CN106357309B - Linear iterative detection method for massive MIMO based on non-ideal channels - Google Patents
Linear iterative detection method for massive MIMO based on non-ideal channels Download PDFInfo
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- 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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- 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
- H04B7/08—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station
- H04B7/0837—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas at the receiving station using pre-detection combining
- H04B7/0842—Weighted combining
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- H04B7/0854—Joint weighting using error minimizing algorithms, e.g. minimum mean squared error [MMSE], "cross-correlation" or matrix inversion
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Abstract
本发明公开了一种基于非理想信道下大规模MIMO线性迭代检测方法，该方法利用不完全Cholesky分解作为共轭梯度迭代的预处理部分，结合两者的优点，使得在非理想信道或者系统规模扩大情况下传统共轭梯度算法收敛速度降低的问题得以解决。并且，提出了一种以信道相关系数和系统规模为依据的动态阈值作为预处理的重要参数，使得整个系统的求解在复杂度和性能上做到了很好的平衡。
The invention discloses a large-scale MIMO linear iterative detection method based on non-ideal channels. The method uses incomplete Cholesky decomposition as the preprocessing part of conjugate gradient iteration, and combines the advantages of the two, so that in non-ideal channels or system scale The problem that the convergence speed of the traditional conjugate gradient algorithm is reduced in the enlarged case is solved. In addition, a dynamic threshold based on channel correlation coefficient and system scale is proposed as an important parameter of preprocessing, which makes the solution of the whole system achieve a good balance between complexity and performance.
Description
Technical field
The present invention relates to wireless communication technology field, more particularly to one kind are linear based on MIMO extensive under non-ideal communication channel
Iteration detection method.
Background technique
The fast development of wireless communication technique and the rapid proliferation of smart phone, bringing people needs wireless data transmission
The explosive increase asked.To further increase message transmission rate, extensive MIMO is constructed by increasing antenna for base station number
(Multiple-Input Multiple-Output, multiple-input and multiple-output) system is a kind of efficiently relatively convenient and fast mode.
Extensive mimo system energy depth excavates the freedom degree of space dimension, base station is served using same running time-frequency resource multiple
User.2010, AT&T Labs scientist Thomas L.Marzetta proposed the concept of extensive MIMO.Extensive MIMO
Wireless communication configures tens of even hundreds of or more antennas in base station coverage area, matches compared with support in LTE in base station highest
It sets 8 antenna numbers and increases a magnitude or more.And transmitting terminal or the antenna number of receiving end configuration are more, transmission channel is capable of providing
Higher freedom degree is able to achieve better performance on throughput and route are stablized.Because multi-user system can be with simultaneous transmission
Several users are serviced, and more flexible in selection reception particular user scheduling aspect, so this gain is in multi-user system
In it is more considerable.
However, the sharply expansion of antenna scale, greatly increases the complexity of system.Signal detection, as communication system
In essential a part, the problem of being also faced under extensive mimo system complexity increase.Therefore, one kind is found to exist
The detection method traded off between reliability and complexity is very important.In recent mainstream document, it is easy to hardware realization
The linearity test of low complex degree be concerned.Wherein with broken zero detection (zero forcing) and minimum mean-squared error algorithm
(MMSE) most representative.However in high-dimensional system, being faced with the solutions of a M dimensional linear systems, (M is user day
Line number) traditional matrix inversion technique, such as QR decomposition method, Gaussian elimination method and the accurate inversion technique of Cholesky decomposition method,
Its complexity is O (M3) order of magnitude.In scale mimo system, when M becomes larger, complexity will be increased dramatically,
The a large amount of computing resources of system can be expended or increased delay time.At this moment, a kind of to be efficiently based on extensive multiple-input and multiple-output
The matrix inversion technique of MIMO linearity test just becomes extremely important.
On the other hand, in extensive mimo system, base station is configured with a large amount of antennas, and the spatial resolution of MIMO transmission is aobvious
It writes and improves, wireless transmission channel needs deeply systematically to inquire into the letter for being suitable for extensive mimo system there is new characteristic
Road model.And the introducing of the correlation of user terminal and base station end can destroy some advantageous properties of original M dimension matrix, make original
Some detection methods face failure.
