiplications is about 2 × n2The method is an inversion algorithm with the lowest complexity in the prior art and is a non-approximate algorithm.
TABLE 2
Figure BDA0003758113610000087
The embodiment of the present application further provides an LMMSE channel estimation device, and it should be noted that the LMMSE channel estimation device according to the embodiment of the present application may be used to execute the LMMSE channel estimation method provided by the embodiment of the present application. The LMMSE channel estimation apparatus provided in 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 Toeplitz matrix;
a first calculating 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 in a last column of the inverse matrix of the revised autocorrelation matrix;
a second calculating unit 30, 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 calculating unit 40 is used for calculating an LMMSE filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula.
In the LMMSE channel estimation device, an acquisition unit acquires 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; the first calculation unit calculates a last column vector of an inverse matrix of the revised autocorrelation matrix by using 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; the 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 the third calculating unit calculates to obtain an LMMSE filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula. The LMMSE channel estimation device solves the inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix, and correspondingly rotates the last 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 inversion matrix without using scaling factor, let LMMSE estimate formula
Figure BDA0003758113610000091
Wherein R isHpHp∈CNXNIs the channel response H at the pilotpThe elements of the autocorrelation matrix are normalized channel correlation values of the subcarriers k1 and k2
Figure BDA0003758113610000092
Can be abbreviated as
Figure BDA0003758113610000093
Is then readily available
Figure BDA0003758113610000094
()*Represents a conjugate and is therefore a Toeplitz matrix;
Figure BDA0003758113610000095
to estimate the noise value, INIs a unit diagonal matrix. Then TnAlso a matrix of Toeplitz,
Figure BDA0003758113610000096
wherein,
Figure BDA0003758113610000097
inverse matrix thereof
Figure BDA0003758113610000098
The form of the (A) is as follows,
Figure BDA0003758113610000099
wherein, r = { r =1 r2…rk}T，
Figure BDA00037581136100000910
Is a rotation matrix, ()HFor conjugate transposition, a recursive calculation method is adopted to calculate the last column vector v, and a formula corresponding to the Toeplitz matrix inversion recursive calculation method is as follows:
Figure BDA00037581136100000911
to solve the last column vector v by the above recursive computation method, first, the Yule-Walker equation of the complex field needs to be solved
Figure BDA0003758113610000101
And its iterative calculation method
Figure BDA0003758113610000102
An equation set with (k + 1) order can be solved in O (k) flops to obtain a formula corresponding to the recursive calculation method of the Toeplitz matrix Yule-Walker equation, wherein the formula is as follows:
Figure BDA0003758113610000103
the formula corresponding to the Toeplitz matrix inversion recursive calculation method and the formula corresponding to the Toeplitz matrix Yule-Walker equation recursive calculation method have common division operation, and the parameter beta is subjected to reciprocal calculation. When in use
Figure BDA0003758113610000104
In (1)
Figure BDA0003758113610000105
The SNR is improved along with the signal-to-noise ratio and is rapidly reduced, and the characteristic of a correlation coefficient is 1 ≈ r1|2＞|r2|2＞…＞|rn-1|2And an initial value α = -r1 */τ0First recursive reduction factor
Figure BDA0003758113610000106
With following
Figure BDA0003758113610000107
Will greatly influence
Figure BDA0003758113610000108
Stability and difficulty of spotting.
To solve the inverse matrix
Figure BDA0003758113610000109
The first computing unit includes a determining module and a first computing unitA calculation module, wherein the determination 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 calculating module is configured to calculate by using the scaling factor as a recursive reduction factor of the recursive calculating method, so as to obtain the last column vector. Specifically, a scaling factor corresponding to the signal-to-noise ratio of the channel is determined, and the scaling factor is used as a recursive reduction factor of the recursive calculation method to perform calculation, so as to obtain the last column vector v, where a formula is as follows:
Figure BDA00037581136100001010
the ranges of the mean value and the standard deviation before and after the adjustment by using the scaling factor are shown in fig. 2 and fig. 3, it can be seen that the inverse matrix of the scaling factor is not used, and the ratio change of the mean value range of each value in the process of solving the inverse matrix is 10 within the range of 0-65 dB when the SNR takes the value5In the above, after the scaling factor is used, each value in the inverse matrix solving process only needs to be shifted according to the scaling factor, and the average value range can be reduced to 102And the difficulty of fixed point is reduced.
In an embodiment of the present application, the apparatus further includes an estimating unit, where the estimating unit includes an estimating module and a second calculating module, where the estimating 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, so as to obtain an effective value of signal power and an estimated value of noise power; the second calculating module is used for 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 an embodiment of the application, the determining module includes a fitting submodule and a determining submodule, wherein the determining submodule includesThe fitting submodule is used for fitting a historical scaling factor and a historical signal-to-noise ratio to obtain a comparison table of the scaling factor and the signal-to-noise ratio; the determining 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. In particular, a scaling factor Γ is established in relation to the SNR value, then
Figure BDA0003758113610000111
C is constant, the table of SNR values and the scale factor F is shown in Table 1, and the scale factor F can be compared with 2nFitting is performed to obtain fitting factors of table 1, and fitting effects are shown in fig. 4, so that the difference between an actual value and a fitting value is extremely small, and the fitting effect is good.
In an embodiment of the application, the characteristic of the Toeplitz matrix includes a persymmetric characteristic, a Hermite characteristic, and an iteration characteristic, and the inverse matrix of the revised autocorrelation matrix is obtained by calculation according to the characteristic of the Toeplitz matrix and the last column vector, including: obtaining a head line vector by rotating according to the persymmetric characteristic and the last column vector, wherein the head line vector is a vector formed by elements of a first line of an inverse matrix of the revised autocorrelation matrix; determining partial region elements according to the iterative characteristic and the head row vector, wherein the partial region elements comprise all elements of two diagonals of the revised autocorrelation matrix, which are close to one side of the first row; 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 persymetric property and the iterative property of the inverse matrix, the 1/4 region elements obtained by row/column rotation are shown in fig. 5, and the formula is as follows:
