CN113055318B - Channel estimation method - Google Patents

Channel estimation method Download PDF

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CN113055318B
CN113055318B CN202110339843.4A CN202110339843A CN113055318B CN 113055318 B CN113055318 B CN 113055318B CN 202110339843 A CN202110339843 A CN 202110339843A CN 113055318 B CN113055318 B CN 113055318B
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channel estimation
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wiener filter
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CN113055318A (en
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戴曼
石晶林
赵赫
刘林
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Institute of Computing Technology of CAS
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04L25/0202—Channel estimation
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    • H04L25/0242—Channel estimation channel estimation algorithms using matrix methods

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Abstract

The invention provides a channel estimation method, which comprises the following steps: step 100: performing least square channel estimation according to the received pilot signal and the local pilot sequence; step 200: obtaining the channel power according to the least square channel estimation resultA rate-delay profile; step 300: calculating the first row of the correlation matrix of all the subcarrier channels according to the power time delay spectrums of the channels; step 400: calculating the front F of the wiener filter matrix according to the first row of the correlation matrix of all the subcarrier channelssLine by line, resulting in a wiener filter matrix W, where FsIs a pilot interval; step 500: a channel estimate output is calculated based on the least squares channel estimate and the wiener filter matrix W. Based on the embodiment of the invention, the complexity of calculation and storage of the channel estimation scheme in the wireless communication system can be reduced, and meanwhile, the performance of the channel estimation algorithm is not influenced.

Description

Channel estimation method
Technical Field
The present invention relates to the field of mobile communications, and in particular, to a channel estimation method.
Background
With the rapid development of the fifth generation mobile communication technology (5th-generation, abbreviated as 5G), higher requirements are put on various algorithms of the receiving end. At the receiving end, the quality of the channel estimation scheme directly determines the receiving performance of the whole system, and the computational complexity of the channel estimation scheme has a great influence on the design and implementation of the receiving end equipment. A channel estimation scheme with low complexity and high accuracy is urgently needed by the receiving end device.
At present, the channel estimation method which is commonly used is mainly a wiener filtering scheme, and the implementation scheme is mainly divided into two schemes. One is to perform wiener filtering on the channel estimation values of the pilot subcarriers to obtain more accurate channel estimation values at the positions of the pilot subcarriers, and then perform interpolation to obtain channel estimation values on all subcarriers. Another method is to perform wiener filtering on the channel estimation values of the pilot subcarriers to directly obtain the channel estimation values on all subcarriers. The latter scheme is more complex but better performing than the first scheme. In consideration of implementation complexity, in practical implementation, simplified and compressed wiener filter matrixes are adopted, the filter order is reduced from the number of all subcarriers to one bit, and the calculation complexity can be greatly reduced by reducing the receiving of the filter, but some performance loss can be brought.
In addition, at present, a commonly used channel estimation scheme, a least square channel estimation plus interpolation scheme, is low in complexity, but poor in performance and is easily affected by noise.
For 5G systems, it is necessary to provide a low-complexity and high-accuracy channel estimation scheme to meet the requirements of low latency and high reliability of communication of the system.
Disclosure of Invention
The present invention is directed to the above problem, and according to a first aspect of the present invention, a channel estimation method is provided, including:
step 100: performing least square channel estimation according to the received pilot signal and the local pilot sequence;
step 200: obtaining a power time delay spectrum of a channel according to the least square channel estimation result;
step 300: calculating the first row of the correlation matrix of all the subcarrier channels according to the power time delay spectrums of the channels;
step 400: calculating the front F of the wiener filter matrix according to the first row of the correlation matrix of all the subcarrier channelssLine by line, resulting in a wiener filter matrix W, where FsIs a pilot interval;
step 500: a channel estimate output is calculated based on the least squares channel estimate and the wiener filter matrix W.
In an embodiment of the present invention, step 200 includes obtaining all subcarrier channel estimation results by interpolation based on the least square channel estimation result, and then calculating the power delay profile.
In one embodiment of the present invention, wherein step 200 comprises calculating the root mean square delay τ of the channel based on least squares channel estimatesrmsAnd obeying τ according to the channel power delay profilermsDetermining the power delay spectrum of the channel according to the negative exponential distribution of the channel.