Summary of the invention
Goal of the invention: in view of the problems of the existing technology the present invention, provides a kind of based on extensive under non-ideal communication channel
MIMO linear iteraction detection method, the main solution used in conjunction with incomplete Choleskydecomposition and conjugate gradient (CG) algorithm
Framework, by the pretreatment of incomplete decomposing, the conditional number of matrix to be asked reduces, make its iteration convergence rate greatly
Fastly.Because pertaining only to add operation and multiplying by optimizing the algorithm, it is very suitable to realization within hardware, is greatly reduced
Hardware complexity.
Technical solution: of the present invention to include: based on MIMO linear iteraction detection method extensive under non-ideal communication channel
Matrix A is detected according to the corresponding matrix H construction MMSE of the channel of non-ideal communication channel；
Threshold value η is constructed according to detection matrix A；
Preconditioning matrix M is obtained according to threshold value η and detection matrix A；
The receipt signal matrix that received end matched filter is exported using preconditioning matrix MCarry out preconditioned conjugate
Iterative detection obtains transmitting signal matrix estimated value
Further, the corresponding matrix H construction MMSE of the channel according to non-ideal communication channel detects matrix A, specifically includes:
MMSE detection matrix A is constructed according to following formula according to the corresponding matrix H of the channel of non-ideal communication channel:
Α=HHH+δ-1Ι
In formula, δ is the average signal-to-noise ratio of transmitting terminal, and Ι is unit matrix.
Further, described that threshold value η is constructed according to detection matrix A, it specifically includes:
Threshold value η is constructed according to detection matrix A using following formula:
η=ε (1-K/N) Aii
In formula, ε is adjustable constant, and K is user terminal antenna number, and N is base station end antenna number, AiiFor i-th for detecting matrix A
Diagonal entry.
Further, described that preconditioning matrix M is obtained according to threshold value η and detection matrix A, it specifically includes:
Preconditioning matrix M is defined as M=L-1L-T；Wherein, if element value is less than threshold value η in detection matrix A, under
Corresponding position element sets 0 in triangular matrix L, if the element is greater than threshold value η, the i-th row jth column element is in triangular matrix LAijFor the i-th row jth column element for detecting matrix A.
Further, the receipt signal matrix that received end matched filter is exported using preconditioning matrix M
Preconditioned conjugate iterative detection is carried out, is specifically included:
(a) it is initialized: s0=0,p0=z0, wherein L is lower three angular moment of M
Battle array；
(b) the number of iterations j=1 is set；
(c) it is calculated according to following formula:
(d) by j=j+1, and it is back to (c), until iterating to preset times m, then smEstimate for transmitting signal matrix
Evaluation
Although wherein zj+1=(LLT)-1rj+1It is related to a linear system solution, but since it can be write as cam system
Form:qj+1For intermediate variable, therefore this step computation complexity is still controlled in O (N2)。
The utility model has the advantages that compared with prior art, the present invention its remarkable advantage is: 1, the present invention is reduced by pretreatment
Because of the conditional number that matrix properties deteriorate and rise, it can preferably solve to expand in matrix size or channel relevancy enhances
In the case where, defect that the convergence rate of iterative algorithm slows down；2, the present invention is in view of channel correlation coefficient and antenna number
On the basis of propose a kind of adaptive threshold, Lai Jinhang incomplete decomposing pretreatment, due to the preprocess method of use, so that every
The complexity of solution linear equation in single-step iteration is controlled in O (N2), it is a kind of method for taking into account complexity and reliability.
Detailed description of the invention
Fig. 1 is flow diagram of the invention；
Fig. 2 is present invention figure compared with traditional conjugate gradient algorithms are in the ber curve under different state of signal-to-noise；
Fig. 3 is the bit error rate of the present invention with traditional conjugate gradient algorithms in fixed number of users different base station antenna number
Curve compares figure；
Fig. 4 is that the complexity that the present invention decomposes accurate inversion technique with traditional conjugate gradient algorithms and Cholskey compares
Figure.
Specific embodiment
In extensive mimo system, generally there is N > > K (antenna for base station number N is much larger than number of users K).S is allowed to indicate K × 1
The signal vector of rank, s contain the transmission symbol generated from K user.Expression channel response matrix, therefore base station end
Received signal vector can be expressed as
Y=Hs+n
Wherein n is the additive white Gaussian noise vector of a N × 1 dimension, and element is obeyed
The multiuser signal detection task of base station is exactly from the plus noise signal vector y estimation transmission signal code received
s.H can be obtained by time domain or pilot tone.Using least mean-square error (MMSE) linearity test theory, to receive signal to
Amount is expressed as
WhereinIt is output of the received vector y in receiving end matched filter.As can be seen that estimating to transmission signal vector
EvaluationIt may be expressed as:In order to solveUsing the iteration detection method of the present embodiment.
