CN106130938B - Multi-user joint channel estimation method for TDD large-scale MIMO system - Google Patents

Multi-user joint channel estimation method for TDD large-scale MIMO system Download PDF

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CN106130938B
CN106130938B CN201610561361.2A CN201610561361A CN106130938B CN 106130938 B CN106130938 B CN 106130938B CN 201610561361 A CN201610561361 A CN 201610561361A CN 106130938 B CN106130938 B CN 106130938B
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叶新荣
张爱清
谢小娟
陈卫松
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Anhui Normal University
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Abstract

The invention discloses an uplink multi-user joint channel estimation method for a large-scale MIMO communication system. The channel estimation method comprises the steps of firstly realizing sparse representation of a physical channel matrix through virtual channel description, modeling multi-user joint channel estimation of a target cell and an interference cell into two-dimensional sparse signal reconstruction in a compressive sensing framework, and then jointly estimating multi-user channel state information through a two-dimensional sparse signal reconstruction algorithm (2D-SL 0). The multi-user joint channel estimation method provided by the invention can greatly reduce the number of pilot frequencies, eliminate the interference of pilot frequency pollution and improve the accuracy of channel estimation.

Description

Multi-user joint channel estimation method for TDD large-scale MIMO system
Technical Field
The invention relates to an uplink multi-user joint channel estimation method of a large-scale multi-input multi-output (MIMO) system, in particular to a large-scale MIMO system channel estimation method applying a compressed sensing technology in the field of 5G (fifth generation mobile communication system) standardization process.
Background
The large-scale MIMO technology can improve the spectrum efficiency of the system, reduce the transmission power and the processing complexity of the system, and is an indispensable support technology in a future 5G communication system. The CSI (channnel State information) information is needed for signal detection and precoding in a massive MIMO system, and particularly, when the complete CSI information is known by a transmitting end, the transmitting power of a user is inversely proportional to the number of antennas of a base station, and when only partial CSI is obtained, the transmitting power is inversely proportional to the square root of the number of antennas of the base station. Therefore, whether the channel state information can be accurately estimated plays a decisive role in the large-scale MIMO wireless transmission performance and the energy efficiency.
The length of the uplink pilot sequence in tdd (time Division multiplexing) massive MIMO system is proportional to the number of mobile terminals served by the base station, so that the pilot overhead for channel parameter estimation increases linearly as the number of users increases. Particularly, in a medium-high speed mobile communication scenario, the pilot overhead will consume most of the time-frequency resources, which becomes a bottleneck of the system. Generally, the impulse response of the wireless channel only contains a small number of significant path coefficients, and the major paths have a large interval in the time domain, i.e. the impulse response of the wireless channel has a sparse characteristic. On the other hand, the compressed sensing technology can reconstruct a high-dimensional sparse signal according to fewer observed values, so that the compressed channel sensing method has the potential of greatly reducing the number of pilot symbols of a large-scale MIMO system by fully excavating the prior knowledge of the sparsity of the wireless channel. The traditional large-scale MIMO system compressed channel sensing method is characterized in that channel estimation is modeled into one-dimensional sparse signal reconstruction, channel state information of multiple users in one beam direction can be estimated at one time, and joint channel estimation of the multiple users is difficult to realize.
Disclosure of Invention
The invention aims to provide an uplink multi-user joint channel estimation method, which overcomes the problems of excessive pilot frequency number and pilot frequency pollution interference required by channel estimation in the prior art.
In order to achieve the above object, the present invention provides an uplink multi-user joint channel estimation method, which includes:
step 11, realizing sparse representation of a physical channel matrix through virtual channel representation;
and step 12, modeling the channel estimation of multiple users in the target cell and the interference cell as two-dimensional sparse signal reconstruction in a compressed sensing theory, and jointly estimating the channel state information of the target multiple users by using a 2D-SL0(two-dimensional smoothed L0 algorithm) reconstruction algorithm.
