U.S. patent application number 17/161628 was filed with the patent office on 2021-12-02 for time-frequency block-sparse channel estimation method based on compressed sensing.
This patent application is currently assigned to WUHAN UNIVERSITY. The applicant listed for this patent is WUHAN UNIVERSITY. Invention is credited to Bolun DU, Liulu HE, Yigang HE, Yuan HUANG, Chaolong ZHANG.
Application Number | 20210377079 17/161628 |
Document ID | / |
Family ID | 1000005968523 |
Filed Date | 2021-12-02 |
United States Patent
Application |
20210377079 |
Kind Code |
A1 |
HE; Yigang ; et al. |
December 2, 2021 |
TIME-FREQUENCY BLOCK-SPARSE CHANNEL ESTIMATION METHOD BASED ON
COMPRESSED SENSING
Abstract
A time-frequency block-sparse channel estimation method based on
compressed sensing includes the following steps. Step 1: A channel
model is established. Step 2: According to the channel model
obtained in Step 1, a sparse signal estimation value is solved by a
compressed sensing method to further calculate an index set. Step
3: According to the index set obtained in Step 2, a channel matrix
estimation value is solved. The method provides a generalized block
adaptive gBAMP algorithm, which uses time-frequency joint block
sparsity of a massive MIMO system to further optimize selection of
an index set in an algorithm iteration process to improve stability
of the algorithm. Then, without a specified threshold parameter,
based on an F norm, an adaptive iteration stop condition is
determined based on a residual, and the validity of the method is
proved.
Inventors: |
HE; Yigang; (Hubei, CN)
; HUANG; Yuan; (Hubei, CN) ; HE; Liulu;
(Hubei, CN) ; ZHANG; Chaolong; (Hubei, CN)
; DU; Bolun; (Hubei, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
WUHAN UNIVERSITY |
Hubei |
|
CN |
|
|
Assignee: |
WUHAN UNIVERSITY
HUBEI
CN
|
Family ID: |
1000005968523 |
Appl. No.: |
17/161628 |
Filed: |
January 28, 2021 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 5/0048 20130101;
H04L 5/0007 20130101; H04L 25/0242 20130101; H04B 7/0413 20130101;
H04L 25/0204 20130101 |
International
Class: |
H04L 25/02 20060101
H04L025/02; H04L 5/00 20060101 H04L005/00; H04B 7/0413 20060101
H04B007/0413 |
Foreign Application Data
Date |
Code |
Application Number |
May 26, 2020 |
CN |
202010454893.2 |
Claims
1. A time-frequency block-sparse channel estimation method based on
compressed sensing, wherein an orthogonal frequency-division
multiplexing (OFDM) system of a downlink frequency-division
duplexing (FDD) massive MIMO channel model is initialized,
supposing M antennas are disposed at a base station end and U
single-antenna users are simultaneously served, and let there be N
subcarriers in the OFDM system, wherein N.sub.P subcarriers are
used to transmit pilot signals, and L is a maximum path delay,
considering observing in R adjacent OFDM symbols, the method
comprising: Step 1: inputting a pilot signal and a reception signal
of a transmitting end, and establishing a channel model as
Y=.PSI.H+V according to the signal, wherein Y.di-elect
cons..sup.N.sup.P.sup..times.R is a reception signal matrix,
H.di-elect cons..sup.LM.times.R is a channel matrix, .PSI..di-elect
cons..sup.N.sup.P.sup..times.LM is a pilot matrix, V.di-elect
cons..sup.N.sup.P.sup..times.R is a noise matrix; Step 2: solving a
sparse signal estimation value {tilde over (H)} by a compressed
sensing method according to the channel model obtained in Step 1 to
further calculate an index set {tilde over (.GAMMA.)}.sub.k; and
Step 3: solving a channel matrix estimation value {tilde over
(H)}.sub.{tilde over (.GAMMA.)}.sub.k according to the index set
{tilde over (.GAMMA.)}.sub.k obtained in Step 2, i.e., {tilde over
(H)}.sub.{tilde over (.GAMMA.)}.sub.k=.PSI..sub.{tilde over
(.GAMMA.)}.sub.k.sup..dagger.Y, wherein a superscript ".dagger."
represents a pseudoinverse, i.e., .PSI..sub.{tilde over
(.GAMMA.)}.sub.k.sup..dagger. represents a pseudoinverse with
respect to .PSI..sub.{tilde over (.GAMMA.)}.sub.k, and after a
baseband signal is demodulated, outputting data information of the
transmitting end according to the obtained channel matrix
estimation value {tilde over (H)}.sub.{tilde over
(.GAMMA.)}.sub.k.