Figure BDA0003758113610000112
according to the persymmetric and Hermite characteristics of the inverse matrix, all elements of the matrix are obtained, and the formula is as follows
Figure BDA0003758113610000121
Thereby obtaining an inverse matrix
Figure BDA0003758113610000122
It should be noted that the inverse matrix is used
Figure BDA0003758113610000123
The LMMSE filtering matrix can be calculated
Figure BDA0003758113610000124
Figure BDA0003758113610000125
In an embodiment of the present application, the apparatus further includes a third calculating unit, where the third calculating unit is configured to calculate an operation complexity of an inverse matrix of the revised autocorrelation matrix after calculating an LMMSE filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula. Specifically, the step complexity statistics of solving the inverse matrix are shown in table 2, and the total number of complex multiplications is about 2 × n2The method is an inversion algorithm with the lowest complexity in the prior art and is a non-approximation algorithm.
The present application also provides 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 above-described methods.
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 the influence of noise, and the revised autocorrelation matrix is a Toeplitz matrix; then, calculating by adopting a recursive calculation method to obtain a last column vector of the 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, calculating according to the characteristics of the Toeplitz matrix and the last column vector to obtain an inverse matrix of the revised autocorrelation matrix; and finally, calculating to obtain an LMMSE filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula. The system solves the inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix, and correspondingly rotates the last column vector according to the symmetry and iteration relation to obtain all elements of the inverse matrix, thereby greatly reducing the calculation complexity and solving the problem of high complexity of the LMMSE channel estimation method in the prior art.
The LMMSE channel estimation device comprises a processor and a memory, wherein the acquisition unit, the first calculation unit, the second calculation unit, the third calculation unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the problem of high complexity of an LMMSE channel estimation method in the prior art is solved by adjusting kernel parameters.
The memory may include volatile memory in a computer readable medium, random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
An embodiment of the present invention provides a computer-readable storage medium on which a program is stored, 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 which is stored on the memory and can run on the processor, wherein when the processor executes the program, at least the following steps are realized:
step S101, acquiring 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;
step S102, calculating a final column vector of the 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 final column vector is a vector formed by elements in 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 filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula.
The device herein may be a server, a PC, a PAD, a mobile phone, etc.
The present application further provides a computer program product adapted to perform a program initialized with at least the following method steps when executed on a data processing device:
step S101, acquiring a revised autocorrelation matrix of an LMMSE estimation formula, wherein the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to the influence of noise, and the revised autocorrelation matrix is a Toeplitz matrix;
step S102, calculating a final column vector of the 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 final column vector is a vector formed by elements in 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 filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula.
In the above embodiments of the present invention, the description of each embodiment has its own emphasis, and reference may be made to the related description of other embodiments for parts that are not described in detail in a certain embodiment.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the above-described units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed coupling or direct coupling or communication connection between each other may be an indirect coupling or communication connection through some interfaces, units or modules, and may be electrical or in other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, the technical solution of the present invention, which is substantially or partly contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a computer readable storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage media comprise: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
From the above description, it can be seen that the above-described embodiments of the present application achieve the following technical effects:
1) Firstly, acquiring a revised autocorrelation matrix of an LMMSE estimation formula, wherein the revised autocorrelation matrix is an autocorrelation matrix of a channel revised according to the influence of noise, and the revised autocorrelation matrix is a Toeplitz matrix; then, calculating by adopting a recursive calculation method to obtain a last column vector of the 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 filtering 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 revised autocorrelation matrix is obtained according to the characteristics of the Toeplitz matrix, all elements of the inverse matrix can be obtained by correspondingly rotating the last column vector according to the symmetry and iteration relation, 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, an acquisition unit acquires 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; the first calculation unit calculates a last column vector of an inverse matrix of the revised autocorrelation matrix by using 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; the 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 the third calculating unit calculates to obtain an LMMSE filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula. The LMMSE channel estimation device solves the inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix, and correspondingly rotates the last 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 an LMMSE channel estimation method in the prior art is solved.
3) 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 the influence of noise, and the revised autocorrelation matrix is a Toeplitz matrix; then, calculating by adopting a recursive calculation method to obtain a last column vector of the 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, calculating according to the characteristics of the Toeplitz matrix and the last column vector to obtain an inverse matrix of the revised autocorrelation matrix; and finally, calculating to obtain an LMMSE filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula. The system solves the inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix, and correspondingly rotates the last column vector according to the symmetry and iteration relation to obtain all elements of the inverse matrix, thereby greatly reducing the calculation complexity and solving the problem of high complexity of the LMMSE channel estimation method in the prior art.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (10)

