CN108494445A - A kind of Downlink channel estimation method of the extensive MIMO communication system based on uplink traffic channel information auxiliary - Google Patents
A kind of Downlink channel estimation method of the extensive MIMO communication system based on uplink traffic channel information auxiliary Download PDFInfo
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- CN108494445A CN108494445A CN201810042658.7A CN201810042658A CN108494445A CN 108494445 A CN108494445 A CN 108494445A CN 201810042658 A CN201810042658 A CN 201810042658A CN 108494445 A CN108494445 A CN 108494445A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0413—MIMO systems
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- H04L25/0224—Channel estimation using sounding signals
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- 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
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Abstract
The invention discloses a kind of Downlink channel estimation methods of the extensive MIMO communication system based on uplink traffic channel information auxiliary, including：Step 1：Base station uses a uniform linear array with N root antennas, and mobile subscriber uses single antenna.Mobile subscriber's pilot signal transmitted after base station receives signal, goes out uplink channel using classical Least Square Method, is denoted asStep 2：Base station pilot signal transmitted matrix X within T moment, the then signal that mobile subscriber receives are denoted as y.Step 3：Initialization correlated variables l, w,α andStep 4：α is updated,γ and τ.Step 5：Update β.Step 6：Judge whether iteration count variable l reaches whether the upper limit L or γ restrain, if be all unsatisfactory for, iteration count variable l=l+1, and return to step 3.Step 7：Thresholding η is set, and chooses the effective angle set of downlink channel using the thresholdingStep 8：Utilize effective angle setEstimate final downlink channel
Description
Technical field
The invention belongs to wireless communication field, be related to a kind of multiple-input and multiple-output (Multi-input Multi-output,
MIMO) the channel estimation methods of communication system, it is specifically a kind of based on the extensive of uplink traffic channel information auxiliary
The Downlink channel estimation method of MIMO communication system.
Background technology
Multiple-input and multiple-output (Multi-input Multi-output, MIMO) technology has become the core of future communication systems
One of heart technology, while also will be one of core technology used by WLAN standard.MIMO communication system is on a large scale
Refer to and tens of even hundreds of or more antennas are centrally placed in a manner of large scale array in base station coverage area.Due to possessing
The spatial degrees of freedom of hundreds of antenna element, extensive mimo system is higher, can be by energy using beam-forming technology
Smaller region is concentrated on, transmission rate is greatly improved and improves energy efficiency.Extensive mimo system is expected to from root
The spectrum efficiency and energy efficiency that mobile communication is solved the problems, such as in sheet have become one of the important technology of 5G mobile communication.
Channel estimation is the basis of signal of communication detection and Adaptive Transmission, is played to the performance of communication system most important
Effect.Since the antenna number of base station is more, the Downlink channel estimation of extensive mimo system becomes abnormal difficult, people
The angle restored from sparse signal is attempted, it is proposed that some sparse downlink link channel estimation sides based on Fourier transform
Method.Such as in document Rao X.and Lau V.K., Distributed compressive CSIT estimation and
feedback for FDD multi-user massive MIMO systems,IEEE Transactions on Signal
It is proposed in Processing, 62 (12) (2014) 3261-3271 a kind of based on l1The channel estimation of the Fourier transform of-norm
Method.But one of main bottleneck of existing method is：Single observation sample is only utilized in corresponding rarefaction representation optimization problem.
Therefore, additional observation sample information is made full use of, it will help improve the performance of Downlink channel estimation.
Invention content
For the deficiency of existing method, the present invention propose it is a kind of assisted based on uplink traffic channel information it is extensive
The Downlink channel estimation method of MIMO communication system.
Include the following steps for realizing technical solution of the invention：
Step 1：Base station uses a uniform linear array with N root antennas, and mobile subscriber uses single antenna.It is mobile
User's pilot signal transmitted after base station receives signal, goes out uplink channel using classical Least Square Method, is denoted as
Step 2：Base station pilot signal transmitted matrix X within T moment, the then signal that mobile subscriber receives are y=Φ
(β)w+n。
Step 3：Iterations counting variable l=1 is set, the precision vector of w is initializedIn each element be
1, initializationPrecision vectorIn each element be 1, initialize noise precisionInitialize β simultaneously
For full neutral element.