As shown in Figure 1, the present embodiment based on MIMO linear iteraction detection method extensive under non-ideal communication channel include with
Lower step:
S1, matrix A is detected according to the corresponding matrix H construction MMSE of channel of non-ideal communication channel.
Wherein, MMSE detects matrix A are as follows: Α=HHH+δ-1Ι, in formula, δ is the average signal-to-noise ratio of transmitting terminal, and Ι is unit square
Battle array.
S2, threshold value η is constructed according to detection matrix A.
Wherein, η=ε (1-K/N) Aii, in formula, ε is adjustable constant, and K is user terminal antenna number, and N is base station end antenna number,
AiiFor i-th of diagonal entry for detecting matrix A.
S3, preconditioning matrix M is obtained according to threshold value η and detection matrix A.
Specifically, the step specifically includes: preconditioning matrix M is defined as M=L-1L-T；Wherein, if detection matrix A
Middle element value is less than threshold value η, then corresponding position element sets 0 in lower triangular matrix L, if the element is greater than threshold value η, triangular matrix
The i-th row jth column element is in LAijFor the i-th row jth column element for detecting matrix A.
S4, the receipt signal matrix that received end matched filter is exported using preconditioning matrix MIt is pre-processed
It is conjugated iterative detection, obtains transmitting signal matrix estimated value
Specifically, the step includes；
(a) it is initialized: s0=0,p0=z0, wherein L is lower three angular moment of M
Battle array；
(b) the number of iterations j=1 is set；
(c) it is calculated according to following formula:
(d) by j=j+1, and it is back to (c), until iterating to preset times m, then smEstimate for transmitting signal matrix
Evaluation
The method reduces because of the conditional number that matrix properties deteriorate and rise by pretreatment, sees Fig. 3, can be preferable
Solution matrix size expand or channel relevancy enhance in the case where, the defect that the convergence rate of iterative algorithm slows down,
See Fig. 2.
In addition, this method proposes a kind of adaptive threshold on the basis of considering channel correlation coefficient and antenna number, come
Incomplete decomposing pretreatment is carried out, due to the preprocess method of use, so that the solution linear equation in every single-step iteration
Complexity is controlled in O (N2).Table 1 lists the algorithm complexity of its each step, complexity due to threshold value setting and set at 0
Reason, so that each step has S element to be not involved in actual operation, therefore algorithm complexity is not significantly increased, and sees Fig. 4.
Table 1
Claims (2)
1. one kind is based on MIMO linear iteraction detection method extensive under non-ideal communication channel, it is characterised in that this method comprises:
MMSE detection matrix A: Α=Η is constructed according to following formula according to the corresponding matrix H of the channel of non-ideal communication channelHΗ+δ-1
Ι, in formula, δ is the average signal-to-noise ratio of transmitting terminal, and Ι is unit matrix；
η: η=ε of threshold value (1-K/N) A is constructed according to detection matrix A using following formulaii, in formula, ε is adjustable constant, and K is to use
Family end antenna number, N are base station end antenna number, AiiFor i-th of diagonal entry for detecting matrix A；
Preconditioning matrix M is obtained according to threshold value η and detection matrix A；It specifically includes: preconditioning matrix M is defined as M=L-1L-T；
Wherein, if element value is less than threshold value η in detection matrix A, corresponding position element sets 0 in lower triangular matrix L, if the element is big
In threshold value η, then the i-th row jth column element is in triangular matrix LAijIt is the i-th of detection matrix A
Row jth column element；
The receipt signal matrix that received end matched filter is exported using preconditioning matrix MCarry out preconditioned conjugate iteration
Detection obtains transmitting signal matrix estimated value
2. according to claim 1 based on MIMO linear iteraction detection method extensive under non-ideal communication channel, feature exists
In: the receipt signal matrix that received end matched filter is exported using preconditioning matrix MCarry out preconditioned conjugate
Iterative detection specifically includes:
(a) it is initialized: s0=0,p0=z0, wherein L is the lower triangular matrix of M；
(b) the number of iterations j=1 is set；
(c) it is calculated according to following formula:
(d) by j=j+1, and it is back to (c), until iterating to preset times m, then smTo emit signal matrix estimated value
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| CN110336594B (en) * | 2019-06-17 | 2020-11-24 | 浙江大学 | A Deep Learning Signal Detection Method Based on Conjugate Gradient Descent |
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