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 a final 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 final column vector is a vector formed by elements of the final column of the inverse matrix of the revised autocorrelation matrix;
calculating to obtain an inverse matrix of the revised autocorrelation matrix according to the characteristics of the Toeplitz matrix and the last column vector;
and calculating to obtain an LMMSE filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula.
2. The method of claim 1, wherein calculating a last column vector of an inverse of the revised autocorrelation matrix using a recursive computation method comprises:
determining a scaling factor of an inverse matrix of the revised autocorrelation matrix according to the signal-to-noise ratio of the channel;
and calculating by adopting the scaling factor as a recursive reduction factor of the recursive calculation method to obtain the last column vector.
3. The method of claim 2, wherein prior to determining the scaling factor for the inverse of the revised autocorrelation matrix based on the signal-to-noise ratio for the channel, the method further comprises:
RMS estimation and noise estimation are carried out on the channel to obtain an effective value of signal power and an estimated value of noise power; and calculating to obtain the signal-to-noise ratio according to the effective value of the signal power and the estimated value of the noise power.
4. The method of claim 2, 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 a scaling factor of an inverse matrix of the revised autocorrelation matrix.
5. The method of any one of claims 1 to 4, wherein the characteristics of the Toeplitz matrix comprise a persymmetric characteristic, a Hermite characteristic and an iterative characteristic, and wherein the inverse of the revised autocorrelation matrix is calculated from the characteristics of the Toeplitz matrix and the last column vector, comprising:
rotating according to the persymmetric characteristic and the last column vector to obtain a head row vector, wherein the head row vector is a vector formed by elements of a first row of an inverse matrix of the revised autocorrelation matrix;
determining partial region elements according to the iteration characteristics and the head row vector, wherein the partial region elements comprise 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.
6. The method of claim 1, wherein after calculating an LMMSE filtering matrix from the inverse of the revised autocorrelation matrix and the LMMSE estimation formula, the method further comprises:
calculating an operational complexity of an inverse matrix of the revised autocorrelation matrix.
7. An LMMSE channel estimation apparatus, comprising:
an obtaining unit, configured to obtain 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 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 Toeplitz matrix, and the last column vector is a vector formed by elements in 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 filtering matrix according to the inverse matrix of the revised autocorrelation matrix and the LMMSE estimation formula.
8. 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 6.
9. A processor, characterized in that the processor is configured to run a program, wherein the program when running performs the method of any of claims 1 to 6.
10. A signal processing system, comprising: one or more processors, memory, and one or more programs stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for performing the method of any of claims 1-6.
CN202210860343.XA 2022-07-21 2022-07-21 LMMSE channel estimation method, device and signal processing system Active CN115277315B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210860343.XA CN115277315B (en) 2022-07-21 2022-07-21 LMMSE channel estimation method, device and signal processing system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210860343.XA CN115277315B (en) 2022-07-21 2022-07-21 LMMSE channel estimation method, device and signal processing system