Step 4：α is updated,γ and τ.
Step 5：Update β.
Step 6：Judge whether iteration count variable l reaches whether the upper limit L or γ restrain, if be all unsatisfactory for, iteration
Counting variable l=l+1, and return to step 4.
Step 7：Thresholding η is set, and chooses the effective angle set omega of downlink channel using the thresholding.
Step 8：Using effective angle set omega, final downlink channel is estimated.
Beneficial effects of the present invention：
Uplink traffic channel information is fused in Downlink channel estimation by the present invention, using uplink channel under
The joint sparse characteristic of Downlink channels so that the corresponding available observation sample quantity of rarefaction representation optimization problem improves
One times.Compared with the conventional method, the present invention can significantly improve the performance of channel estimation
Description of the drawings
Fig. 1 is implementing procedure figure of the present invention.
Under the conditions of Fig. 2 is 200 Monte Carlo Experiments, when signal-to-noise ratio is 0dB, when pilot tone moment T is changed by 30 to 100,
Compared with the present invention estimates the normalization root-mean-square error (NMSE) of channel respectively with conventional Fourier Transform method.
Specific implementation mode
The invention will be further described in the following with reference to the drawings and specific embodiments.
As shown in Figure 1, the method for the present invention includes following steps：
(1) base station uses a uniform linear array with N root antennas, and mobile subscriber uses single antenna.It is mobile to use
Family pilot signal transmitted after base station receives signal, goes out uplink channel using classical Least Square Method, is denoted asAnd it is expressed as the form of sparse signal recovery：Wherein：
The referred to as calculation matrix of uplink,
Expression is evenly dividingN number of mesh point, i.e.,
dn, n=1,2 ..., N, the spacing of expression the n-th array element and the 1st array element,Indicate uplink electrical magnetic wave
Wavelength,
In element βiIndicate θiOn angular deviation,
It is channel in calculation matrixOn rarefaction representation vector,
E is that the mean value of a N-dimensional is 0, and precision isWhite Gaussian noise vector.
(2) base station pilot signal transmitted matrix X within T moment, the then signal that mobile subscriber receives are y=Φ (β) w
+ n, wherein
Φ (β)=XA (β) is known as the calculation matrix of downlink,
A (β)=[a (θ1+β1),a(θ2+β2),...,a(θN+βN)],
λ indicates the wavelength of downlink electromagnetic wave,
W is rarefaction representation vector of the channel on calculation matrix Φ (β),
N is that the mean value of T dimensions is 0, the white Gaussian noise vector that precision is α.
(3) iterations counting variable l=1 is set, the precision vector of w is initializedIn each element be 1, just
BeginningizationPrecision vectorIn each element be 1, initialize noise precisionIt is complete zero to initialize β simultaneously
Element.
(4) α is updated,γ and τ, i.e.,：
Wherein：
The mark of tr () representing matrix, | | | |22 norms of representing matrix, []iiI-th of representing matrix is diagonal
Line element, ()HExpression conjugate transposition, a=b=0.0001,
μ (α, γ, β)=α Σ (α, γ, β) ΦH(β) y, Σ (alpha, gamma, β)=(α ΦH(β)Φ(β)+diag(γ))-1
⊙ indicates Hadamard products,
Diag () indicates diagonal operation matrix, Ξ (alpha, gamma, β)=μ (alpha, gamma, β) μH(alpha, gamma, β)+Σ (alpha, gamma, β),
(5) β is updated, i.e.,：
Wherein
Sign () expressions take sign operation,
Re () expressions take real part operation,
ci1=-α (χii+|μi|2),
μiIndicate i-th of element of μ (alpha, gamma, β), χjiIndicate Σ (α, γ, β)
(j, i) a element,
(·)*Indicate conjugate operation, a'(θi+βi) indicate a (θi+βi) about βiDerivative,
It indicatesI-th of element,It indicates
(j, i) a element,It indicatesAbout βiDerivative.