Preferably, in step 11, the physical channel matrix from all K users in the ith cell to the jth cell base station is sparsely represented as
Figure BDA0001051769070000021
Wherein G isjiIs a physical channel matrix and Gji＝[gji1,…,gjiK]Column vector gjikRepresenting the channel response vector from the kth user in the ith cell to the base station of the jth cell;
ARis a received response matrix and
Figure BDA0001051769070000022
where theta ismM/M, M representing the number of base station antennas, M × K dimensional matrix
Figure BDA0001051769070000023
Is a sparse representation matrix and its element values
Figure BDA0001051769070000024
Indicating the link gain between the kth user and the mth virtual reception angle, and if the link does not exist, the element has a value of 0.
Preferably, in step 12, the method for modeling channel estimation of multiple users in a target cell and an interfering cell as two-dimensional sparse signal reconstruction includes:
the received pilot signal for the jth cell base station may be expressed as:
Figure BDA0001051769070000031
transposing this equation yields the following equation:
Figure BDA0001051769070000032
wherein Hji＝GjiDjiAnd HjiRepresents the channel response matrix from the kth user in the ith cell to the jth cell site, where Hji＝[hji1,…,hjiK]，
Figure BDA0001051769070000033
Figure BDA0001051769070000034
βjikThe large-scale fading factor is represented, K represents the number of single-antenna users, and K is less than M.
The following linear equation can be obtained:
Y＝XGAR+N。
preferably, in step 12, the method for jointly estimating the channel state information of the target multiple users by using the 2D-SL0 reconstruction algorithm includes:
step 121, inputting an observation signal Y, an observation matrix X and a DFT transformation matrix ARThreshold value σminA contraction factor ρ, a step size μ and an iteration number Q;
step 122, let
Figure BDA0001051769070000035
Step 123, if the sigma is larger than or equal to the sigmaminSequentially executing (I) and (II); otherwise, step 124 is performed.
(I) in a feasible solution set { G | Y ═ XGAROn, from the initial solution
Figure BDA0001051769070000036
The maximization of the objective function is started by the Q-time steepest descent algorithm
Figure BDA0001051769070000037
(a) Setting matrix delta ═ deltaij]Has an element value of
Figure BDA0001051769070000038
(b) Order to
Figure BDA0001051769070000039
Then pass through
Figure BDA00010517690700000310
Will be provided with
Figure BDA00010517690700000311
Projecting onto its feasible solution set;
(II) making σ ← ρ σ, and returning to step 123;
step 124, calculating and outputting
Figure BDA0001051769070000041
Compared with the prior art, the multi-user joint channel estimation method provided by the invention uses a two-dimensional sparse signal reconstruction algorithm 2D-SL0, can greatly reduce the number of pilot frequencies required by channel estimation, and can eliminate interference of pilot frequency pollution. The method is low in calculation complexity and easy to implement.
Additional features and advantages of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart illustrating the steps of a massive MIMO uplink multiuser joint channel estimation method according to the invention;
FIG. 2 is a normalized mean square error comparison diagram of a single cell scenario using a 2D-SL0 compressed sensing channel estimation method (labeled as the channel estimation method provided by the present invention) and a conventional least squares channel estimation method using different numbers of pilots;
fig. 3 is a normalized mean square error curve diagram of the channel estimation method provided by the present invention and the traditional least square channel estimation method using different numbers of pilots in a multi-cell scenario.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
The invention provides an uplink multi-user joint channel estimation method, which comprises the following steps:
step 11, realizing sparse representation of a physical channel matrix through virtual channel representation;
and step 12, modeling the channel estimation of multiple users in the target cell and the interference cell into two-dimensional sparse signal reconstruction in a compressed sensing theory, and jointly estimating the channel state information of the multiple users in the target cell by using a 2D-SL0 reconstruction algorithm.