2. The time-frequency block-sparse channel estimation method based
on compressed sensing according to claim 1, wherein Step 1 further
comprises: after establishing the channel model, since
N.sub.P<<LM, determining that the channel model is an
underdetermined equation, and since a joint sparsity structure is
present in the massive MIMO channel, determining to reconstruct a
high-dimensional channel H from a low-dimensional vector Y by a
channel estimation method based on compressed sensing.
3. The time-frequency block-sparse channel estimation method based
on compressed sensing according to claim 1, wherein the compressed
sensing method in Step 2 specifically comprises: inputting
parameters as a measurement value Y, a sensing matrix W, a step
size S, and a maximum path delay L; initializing a residual vector
.nu..sub.0=Y, reconstructing a signal estimation value H=O.di-elect
cons..sup.LM.times.T, an index set r=0, letting an initial
iteration count k=1, and updating a step size count I=1, the method
comprising: Step 201: calculating a projection coefficient of each
column of the sensing matrix on the residual vector, i.e.,
Z=.PSI..sup.H.nu..sub.k-1; Step 202: converting a matrix Z.di-elect
cons..sup.LM.times.R into a matrix {circumflex over (Z)} of
L.times.RM by joint sparsity of the channel, and summing
{circumflex over (Z)} by row to obtain {circumflex over
(Z)}=.SIGMA..sub.i.sup.RM.parallel.{circumflex over
(Z)}.parallel..sub.F.sup.2.di-elect cons..sup.L.times.1; Step 203:
updating the index set:
.GAMMA..sub.k.sup.L=.GAMMA..sub.k-1.sup.L.orgate.{arg max ({tilde
over (Z)},S)}; Step 204: extending the index set
.GAMMA..sub.k.sup.L to .GAMMA..sub.k.sup.Li=.GAMMA..sub.k.sup.L+iL,
1.ltoreq.i.ltoreq.M, and merging the index sets,
.GAMMA..sub.k=.GAMMA..sub.k.sup.L.orgate..GAMMA..sub.k.sup.L2 . . .
.orgate..GAMMA..sub.k.sup.LM; Step 205: solving the estimation
value of the channel H by a least squares method:
H.sub..GAMMA..sub.k.sup.k=.PSI..sub..GAMMA..sub.k.sup..dagger.Y;
Step 206: converting a matrix H.sub..GAMMA..sub.k.sup.k.di-elect
cons..sup.LM.times.R into a matrix H .sub..GAMMA..sub.k.sup.k of
L.times.RM, and summing H .sub..GAMMA..sub.k.sup.k by row to obtain
H.sub..GAMMA..sub.k.sup.k=.SIGMA..sub.i.sup.RMH
.sub..GAMMA..sub.k.sup.k.di-elect cons..sup.L.times.1; Step 207:
obtaining an index set: .GAMMA..sub.k.sup.L=arg max
(H.sub..GAMMA..sub.k.sup.k, S); Step 208: extending the index set
.GAMMA..sub.k.sup.L to .GAMMA..sub.k.sup.Li=.GAMMA..sub.k.sup.L+iL,
1.ltoreq.i.ltoreq.M, and merging the index sets,
.GAMMA..sub.k=.GAMMA..sub.k.sup.L.orgate..GAMMA..sub.k.sup.L2 . . .