(6) judge whether iteration count variable l reaches upper limit L (such as L=100) or whether γ restrains (i.e. when secondary update
As a result whether equal with last time update result), if be all unsatisfactory for, iteration count variable l=l+1, and return to (4).
(7) thresholding is setWhereinIndicate the maximum element of i-th of value in μ (alpha, gamma, β), and
Using the thresholding choose downlink channel effective angle set omega=i | (μi)2>=η, i=1,2 ..., N }.
(8) effective angle set omega is utilized, estimates final downlink channel：Wherein
(·)ΩIndicate by the corresponding Column vector groups of set omega in matrix at submatrix,The generalized inverse of representing matrix.
The effect of the present invention is described further with reference to emulation experiment.
In order to assess the performance of this method, it is assumed that base station uses a homogenous linear battle array with N=150 root antennas
The working frequency of row, downlink is 1980MHz, and the working frequency of downlink is 2170MHz, and wireless channel is by 3GPP
Spatial channel model (SCM) model randomly generates, and each element of base station pilot signal transmitted matrix X obeys zero
The independent Gaussian of mean value unit variance is distributed, and ambient noise is assumed to be white Gaussian noise.
Experiment condition
It uses the present invention in signal-to-noise ratio for 0dB, 200 estimations is carried out to channel when pilot tone moment T is changed by 30 to 100,
Simulation result is as shown in Figure 2.
Experimental analysis
Figure it is seen that the present invention can accurately estimate out the downlink channel of extensive MIMO communication system,
NMSE performances are substantially better than conventional method.
The series of detailed descriptions listed above only for the present invention feasible embodiment specifically
Bright, they are all without departing from equivalent implementations made by technical spirit of the present invention not to limit the scope of the invention
Or change should all be included in the protection scope of the present invention.
Claims (10)
1. a kind of Downlink channel estimation method of the extensive MIMO communication system based on uplink traffic channel information auxiliary,
It is characterised in that it includes following steps：
Step 1：Using a uniform linear array with N root antennas, mobile subscriber uses single antenna, mobile subscriber's hair for base station
After sending pilot signal, base station to receive signal, uplink channel is gone out using classical Least Square Method, is denoted asWherein,For the calculation matrix of uplink,It is channel in calculation matrixOn it is sparse
Indicate that vector, e are that the mean value of a N-dimensional is 0, precision isWhite Gaussian noise vector；
Step 2：Base station pilot signal transmitted matrix X within T moment, the then signal that mobile subscriber receives are y=Φ (β) w+
n；Wherein, Φ (β) is known as the calculation matrix of downlink, and w is rarefaction representation vector of the channel on calculation matrix Φ (β), and n is
The mean value of one T dimensions is 0, the white Gaussian noise vector that precision is α；
Step 3：The initial value that iterations counting variable l is arranged is l=1, initializes the precision vector of wIn it is each
Element is 1, initializationPrecision vectorIn each element be 1, initialize noise precisionJust simultaneously
Beginningization β is full neutral element；
Step 4：α is updated,γ and τ；
Step 5：Update β；
Step 6：Judge whether iteration count variable l reaches whether the upper limit L or γ restrain, if be all unsatisfactory for, iteration count
Variable l=l+1, and return to step 4.
Step 7：Thresholding η is set, and chooses the effective angle set omega of downlink channel using the thresholding；
Step 8：Using effective angle set omega, final downlink channel is estimated.
2. under a kind of extensive MIMO communication system based on uplink traffic channel information auxiliary according to claim 1
Downlink channels method of estimation, which is characterized in that in the step 1Wherein
Expression is evenly dividingN number of mesh point, i.e.,
dn, n=1,2 ..., N, the spacing of expression the n-th array element and the 1st array element,Indicate the wavelength of uplink electrical magnetic wave,In element βiIndicate θiOn angular deviation.
3. under a kind of extensive MIMO communication system based on uplink traffic channel information auxiliary according to claim 2
Downlink channels method of estimation, which is characterized in that Φ (β)=XA (β) in the step 2, wherein：
A (β)=[a (θ1+β1),a(θ2+β2),...,a(θN+βN)]
λ indicates downlink chain
The wavelength of road electromagnetic wave.