Compared with the prior art, the multi-user joint channel estimation method provided by the invention uses a two-dimensional sparse signal reconstruction algorithm 2D-SL0, can greatly reduce the number of pilot frequencies required by channel estimation, and can eliminate interference of pilot frequency pollution.
For a better understanding of the contents of the embodiments of the present invention, a system model of the embodiments of the present invention will be described in detail first. Considering a multi-cell massive MIMO system uplink scenario, each target cell shares the same band with L-1 neighboring cells, each cell has a base station with M antennas and K (K < M) single-antenna users.
The uplink channel vector from the kth user in the ith cell to the jth cell base station may be expressed as
Figure BDA0001051769070000051
Wherein g isjikIs a fast fading vector whose elements are independent identically distributed complex Gaussian variables with mean value of 0 and variance of 1, and large-scale fading factor βjikQuasi-static shadowing and path loss are described, so the uplink channel matrix from all K users in the ith cell to the jth cell base station can be represented as
Hji＝GjiDji(formula 2)
Wherein Hji＝[hji1,…,hjiK],Gji＝[gji1,…,gjiK]And
Figure BDA0001051769070000052
in the context of a block fading channel model, the received pilot signal of the jth base station may be written as
Figure BDA0001051769070000053
Wherein
Figure BDA0001051769070000054
Which represents the signal-to-noise ratio of the uplink pilot,
Figure BDA0001051769070000055
the column vector in (ii) represents the pilot signals of the users in the ith cell,
Figure BDA0001051769070000056
representing complex additive white gaussian noise.
The embodiment of the invention discloses a method for estimating uplink multi-user joint channels of a large-scale MIMO system, which mainly comprises the following steps:
step one, virtual sparse representation of a physical channel. The virtual channel representation method describes a physical channel matrix at a series of fixed spatial domain beam angles. Considering a base station employing a uniform linear antenna array, a physical channel matrix G for massive MIMOjiCan be mapped to a virtual channel matrix in the following way
Figure BDA0001051769070000061
Figure BDA0001051769070000062
Wherein the physical channel matrix Gji＝[gji1,…,gjiK]，gjikRepresenting the channel response vector from the kth user in the ith cell to the base station of the jth cell, and receiving a response matrix AR＝[aR(θ1),…,aR(θM)]Where the received response vector is
Figure BDA0001051769070000063
Wherein the direction thetamFrom a physical angle phim∈[-π/2,π/2]Is in a relation ofm＝dsin(φm) And/λ, where λ represents the wavelength and d represents the distance between the antennas. The interval of principal values for which theta is uniformly sampled is a natural choice, i.e. thetamM/M, so that the response matrix a is receivedRIs an M x M dimensional DFT transform array. M x K dimensional matrix
Figure BDA0001051769070000064
Refers to a sparse representation matrix whose element values
Figure BDA0001051769070000065
Indicating the link gain between the kth user and the mth virtual reception angle, and if the link does not exist, the element has a value of 0. If matrix
Figure BDA0001051769070000066
The number of non-zero elements involved is much smaller than the total number of elements in the matrix, i.e.
Figure BDA0001051769070000067
Where f isiIs defined as
Figure BDA0001051769070000068
The number of non-zero elements in the ith column, then
Figure BDA0001051769070000069
Is a sparse matrix.
And step two, modeling the multi-user joint channel estimation as two-dimensional sparse signal reconstruction in a compressed sensing theory. The received pilot signal of the jth cell base station may be represented as
Figure BDA00010517690700000610
Transposing the formula to obtain
Figure BDA0001051769070000071
And thirdly, jointly estimating the channel state information of multiple users by using a 2D-SL0 two-dimensional sparse signal reconstruction algorithm. Linear equation Y ═ XGA according to (formula 7)R+ N, the specific steps of estimating the channel response by using the 2D-SL0 algorithm provided by the invention can be summarized as follows:
Figure BDA0001051769070000072
in order to achieve the above object, the present invention provides an uplink multi-user joint channel estimation method, which includes:
step 11, realizing sparse representation of a physical channel matrix through virtual channel representation;
and step 12, modeling the channel estimation of multiple users in the target cell and the interference cell as two-dimensional sparse signal reconstruction in a compressed sensing theory, and jointly estimating the channel state information of the target multiple users by using a 2D-SL0(two-dimensional smoothed L0 algorithm) reconstruction algorithm.