.orgate..GAMMA..sub.k.sup.LM; Step 209: solving the estimation
value of the channel H by a least squares method: {tilde over
(H)}.sub..GAMMA..sub.k.sup.k=.PSI..sub..GAMMA..sub.k.sup..dagger.Y;
Step 210: updating the residual: .nu.'.sub.k=Y-.PSI.{tilde over
(H)}.sub..GAMMA..sub.k.sup.k; Step 211: if
.parallel..nu.'.sub.k.parallel..sub.F>.parallel..nu..sub.k-1.parallel.-
.sub.F, then {tilde over (.GAMMA.)}.sub.k={circumflex over
(.GAMMA.)}.sub.k and stopping operation; Step 212: if
.parallel..nu.'.sub.k.parallel..sub.F=.parallel..nu..sub.k-1.parallel..su-
b.F, then I=I+1, S=S.times.I, {circumflex over
(.GAMMA.)}.sub.k=.GAMMA..sub.k; Step 213: if
.parallel..nu.'.sub.k.parallel..sub.F<.parallel..nu..sub.k-1.parallel.-
.sub.F, then .nu..sub.k=.nu.'.sub.k,
.GAMMA..sub.k.sup.L=.GAMMA..sub.k.sup.L; and Step 214: k=k+1,
repeating Step 201 to Step 214 until the stop condition is
satisfied.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims the priority benefit of China
application serial no. 202010454893.2, filed on May 26, 2020. The
entirety of the above-mentioned patent application is hereby
incorporated by reference herein and made a part of this
specification.
BACKGROUND
Technical Field
[0002] The disclosure relates to the field of pilot-assisted
channel estimation in a wireless communication system, and in
particular, to a time-frequency block-sparse channel estimation
method based on compressed sensing.
Description of Related Art
[0003] Massive multiple-input and multiple-output (MIMO) is a key
technology in next-generation 5G mobile cellular network
communications and can improve the system capacity and spectrum
utilization. However, in a massive MIMO system, as the antenna
quantity at the base station end and the number of users in a cell
increase, the acquisition and accuracy of channel state information
become key issues. Compared with the time-division duplexing (TDD)
system, the frequency-division duplexing (FDD) system can provide
more efficient communication with low delay and dominates the
current wireless communication. Therefore, it is necessary to study
more effective channel estimation of the FDD system.
[0004] In a massive MIMO system, a channel has block sparsity of
its time domain, frequency domain, and spatial domain. With respect
to this sparsity structure, in recent years, many scholars have
applied the compressed sensing theory to pilot-assisted channel
estimation to achieve better performance. However, these algorithms
all require a specified threshold condition to ensure the algorithm
reconstruction precision, and for different occasions, the
threshold is different. Therefore, how to determine the size of the
threshold becomes a difficult issue.
SUMMARY
[0005] The disclosure addresses the issue of channel estimation of
an FDD downlink massive MIMO system which remains unsolved in the
related art, and provides a time-frequency block-sparse channel
estimation method based on compressed sensing which can quickly and
accurately recover massive MIMO channel information of which the
sparsity degree is unknown.
[0006] The technical solutions adopted to solve the technical
problems herein are as follows.
[0007] The disclosure provides a time-frequency block-sparse
channel estimation method based on compressed sensing, where an
orthogonal frequency-division multiplexing (OFDM) system of a
downlink frequency-division duplexing (FDD) massive MIMO channel
model is initialized, supposing M antennas are disposed at a base
station end and U single-antenna users are simultaneously served,
and let there be N subcarriers in the OFDM system, where N.sub.P
subcarriers are used to transmit pilot signals, and L is a maximum
path delay, considering observing in R adjacent OFDM symbols.
[0008] Based on a time-frequency block sparsity and a compressed
sensing framework of a massive MIMO channel, the method includes
the following steps.
[0009] Step 1: A pilot signal and a reception signal of a
transmitting end are inputted, and a channel model is established
as Y=.PSI.H+V according to the signal,
[0010] where Y.di-elect cons..sup.N.sup.P.sup..times.R is a
reception signal matrix, H.di-elect cons..sup.N.sup.P.sup..times.R
is a channel matrix, .PSI.=.di-elect
cons..sup.N.sup.P.sup..times.LM is a pilot matrix, V.di-elect
cons..sup.N.sup.P.sup..times.R is a noise matrix.
[0011] Step 2: A sparse signal estimation value {tilde over (H)} is
solved by a compressed sensing method according to the channel
model obtained in Step 1 to further calculate an index set {tilde
over (.GAMMA.)}.sub.k.