In one embodiment of the present invention, step 400 comprises:
step 410: is obtained according to the following formula
Figure BDA0002999140150000021
First row of
T(1)＝IDFT(PP)
Wherein PP is a vector consisting of
Figure BDA0002999140150000022
P (k) is the power of the pilot frequency sub-carrier position, the IDFT adopts FFT calculation, and right shift one bit row by row for T (1) is calculated to obtain T;
step 420: according to the first row R of the correlation matrix of all sub-carrier channelsHH(1) Computing
Figure BDA0002999140150000023
Front F of the matrixsA row;
step 430: according to T and
Figure BDA0002999140150000024
front F of the matrixsFront F of row calculation wiener filter matrix WsAnd (6) rows.
In one embodiment of the present invention, step 500 comprises:
channel estimation result according to least square
Figure BDA0002999140150000025
And the wiener filter matrix W to obtain a channel estimation output of
Figure BDA0002999140150000026
Wherein the front F of the wiener filter matrix is divided intosLine, per FsThe rows are cyclically shifted 1 time to obtain a wiener filter matrix W.
In one embodiment of the present invention, wherein step 420 comprises:
by extracting RHH(1) Wherein all k columns are obtained
Figure BDA0002999140150000031
First row of
Figure BDA0002999140150000032
For front FsOther ones of the rows by decimating RHH(i) Wherein all k columns are obtained
Figure BDA0002999140150000033
Row i of (1), wherein RHH(i) Is formed by RHH(i-1) is obtained by cyclic shift once, i is 2, 3sAnd k is a pilot subcarrier position index.
In one embodiment of the present invention, wherein step 430 comprises calculating the front F of W from the following equationsLine of
Figure BDA0002999140150000034
In one embodiment of the invention, the multiplication of the vector and the matrix is performed by an FFT.
According to a second aspect of the present invention, there is provided a computer readable storage medium in which one or more computer programs are stored which, when executed, are for implementing the channel estimation method of the present invention.
According to a third aspect of the invention there is provided a computing system comprising: a storage device, and one or more processors; wherein the storage means is adapted to store one or more computer programs which, when executed by the processor, are adapted to carry out the channel estimation method of the invention.
Compared with the prior art, the method can obtain the channel correlation matrix among all the subcarrier channels through the power delay spectrum of the channel, greatly reduces the calculation and storage complexity of the channel estimation scheme in the wireless communication system by utilizing the cyclic characteristic of the wiener filter matrix, and simultaneously ensures that the performance of the channel estimation algorithm is not influenced.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort. In the drawings:
fig. 1 shows a prior art channel diagram of a mobile communication system;
FIG. 2 shows a block diagram of a signal time-frequency domain;
FIG. 3 shows a process flow diagram according to an embodiment of the invention.
Detailed Description
In order to solve the problems in the background art, the inventor provides a low-complexity channel estimation scheme through research, and the scheme utilizes the characteristic that a wiener filter matrix is a cyclic matrix, greatly simplifies the computational complexity and simultaneously ensures that the performance is not influenced.
Fig. 1 shows a related art mobile communication system, which includes: the transmitting end is used for transmitting signals; a wireless channel; and the receiving end is used for receiving and processing the signals. Wherein, the transmitting signal is faded by a wireless channel and added with white Gaussian noise to reach a receiving end. Fig. 2 shows the time-frequency structure of a signal, assuming that one OFDM symbol has a total of N subcarriers, where there are M pilot subcarriers, the pilots (also referred to as reference signals in the art) are evenly distributed over the whole bandwidth, with a pilot spacing FsThe index of the positions of all sub-carrier channels is represented by s, and the index of the positions of the pilots is represented by k.