Preferably, in step 11, the physical channel matrix from all K users in the ith cell to the jth cell base station is sparsely represented as
Figure BDA0001051769070000081
Wherein G isjiIs a physical channel matrix and Gji＝[gji1,…,gjiK]Column vector gjikRepresenting the channel response vector from the kth user in the ith cell to the base station of the jth cell;
ARis a received response matrix and
Figure BDA0001051769070000082
where theta ismM/M, M representing the number of base station antennas, M × K dimensional matrix
Figure BDA0001051769070000083
Is a sparse representation matrix and its element values
Figure BDA0001051769070000084
Indicating the link gain between the kth user and the mth virtual reception angle, and if the link does not exist, the element has a value of 0.
Preferably, in step 12, the method for modeling channel estimation of multiple users in a target cell and an interfering cell as two-dimensional sparse signal reconstruction includes:
the received pilot signal for the jth cell base station may be expressed as:
Figure BDA0001051769070000085
transposing this equation yields the following equation:
Figure BDA0001051769070000086
wherein Hji＝GjiDjiAnd HjiRepresents the channel response matrix from the kth user in the ith cell to the jth cell site, where Hji＝[hji1,…,hjiK]，
Figure BDA0001051769070000091
Figure BDA0001051769070000092
βjikThe large-scale fading factor is represented, K represents the number of single-antenna users, and K is less than M.
The following linear equation can be obtained:
Y＝XGAR+N。
preferably, in step 12, the method for jointly estimating the channel state information of the target multiple users by using the 2D-SL0 reconstruction algorithm includes:
step 121, inputting an observation signal Y, an observation matrix X and a DFT transformation matrix ARThreshold value σminA contraction factor ρ, a step size μ and an iteration number Q;
step 122, let
Figure BDA0001051769070000093
Step 123, if the sigma is larger than or equal to the sigmaminSequentially executing (I) and (II); otherwise, step 124 is performed.
(I) in a feasible solution set { G | Y ═ XGAROn, from the initial solution
Figure BDA0001051769070000094
The maximization of the objective function is started by the Q-time steepest descent algorithm
Figure BDA0001051769070000095
(a) Setting matrix delta ═ deltaij]Has an element value of
Figure BDA0001051769070000096
(b) Order to
Figure BDA0001051769070000097
Then pass through
Figure BDA0001051769070000098
Will be provided with
Figure BDA0001051769070000099
Projecting onto its feasible solution set;
(II) making σ ← ρ σ, and returning to step 123;
step 124, calculating and outputting
Figure BDA00010517690700000910
The invention provides a multi-user joint channel estimation method of a TDD large-scale MIMO system. The provided method adopts a two-dimensional sparse signal reconstruction algorithm 2D-SL0 in the compressive sensing theory, can greatly reduce the number of required pilot frequencies, and can eliminate the influence of pilot frequency pollution. The method is low in calculation complexity and easy to implement.
To verify the effectiveness of the method of the present invention versus the advantages over prior methods, the following simulation comparative tests were performed. The scene system parameters considered are: the same frequency band is used in a single cell or 7 cells, the number of base station antennas is 256, the number of users per cell is 20, and a channel virtual representation matrix
Figure BDA0001051769070000101
Has random 5 elements that are non-zero. Fig. 2 is a normalized mean square error comparison diagram of applying a 2D-SL0 compressed sensing channel estimation method (labeled as the channel estimation method provided by the present invention) in a single-cell scenario and adopting different numbers of pilots in a conventional least square channel estimation method, and it can be seen from the diagram that the channel estimation method provided by the present invention can reduce the number of pilots by 40%. Fig. 3 is a normalized mean square error curve diagram of the channel estimation method provided by the present invention and the traditional least square channel estimation method using different numbers of pilots in a multi-cell scenario, and it can be seen from the diagram that the channel estimation method provided by the present invention can reduce 75% of pilot symbols, and improve the estimation accuracy by eliminating the pilot pollution.