[0012] Step 3: A channel matrix estimation value {tilde over
(H)}.sub.{tilde over (.GAMMA.)}.sub.k is solved according to the
index set {tilde over (.GAMMA.)}.sub.k obtained in Step 2, i.e.,
{tilde over (H)}.sub.{tilde over (.GAMMA.)}.sub.k=.PSI..sub.{tilde
over (.GAMMA.)}.sub.k.sup..dagger.Y, where a superscript ".dagger."
represents a pseudoinverse, i.e., .PSI..sub.{tilde over
(.GAMMA.)}.sub.k.sup..dagger. represents a pseudoinverse with
respect to .PSI..sub.{tilde over (.GAMMA.)}.sub.k, and after a
baseband signal is demodulated, data information of the
transmitting end is outputted according to the obtained channel
matrix estimation value {tilde over (H)}.sub.{tilde over
(.GAMMA.)}.sub.k.
[0013] Further, Step 1 of the disclosure further includes the
following.
[0014] After the channel model is established, since
N.sub.P<<LM, it is determined that the channel model is an
underdetermined equation, and since a joint sparsity structure is
present in the massive MIMO channel, it is determined to
reconstruct a high-dimensional channel H from a low-dimensional
vector Y by a channel estimation method based on compressed
sensing.
[0015] Further, the compressed sensing method in Step 2
specifically includes the following.
[0016] Parameters are inputted as a measurement value Y, a sensing
matrix .PSI., a step size S, and a maximum path delay L; a residual
vector .nu..sub.0=Y is initialized, a signal estimation value
H=O.di-elect cons..sup.LM.times.T is reconstructed, an index set
.GAMMA.=O, let an initial iteration count k=1, and a step size
count I=1 is updated. The method includes the following steps.
[0017] Step 201: A projection coefficient of each column of the
sensing matrix on the residual vector is calculated, i.e.,
Z=.PSI..sup.H.nu..sub.k-1.
[0018] Step 202: A matrix Z.di-elect cons..sup.LM.times.R is
converted into a matrix {circumflex over (Z)} of L.times.RM by
joint sparsity of the channel, and {circumflex over (Z)} is summed
by row to obtain {circumflex over
(Z)}=.SIGMA..sub.i.sup.RM.parallel.{circumflex over
(Z)}.parallel..sub.F.sup.2.di-elect cons..sup.L.times.1.
[0019] Step 203: The index set updated:
.GAMMA..sub.k.sup.L=.GAMMA..sub.k-1.sup.L.orgate.{arg max ({tilde
over (Z)},S)}.
[0020] Step 204: The index set .GAMMA..sub.k.sup.L is extended to
.GAMMA..sub.k.sup.Li=.GAMMA..sub.k.sup.L+iL, 1.ltoreq.i.ltoreq.M,
and the index sets are merged,
.GAMMA..sub.k=.GAMMA..sub.k.sup.L.orgate..GAMMA..sub.k.sup.L2 . . .
.orgate..GAMMA..sub.k.sup.LM.
[0021] Step 205: The estimation value of the channel H is solved by
a least squares method:
H.sub..GAMMA..sub.k.sup.k=.PSI..sub..GAMMA..sub.k.sup..dagger.Y.
[0022] Step 206: A matrix H.sub..GAMMA..sub.k.sup.k.di-elect
cons..sup.LM.times.R is converted into a matrix H
.sub..GAMMA..sub.k.sup.k of L.times.RM, and H
.sub..GAMMA..sub.k.sup.k is summed by row to obtain
H.sub..GAMMA..sub.k.sup.k=.SIGMA..sub.i.sup.RMH
.sub..GAMMA..sub.k.sup.k .di-elect cons..sup.L.times.1.
[0023] Step 207: An index set is obtained: .GAMMA..sub.k.sup.L=arg
max (H.sub..GAMMA..sub.k.sup.k,S).
[0024] Step 208: The index set .GAMMA..sub.k.sup.L is extended to
.GAMMA..sub.k.sup.Li=.GAMMA..sub.k.sup.L+iL, 1.ltoreq.i.ltoreq.M,
and the index sets are merged, .GAMMA..sub.k=.GAMMA..sub.k.sup.L
.orgate..GAMMA..sub.k.sup.L2 . . .