Supposing that a signal carried by a pilot frequency subcarrier sent by a sending end is X, the signal is influenced by noise in the transmission process through the action of a channel H, and the system supposes that the noise is additive white Gaussian noise Z, namely the noise obeys the mean value to be 0, and the variance to be sigma is2The received signal Y at the pilot subcarrier of the receiving end can be expressed as
Y(k)＝H(k)X(k)+Z(k)
At the receiving end, firstly, the least square channel estimation is carried out according to the pilot frequency subcarrier, and the least square channel estimation result is obtained
Figure BDA0002999140150000041
The wiener filter matrix is expressed as
Figure BDA0002999140150000042
Wherein I is an identity matrix
Figure BDA0002999140150000043
For the correlation matrix of the channel responses at all subcarriers with the channel responses at the pilot subcarriers,
Figure BDA0002999140150000044
a correlation matrix being a channel response of the pilot subcarriers, wherein
Figure BDA0002999140150000045
And with
Figure BDA0002999140150000046
The subscript H of (a) denotes the sequence of frequency domain channel response components over all subcarriers, Hp denotes the sequence of frequency domain channel response components over pilot subcarrier locations,
Figure BDA0002999140150000047
the channel estimation value after wiener filtering is
Figure BDA0002999140150000051
Assuming a channel impulse response of
Figure BDA0002999140150000052
Wherein h islFor the complex gain of the l-th multipath, δ (-) isUnit impulse function, τlIs the delay of the ith multipath, and L is the number of multipaths. In a wireless system, consider different multipaths hl(i) Are independent of each other, and the power of the first multipath is sigmal 2. The channel is normalized, so
σh 2＝∑lσl 2＝1 (4)
The channel response in the frequency domain is
Figure BDA0002999140150000053
The frequency domain correlation matrix R of the overall subcarrier channel responses is then based on equations (3) and (4)HHIs an element of
Figure BDA0002999140150000054
Where E represents the expectation, and P(s) is the power value of the s time domain sample point, where RHHThe subscript H denotes the sequence of frequency domain channel responses over all subcarriers, RHH＝E(HHH)。
From the above formula, R can be derived based on calculationHHMatrix from RHHExtracting matrix formed by position indexes of pilot subcarriers from each row of the matrix to obtain correlation matrix of channel responses of all subcarriers and channel responses at the pilot subcarriers
Figure BDA0002999140150000055
From RHHExtracting the position index column of the pilot frequency from the position index row of the pilot frequency of the matrix to obtain the correlation matrix of the pilot frequency subcarrier channel response
Figure BDA0002999140150000056
At the same time, the channel correlation matrix R can be seenHHIs a circulant matrix, i.e. each row is the result of a 1-time cyclic shift of the previous row, and therefore
Figure BDA0002999140150000061
And
Figure BDA0002999140150000062
also a circulant matrix. The power values P(s) of the time domain sampling points form a power time delay spectrum R of the channelHHMay be represented as N-1, N being 0, 1, 2
Figure BDA0002999140150000063
Wherein
Figure BDA0002999140150000064
Or idft (P), where P is a vector of the power delay profile P(s) of all sub-carrier channels, s 0, 1, 2HH(0, N) is N times the nth element of the IDFT (P) output vector.
According to RHHAs can be seen from the expression, RHHMay be obtained by IDFT transformation of the power delay profile of the channel. That is, R can be obtained from the power delay profile of the channelHHThen the whole cyclic matrix can be obtained by cyclic shifting, thus obtaining
Figure BDA0002999140150000065
And
Figure BDA0002999140150000066
and further obtaining a wiener filter matrix W. Because of the fact that
Figure BDA0002999140150000067
And
Figure BDA0002999140150000068
are all circulant matrices, and according to the nature of the circulant matrices, the wiener filter matrix W is also a circulant matrix.
After obtaining the wiener filter matrix W, the channel estimation value after the wiener filtering can be obtained as
Figure BDA0002999140150000069
Because each matrix is a cyclic matrix, the channel estimation scheme of the invention can be quickly realized by fully utilizing the properties of the cyclic matrix, thereby avoiding inversion and filtering operations of large-scale matrices.
The channel estimation method of the present invention is described in detail below with reference to an embodiment of the present invention.
In an OFDM system, assuming a bandwidth of 10MHz and subcarrier spacing of 15KHz, a total of N600 available subcarriers. Each slot has 14 OFDM symbols for data transmission, where there are 2 pilot symbols, as shown in fig. 2. On the OFDM symbol where the pilot frequency is located, the pilot frequency density is 0.5, namely the pilot frequency occupies half of the sub-carrier of the symbol where the pilot frequency is located, and the pilot frequency interval FsA pilot spacing is the number of subcarriers that differ between two adjacent pilots, e.g., 1 and 3 are the subcarriers occupied by the pilots, and 3-1 is 2, then 2 is the pilot spacing, which is the inverse of the pilot density. The set of all subcarrier position indices is {0, 1, 2, 3.. 599}, with s representing all subcarrier position indices. The position index set of the pilot subcarriers is {1, 3, 5.., 599}, the pilot occupies the subcarriers at the odd positions of the symbol, and k represents the position index of the pilot subcarrier.