The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications can be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.

Claims (1)

1. A method for uplink multiuser joint channel estimation of a TDD massive MIMO system is characterized by comprising the following steps:
step 11, implementing sparse representation of the physical channel matrix through virtual channel representation:
the method comprises the following steps: the physical channel matrix from all K users in the ith cell to the base station of the jth cell is sparsely expressed as
Figure FDA0002276983330000011
Wherein G isjiIs a physical channel matrix and Gji＝[gji1,…,gjiK]Column vector gjikRepresenting the channel response vector from the kth user in the ith cell to the base station of the jth cell; a. theRIs a received response matrix and AR＝[aR(θ1),…,aR(θM)]，
Figure FDA0002276983330000012
Where theta ismM/M, M representing the number of base station antennas; m x K dimensional matrix
Figure FDA0002276983330000013
Is a sparse representation matrix and its element values
Figure FDA0002276983330000014
Indicating the link gain between the kth user and the mth virtual reception angle, and if the link does not exist, the element value is 0;
step 12, modeling multi-user joint channel estimation of a target cell and an interference cell as two-dimensional sparse signal reconstruction in a compressive sensing theory, and jointly estimating channel state information of target multi-users by using a 2D-SL0(two-dimensional smooth L0 algorithm) reconstruction algorithm;
the method for reconstructing the two-dimensional sparse signal in the compressed sensing theory by modeling the multi-user joint channel estimation of the target cell and the interference cell comprises the following steps:
the received pilot signal for the jth cell base station may be expressed as:
Figure FDA0002276983330000015
transposing this equation yields the following equation:
Figure FDA0002276983330000016
wherein Hji＝GjiDjiAnd HjiRepresents the channel response matrix from the kth user in the ith cell to the jth cell site, where Hji＝[hji1,…,hjiK]，
Figure FDA0002276983330000021
Figure FDA0002276983330000022
βjikThe large-scale fading factor is represented, K represents the number of single-antenna users, and K is less than M;
the following linear equation can be obtained:
Y＝XGAR+N；
the method for jointly estimating the target multi-user channel state information by using the 2D-SL0 reconstruction algorithm comprises the following steps:
step 121, inputting an observation signal Y, an observation matrix X and a receiving response matrix ARThreshold value σminA contraction factor ρ, a step size μ and an iteration number Q;
step 122, let
Figure FDA0002276983330000023
Step 123, if the sigma is larger than or equal to the sigmaminSequentially executing the steps (I) and (II); otherwise, go to step 124;
(I) in a feasible solution set { G | Y ═ XGAROn, from the initial solution
Figure FDA0002276983330000024
The objective function is maximized by starting with Q iterations of the steepest descent algorithm
Figure FDA0002276983330000025
(a) Setting matrix delta ═ deltaij]Has an element value of
Figure FDA0002276983330000026
(b) Order to
Figure FDA0002276983330000027
Then pass through
Figure FDA0002276983330000028
Will be provided with
Figure FDA0002276983330000029
Projecting onto its feasible solution set;
(II) making σ ← ρ σ, and returning to step 123;
step 124, calculating and outputting
Figure FDA00022769833300000210
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* Cited by examiner, † Cited by third party
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* Cited by examiner, † Cited by third party
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WO2012040117A1 (en) * 2010-09-22 2012-03-29 Qualcomm Incorporated Efficient compressed sensing channel estimation for single-carrier communication systems
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