.orgate..GAMMA..sub.k.sup.LM.
[0025] Step 209: The estimation value of the channel H is solved by
a least squares method: {tilde over
(H)}.sub..GAMMA..sub.k.sup.k=.PSI..sub..GAMMA..sub.k.sup..dagger.Y.
[0026] Step 210: The residual is updated: .nu.'.sub.k=Y-.PSI.{tilde
over (H)}.sub..GAMMA..sub.k.sup.k.
[0027] Step 211: If
.parallel..nu.'.sub.k.parallel..sub.F>.parallel..nu..sub.k-1.parallel.-
.sub.F, then {tilde over (.GAMMA.)}.sub.k={circumflex over
(.GAMMA.)}.sub.k and operation is stopped.
[0028] Step 212: If
.parallel..nu.'.sub.k.parallel..sub.F=.parallel..nu..sub.k-1.parallel..su-
b.F, then I=I+1, S=S.times.I, {circumflex over
(.GAMMA.)}.sub.k=.GAMMA..sub.k.
[0029] Step 213: If
.parallel..nu.'.sub.k.parallel..sub.F<.parallel..nu..sub.k-1.parallel.-
.sub.F, then .nu..sub.k=.nu.'.sub.k,
.GAMMA..sub.k.sup.L=.GAMMA..sub.k.sup.L.
[0030] Step 214: k=k+1, Step 201 to Step 214 are repeated until the
stop condition is satisfied.
[0031] In the time-frequency block-sparse channel estimation method
based on compressed sensing of the disclosure, with respect to an
FDD downlink massive MIMO system, the iteration stop condition is
adaptively determined based on the residual by using channel
time-frequency block sparsity while there is no threshold parameter
and the sparsity degree is unknown, which achieves more accurate
channel estimation performance than conventional matching pursuit
algorithms. Simulation shows that the algorithm can quickly and
accurately recover massive MIMO channel information of which the
sparsity degree is unknown.
BRIEF DESCRIPTION OF THE DRAWINGS
[0032] The disclosure will be further described below with
reference to the accompanying drawings and embodiments.
[0033] FIG. 1 is a diagram showing normalized mean square errors
(NMSE) at different signal-to-noise ratios (SNR) of an embodiment
of the disclosure and comparative embodiments.
[0034] FIG. 2 is a diagram showing normalized mean square errors at
different transmitting antenna quantities of the embodiment of the
disclosure and the comparative embodiments.
DESCRIPTION OF THE EMBODIMENTS
[0035] To make the objectives, technical solutions, and advantages
of the disclosure more apparent, the disclosure will be described
in further detail below with reference to the accompanying drawings
and embodiments. It should be understood that the specific
embodiments described herein are merely illustrative of the
disclosure and are not intended to limit the disclosure.
[0036] In an embodiment of the disclosure, an FDD downlink massive
MIMO system is considered, in which an antenna quantity of a base
station is M=20, and U=6 single-antenna users are simultaneously
served. A total number of subcarriers of OFDM symbols is N=4096,
where N.sub.P=100 subcarriers are used to transmit pilot signals.
Pilots are placed all in the same manner; namely, they are
distributed randomly and the pilots among different antennas are
orthogonal to each other. A channel length L is 160, and a TU-6
channel model is adopted, where a number of paths S=6, path delays
are respectively 0.0, 0.2, 0.5, 1.6, 2.3, 5, and path gains are
respectively -3, 0, -2, -6, -8, -10. Let a coherence time T of the
channel be T=4 OFDM symbols. Based on a time-frequency block
sparsity and a compressed sensing framework of a massive MIMO
channel, the channel estimation method includes the following
steps.
[0037] Step 1: A pilot signal and a reception signal of a
transmitting end are inputted, and a channel model is established
as Y=.PSI.H+V according to the signal.