The execution process of the method is shown in fig. 3, and can be divided into five steps, and the specific flow is as follows.
The method comprises the following steps: the receiver performs least squares channel estimation based on the received pilot signal and the local pilot sequence
The receiving end receives the pilot signal Y k]1, 3, 5, 9 and a locally generated pilot sequence X k]K is 1, 3, 5, 9, and the least square channel estimation result is obtained as
Figure BDA0002999140150000071
Step two: obtaining the power time delay spectrum P of the channel according to the least square channel estimation result
According to one embodiment of the invention, the method can be implemented by
Figure BDA0002999140150000072
And (3) performing interpolation to obtain channel estimation results of all subcarriers, such as linear interpolation, secondary interpolation and wiener filtering interpolation, and then calculating a power delay spectrum P of all subcarrier channels, wherein the element of P is P(s), and s is 0, 1, 2. According to another embodiment of the invention, by
Figure BDA0002999140150000073
Estimating the root mean square time delay taurmsThe specific calculation process is described in the document HARSLAN, T Yucek<Delay spread estimation for wireless communication systems>Proceedings of the observation IEEE Symposium on Computers and communications ISCC 2003, in a further embodiment of the invention, the channel power delay profile is considered to be subject to τrmsNegative exponential distribution of (e.g. P(s) ═ exp (-s/τ)rms) Thus, a power delay spectrum p(s) of the channel is obtained, s 1, 2.
HHStep three: calculating the first row of the full subcarrier channel correlation matrix R
The calculation formula is shown in formula 7:
RHH(1)＝N·IDFT(P) (8)
the j-th row of the matrix is denoted herein by the matrix name plus (j), e.g. RHH(1) Representing a matrix RHHLine 1.
sStep four: computing the first F rows of a wiener filter matrix
4.1 calculation matrix
Figure BDA0002999140150000074
According to the well-known technique in the art,
Figure BDA0002999140150000075
the first line of (c) can be calculated from the following formula:
T(1)＝IDFT(PP) (9)
wherein PP is a vector consisting of
Figure BDA0002999140150000081
P (k) is the power at the pilot subcarrier position, and in this embodiment, k is 1, 3, 5.. 599, and the IDFT in equation 9 may be calculated by IFFT. Specific calculation procedures are described, for example, in Hou S, Jiang J. Low Complex Fast LMMSE-Based Channel Estimation for OFDM Systems in Frequency Selective diversity Channels [ C]Vehicular Technology conference. IEEE, 2012 and Zhou W, Lam W H.A fast LMMSE channel estimation method for OFDM systems [ J].Eurasip Journal on Wireless Communications&Networking，2009，2009(1)：1-13.
Since T is a circulant matrix, after T (1) is calculated, T can be found by right shifting T (1) row by one bit.
4.2 calculation of
Figure BDA0002999140150000082
Front F of the matrixsLine of
By extracting RHH(1) Wherein all k columns are obtained
Figure BDA0002999140150000083
First row of
Figure BDA0002999140150000084
For front FsOther ones of the rows by decimating RHH(i) Wherein all k columns are obtained
Figure BDA0002999140150000085
Row i of (1), wherein RHH(i) Is formed by RHH(i-1) is obtained by cyclic shift once, i is 2, 3sAnd k is a pilot subcarrier position index.
In this embodiment, R is extractedHH(1) All k columns (k 1, 3, 5., 599) give
Figure BDA0002999140150000086
First row of
Figure BDA0002999140150000087
By RHH(1) Cyclically shifted 1 time to obtain RHH(2) By extracting RHH(2) All k columns (k 1, 3, 5.., 599) result in
Figure BDA0002999140150000088
4.3 calculating the front F of the wiener Filter matrix WsLine of
By
Figure BDA0002999140150000089
To find the front F of WsIn a row, only need to
Figure BDA00029991401500000810
Multiplying by the T matrix to obtain W (r), i.e
Figure BDA00029991401500000811
In this embodiment, the method comprises
Figure BDA00029991401500000812
The first 2 rows of W are calculated. Because of T and
Figure BDA00029991401500000813
for circulant matrices, the multiplication of the vector and matrix described above may be performed by an FFT.