4. under a kind of extensive MIMO communication system based on uplink traffic channel information auxiliary according to claim 1
Downlink channels method of estimation, which is characterized in that α is updated in the step 4,The method of γ and τ is：
Wherein：
The mark of tr () representing matrix, | | | |22 norms of representing matrix, []iiI-th of diagonal entry of representing matrix,
(·)HExpression conjugate transposition, a=b=0.0001,
μ (α, γ, β)=α Σ (α, γ, β) ΦH(β) y, Σ (alpha, gamma, β)=(α ΦH(β)Φ(β)+diag(γ))-1
⊙ indicates Hadamard products,
Diag () indicates diagonal operation matrix, Ξ (alpha, gamma, β)=μ (alpha, gamma, β) μH(alpha, gamma, β)+Σ (alpha, gamma, β),
5. under a kind of extensive MIMO communication system based on uplink traffic channel information auxiliary according to claim 1
Downlink channels method of estimation, which is characterized in that the method for update β is in the step 5：
Wherein：
Sign () expressions take sign operation,
Re () expressions take real part operation,
μiIndicate i-th of element of μ (alpha, gamma, β), χjiIndicate Σ (α, γ, β) the (j,
I) a element,
(·)*Indicate conjugate operation, a'(θi+βi) indicate a (θi+βi) about βiDerivative,
It indicatesI-th of element,It indicates(j,
I) a element,It indicatesAbout βiDerivative.
6. under a kind of extensive MIMO communication system based on uplink traffic channel information auxiliary according to claim 1
Downlink channels method of estimation, which is characterized in that the expression formula of the thresholding η in the step 7 is：Its
InIndicate the maximum element of i-th of value in μ (α, γ, β).
7. under a kind of extensive MIMO communication system based on uplink traffic channel information auxiliary according to claim 6
Downlink channels method of estimation, which is characterized in that the expression formula of the effective angle set omega is：Ω=i | (μi)2>=η, i=
1,2,...,N}。
8. under a kind of extensive MIMO communication system based on uplink traffic channel information auxiliary according to claim 3
Downlink channels method of estimation, which is characterized in that the final downlink channel estimated in the step 8Wherein ()ΩIndicate by the corresponding Column vector groups of set omega in matrix at submatrix,Table
Show group inverse matrices.
9. a kind of extensive MIMO communication system based on uplink traffic channel information auxiliary according to claim 1-8
Downlink channel estimation method, which is characterized in that the L is set as 100, and the N is set as 150.
10. a kind of extensive MIMO communication system based on uplink traffic channel information auxiliary according to claim 1-8
Downlink channel estimation method, which is characterized in that pilot tone moment T be 30 to 100.
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Cited By (3)
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| CN112422458A (en) * | 2019-08-23 | 2021-02-26 | 中兴通讯股份有限公司 | Channel estimation method, apparatus and computer storage medium |
| CN113489519A (en) * | 2021-07-07 | 2021-10-08 | 东南大学 | Wireless communication transmission method for asymmetric large-scale MIMO system |
| CN114726685A (en) * | 2022-03-04 | 2022-07-08 | 江苏大学 | Low-complexity downlink channel estimation method for large-scale MIMO communication system |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112422458A (en) * | 2019-08-23 | 2021-02-26 | 中兴通讯股份有限公司 | Channel estimation method, apparatus and computer storage medium |
| WO2021036999A1 (en) * | 2019-08-23 | 2021-03-04 | 中兴通讯股份有限公司 | Channel estimation method and apparatus, and computer storage medium |
| CN113489519A (en) * | 2021-07-07 | 2021-10-08 | 东南大学 | Wireless communication transmission method for asymmetric large-scale MIMO system |
| CN114726685A (en) * | 2022-03-04 | 2022-07-08 | 江苏大学 | Low-complexity downlink channel estimation method for large-scale MIMO communication system |
| CN114726685B (en) * | 2022-03-04 | 2024-04-09 | 江苏大学 | Low-complexity downlink channel estimation method for large-scale MIMO communication system |
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