[0038] where Y.di-elect cons..sup.N.sup.P.sup..times.R is a
reception signal matrix, H.di-elect cons..sup.LM.times.R is a
channel matrix, .PSI.=.di-elect cons..sup.N.sup.P.sup..times.LM is
a pilot matrix, V.di-elect cons..sup.N.sup.P.sup..times.R is a
noise matrix; since N.sub.P<<LM, the channel model is an
underdetermined equation, but a joint sparsity structure is present
in the massive MIMO channel, and a high-dimensional channel H may
be reconstructed from a low-dimensional vector Y by a channel
estimation method based on compressed sensing.
[0039] Step 2: A sparse signal estimation value {tilde over (H)} is
solved by a compressed sensing method according to the channel
model obtained in Step 1 to further calculate an index set {tilde
over (.GAMMA.)}.sub.k.
[0040] The compressed sensing method in Step 2 specifically
includes the following.
[0041] Parameters are inputted as a measurement value Y, a sensing
matrix .PSI., a step size S, and a maximum path delay L; a residual
vector .nu..sub.0=Y is initialized, a signal estimation value
H=O.di-elect cons..sup.LM.times.T is reconstructed, an index set
.GAMMA.=O, letting an initial iteration count k=1, and a step size
count I=1 is updated; the method includes the following steps.
[0042] Step 201: A projection coefficient of each column of the
sensing matrix on the residual vector is calculated, i.e.,
Z=.PSI..sup.H.nu..sub.k-1.
[0043] Step 202: A matrix Z.di-elect cons..sup.LM.times.R is
converted into a matrix {circumflex over (Z)} of L.times.RM by
joint sparsity of the channel, and {circumflex over (Z)} is summed
by row to obtain {tilde over
(Z)}=.SIGMA..sub.i.sup.RM.parallel.{circumflex over
(Z)}.parallel..sub.F.sup.2.di-elect cons..sup.L.times.1.
[0044] Step 203: The index set is updated:
.GAMMA..sub.k.sup.L=.GAMMA..sub.k-1.sup.L.orgate.{arg max ({tilde
over (Z)}, S)}.
[0045] Step 204: The index set .GAMMA..sub.k.sup.L is extended to
.GAMMA..sub.k.sup.Li=.GAMMA..sub.k.sup.L+iL, 1.ltoreq.i.ltoreq.M,
and the index sets are merged,
.GAMMA..sub.k=.GAMMA..sub.k.sup.L.orgate..GAMMA..sub.k.sup.L2 . . .
.orgate..GAMMA..sub.k.sup.LM.
[0046] Step 205: The estimation value of the channel H is solved by
a least squares method:
H.sub..GAMMA..sub.k.sup.k=.PSI..sub..GAMMA..sub.k.sup..dagger.Y.
[0047] Step 206: A matrix H.sub..GAMMA..sub.k.sup.k.di-elect
cons..sup.LM.times.R is converted in to a matrix H
.sub..GAMMA..sub.k.sup.k of L.times.RM, and H
.sub..GAMMA..sub.k.sup.k is summed by row to obtain
H.sub..GAMMA..sub.k.sup.k=.SIGMA..sub.i.sup.RMH
.sub..GAMMA..sub.k.sup.k.di-elect cons..sup.L.times.1.
[0048] Step 207: An index set is obtained: .GAMMA..sub.k.sup.L=arg
max (H.sub..GAMMA..sub.k.sup.k, S).
[0049] Step 208: The index set .GAMMA..sub.k.sup.L is extended to
.GAMMA..sub.k.sup.Li=.GAMMA..sub.k.sup.L+iL, 1.ltoreq.i.ltoreq.M,
and the index sets are merged,
.GAMMA..sub.k=.GAMMA..sub.k.sup.L.orgate..GAMMA..sub.k.sup.L2 . . .
.orgate..GAMMA..sub.k.sup.LM.
[0050] Step 209: The estimation value of the channel H is solved by
a least squares method: {tilde over
(H)}.sub..GAMMA..sub.k.sup.k=.PSI..sub..GAMMA..sub.k.sup..dagger.Y.
[0051] Step 210: The residual is updated: .nu.'.sub.k=Y-.PSI.{tilde
over (H)}.sub..GAMMA..sub.k.sup.k.
[0052] Step 211: If
.parallel..nu.'.sub.k.parallel..sub.F>.parallel..nu..sub.k-1.parallel.-
.sub.F, then {tilde over (.GAMMA.)}.sub.k={circumflex over
(.GAMMA.)}.sub.k and operation is stopped.
[0053] Step 212: If
.parallel..nu.'.sub.k.parallel..sub.F=.parallel..nu..sub.k-1.parallel..su-
b.F, then I=I+1, S=S.times.I, {circumflex over
(.GAMMA.)}.sub.k=.GAMMA..sub.k.
[0054] Step 213: If
.parallel..nu.'.sub.k.parallel..sub.F<.parallel..nu..sub.k-1.parallel.-
.sub.F, then .nu..sub.k=.nu.'.sub.k,
.GAMMA..sub.k.sup.L=.GAMMA..sub.k.sup.L.
[0055] Step 214: k=k+1; Step 201 to Step 214 are repeated until the
stop condition is satisfied.
[0056] Step 3: According to the index set {tilde over
(.GAMMA.)}.sub.k obtained in Step 2, a channel matrix estimation
value {tilde over (H)}.sub.{tilde over (.GAMMA.)}.sub.k may be
solved, i.e., {tilde over (H)}.sub.{tilde over
(.GAMMA.)}.sub.k=.PSI..sub.{tilde over
(.GAMMA.)}.sub.k.sup..dagger.Y, where a superscript ".dagger."
represents a pseudoinverse, i.e., .PSI..sub.{tilde over
(.GAMMA.)}.sub.k.sup..dagger. represents a pseudoinverse with
respect to .PSI..sub.{tilde over (.GAMMA.)}.sub.k, and according to
the obtained channel matrix estimation value {tilde over
(H)}.sub.{tilde over (.GAMMA.)}.sub.k, after a baseband signal is
demodulated, data information of the transmitting end is
outputted.
[0057] To evaluate the performance of the disclosure, when the
antenna quantity M is 16, the step size s is 2, and a threshold
parameter .mu. of the algorithm reconstruction precision is all
0.001, normalized least mean square errors of channel estimation
algorithms at different signal-to-noise ratios are calculated, and
the result is shown in FIG. 1.
[0058] To further evaluate the performance of the disclosure, when
the signal-to-noise ratio is 20 dB, the step size s is 2, the
threshold parameter .mu. of the algorithm reconstruction precision
is all 0.001, normalized least mean square errors of channel
estimation algorithms at different transmitting antennas of the
base station are calculated, and the result is shown in FIG. 2.
[0059] The normalized least mean square error is defined as
follows:
NMSE = j = 1 N e .times. i = 1 LMT .times. ( H ~ - H ) 2 N e
.times. MT ##EQU00001##
[0060] where N.sub.e represents an operation count of the algorithm
at each signal-to-noise ratio, and herein, N.sub.e is 20.
[0061] According to FIG. 1, in the embodiment of the disclosure,
with the generalized adaptive mechanism introduced, the proposed
gBAMP algorithm can detect the minimum precision achieved by the
reconstruction and automatically end the reconstruction process
without the constraint of the threshold parameter .mu.. The
experimental result shows that the algorithm achieves good
performance close to that of the exact-LS algorithm and is superior
to other algorithms.
[0062] According to FIG. 2, as the antenna quantity increases, the
precision of channel reconstruction is affected, and the reason
lies in that the increase in the antenna quantity leads to pilot
insufficiency. The reconstruction by the SAMP-Block algorithm fails
at the threshold parameter. However, in the embodiment of the
disclosure, when the antenna quantity is 16 more, the proposed
gBAMP algorithm exhibits better performance, and exhibits optimal
performance in both precision and stability that are even better
than those of the exact-LS algorithm.
[0063] The time-frequency block-sparse channel estimation method
based on compressed sensing in the embodiment of the disclosure,
i.e., a generalized block adaptive gBAMP algorithm, has good
reconstruction performance and is applicable to occasions requiring
pilot-assisted channel estimation of a wireless communication
system.
[0064] Simulation shows that the method of the disclosure can
quickly and accurately recover massive MIMO channel information of
which a sparsity degree is unknown.
[0065] It will be understood that modifications and variations may
be made by persons skilled in the art according to the above
description, and all such modifications and variations are intended
to be included within the scope of the disclosure as defined in the
appended claims.
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