Step five: carrying out wiener filtering to obtain channel estimation output;
channel estimation result according to least square
Figure BDA0002999140150000091
The sum wiener filter matrix W may yield a channel estimate output of
Figure BDA0002999140150000092
Due to the wiener filter matrix W per FsThe rows are circularly shifted 1 time, and the multiplication of the matrix and the vector can be realized through FFT, so that the realization complexity is reduced.
In the invention, DFT can be calculated by adopting FFT and IDFT can be calculated by adopting IFFT.
The previous description is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to the disclosure will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Moreover, all or a portion of any aspect and/or embodiment may be utilized with all or a portion of any other aspect and/or embodiment, unless stated otherwise. Thus, the disclosure is not intended to be limited to the examples and designs described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims (7)

1. A method of channel estimation, comprising:
step 100: performing least square channel estimation according to the received pilot signal and the local pilot sequence;
step 200: obtaining a power time delay spectrum of a channel according to the least square channel estimation result;
step 300: calculating the first row of the correlation matrix of all the subcarrier channels according to the power time delay spectrums of the channels;
step 400: calculating the front F of the wiener filter matrix according to the first row of the correlation matrix of all the subcarrier channelssLine by line, resulting in a wiener filter matrix W, where FsFor pilot spacing, step 400 includes:
step 410, calculating a matrix
Figure FDA0003584137820000011
Included: obtaining a first row of the matrix T according to T (1) ═ IDFT (PP), wherein
Figure FDA0003584137820000012
A correlation matrix representing the channel response of the pilot subcarriers, I representing the identity matrix, σ2Representing the noise power magnitude, obeying a mean of 0 and a variance of σ2Normal distribution of (1), PP is represented by
Figure FDA0003584137820000013
The formed vector, p (k) represents the power at the pilot subcarrier position, N represents the number of subcarriers in one OFDM symbol, and the IDFT is calculated using FFT; and, right shifting one bit row by row for T (1) to calculate matrix T;
step 420, extracting the first row R of the correlation matrix of all the sub-carrier channelsHH(1) Wherein all k columns are obtained as a matrix
Figure FDA0003584137820000014
First row of
Figure FDA0003584137820000015
For matrix
Figure FDA0003584137820000016
Front F ofsOther ones of the rows by decimating RHH(i) Wherein all k columns are obtained
Figure FDA0003584137820000017
Row i of (1), wherein RHH(i) Is formed by RHH(i-1) is obtained by cyclic shift once, i is 2, 3, … FsK is a pilot subcarrier position index;
step 430, calculating the front F of the wiener filter matrix W according to the following formulasLine:
Figure FDA0003584137820000018
step 500: a channel estimate output is calculated based on the least squares channel estimate and the wiener filter matrix W.
2. The method of claim 1, wherein step 200 comprises obtaining all subcarrier channel estimation results by interpolation based on least squares channel estimation results, and then calculating power delay profile.
3. The method of claim 1, wherein step 200 comprises computing a root mean square delay τ of the channel based on least squares channel estimatesrmsAnd obeying τ according to the channel power delay profilermsDetermining the power delay spectrum of the channel according to the negative exponential distribution of the channel.
4. The method of claim 1, step 500 comprising:
channel estimation result according to least square
Figure FDA0003584137820000021
And the wiener filter matrix W to obtain a channel estimation output of
Figure FDA0003584137820000022
Wherein the front F of the wiener filter matrix is divided intosLine, per FsThe rows are cyclically shifted 1 time to obtain a wiener filter matrix W.
5. The method of claim 1, wherein the multiplication of the vector and the matrix is performed by an FFT.
6. A computer-readable storage medium, in which one or more computer programs are stored, which when executed, are for implementing the method of any one of claims 1-5.
7. A computing system, comprising:
a storage device, and one or more processors;
wherein the storage means is for storing one or more computer programs which, when executed by the processor, are for implementing the method of any one of claims 1-5.
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