Publications (2)

Publication Number Publication Date
CN115277315A true CN115277315A (en) 2022-11-01
CN115277315B CN115277315B (en) 2023-08-25

Family

ID=83767121

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210860343.XA Active CN115277315B (en) 2022-07-21 2022-07-21 LMMSE channel estimation method, device and signal processing system

Country Status (1)

Country Link
CN (1) CN115277315B (en)

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108737299A (en) * 2018-05-11 2018-11-02 重庆邮电大学 A kind of LMMSE channel estimation methods of low complex degree
CN110708264A (en) * 2019-08-01 2020-01-17 南京邮电大学 Recursive least square directional tracking method based on complex exponential basis model channel
CN111245752A (en) * 2020-01-13 2020-06-05 重庆邮电大学 Low-complexity 5G NR channel estimation method based on compressed sensing
CN111935746A (en) * 2020-08-14 2020-11-13 Oppo广东移动通信有限公司 Method, device, terminal and storage medium for acquiring communication parameters
CN112953463A (en) * 2021-03-05 2021-06-11 苏州大学 Constrained recursive maximum correlation entropy adaptive filter with forgetting factor
CN113541650A (en) * 2021-06-23 2021-10-22 苏州大学 Sparse linear constraint recursive maximum correlation entropy adaptive filter

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108737299A (en) * 2018-05-11 2018-11-02 重庆邮电大学 A kind of LMMSE channel estimation methods of low complex degree
CN110708264A (en) * 2019-08-01 2020-01-17 南京邮电大学 Recursive least square directional tracking method based on complex exponential basis model channel
CN111245752A (en) * 2020-01-13 2020-06-05 重庆邮电大学 Low-complexity 5G NR channel estimation method based on compressed sensing
CN111935746A (en) * 2020-08-14 2020-11-13 Oppo广东移动通信有限公司 Method, device, terminal and storage medium for acquiring communication parameters
CN112953463A (en) * 2021-03-05 2021-06-11 苏州大学 Constrained recursive maximum correlation entropy adaptive filter with forgetting factor
CN113541650A (en) * 2021-06-23 2021-10-22 苏州大学 Sparse linear constraint recursive maximum correlation entropy adaptive filter

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
CHAO-KAI WEN; JUNG-CHIEH CHEN; PANGAN TING: ""A Shrinkage Linear Minimum Mean Square Error Estimator"" *
孙兴国: ""基于压缩感知的无线信道估计方法研究"", pages 136 - 116 *
陶佳，龙恳，冯雪林，林江南: ""TD-LTE下行信道估计的ASIP实现"" *

Also Published As

Publication number Publication date
CN115277315B (en) 2023-08-25

Similar Documents

Publication Publication Date Title
US10419137B2 (en) Estimating the frequency response of multipath channels
US20080049814A1 (en) Apparatus and method for estimating a channel in broadband wireless access system
EP2363985B1 (en) A method for estimating basis expansion model coefficients of an OFDM transmission channel
CN114726688A (en) Channel estimation method, system, equipment and readable storage medium
US20220182265A1 (en) Linear Equalization Method and Device for OTFS Systems
CN108874743B (en) Windowing method for controlling main and side lobe characteristics of frequency spectrum
CN111404853B (en) Carrier frequency offset estimation method, device and computer storage medium
Bhandari et al. Novel spectral efficient technique for MIMO-OFDM channel estimation with reference to PAPR and BER analysis
WO2020135875A1 (en) Wiener adaptation-based channel estimation method and system
CN108566347B (en) Pilot frequency design method for double-selection sparse channel of multi-user OFDM system
CN103782557B (en) Noise-reduction method in channel estimating and device thereof
CN115277315A (en) LMMSE channel estimation method, device and signal processing system
Yamashita* et al. A nonlinear complementarity approach to multiuser power control for digital subscriber lines
Bakulin et al. A New Approach to Problems of MIMO Detection and Multiuser Demodulation
Sure et al. Large random matrix‐based channel estimation for massive MIMO‐OFDM uplink
CN113055318B (en) Channel estimation method
CN113472703B (en) OFDM channel estimation method
US8614939B2 (en) Method and apparatus for crosstalk channel estimation
Hou et al. Low-complexity enhanced DFT-based channel estimation for OFDM systems with virtual subcarriers
CN115442188A (en) Channel estimation method, device, equipment and storage medium
CN108881095B (en) OFDM data processing method and device
CN112995078B (en) Frequency offset compensation algorithm of OFDMA uplink
Jan Fast time-varying multipath channel estimation and signal detection for of OFDM systems with interference compensation
CN114501672A (en) PRACH signal processing method, PRACH signal processing device, electronic equipment and storage medium
CN109818885B (en) Channel estimation method, device, equipment and computer readable storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant