CN105915477A - Large-scale MIMO detection method based on GS method, and hardware configuration - Google Patents
Large-scale MIMO detection method based on GS method, and hardware configuration Download PDFInfo
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- CN105915477A CN105915477A CN201610243953.XA CN201610243953A CN105915477A CN 105915477 A CN105915477 A CN 105915477A CN 201610243953 A CN201610243953 A CN 201610243953A CN 105915477 A CN105915477 A CN 105915477A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- 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
- H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
- H04L25/03891—Spatial equalizers
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
- H04L1/0045—Arrangements at the receiver end
- H04L1/0047—Decoding adapted to other signal detection operation
- H04L1/005—Iterative decoding, including iteration between signal detection and decoding operation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L1/00—Arrangements for detecting or preventing errors in the information received
- H04L1/004—Arrangements for detecting or preventing errors in the information received by using forward error control
- H04L1/0045—Arrangements at the receiver end
- H04L1/0047—Decoding adapted to other signal detection operation
- H04L1/005—Iterative decoding, including iteration between signal detection and decoding operation
- H04L1/0051—Stopping criteria
Abstract
The invention discloses a large-scale MIMO detection method based on a GS method, and a hardware configuration. The method comprises the steps that a channel matrix and a receiving signal vector pass through a preprocessing module to respectively enter a Neumann series expansion module and a GS method module; the preprocessing module carries out the Gram matrix calculation, MMSE filtering matrix calculation and matching filtering; the Neumann series expansion module obtains an MMSE filtering matrix, carries out approximate inversion of two items, carries out the multiplication of the MMSE filtering matrix with matching filtering output, and obtaining an initial solution of the GS iterative method; the GS method module obtains the MMSE filtering matrix and the matching filtering output as a coefficient matrix and a constant vector for GS iteration for solving a linear equation set, wherein the output of each iteration is stored in a register as a signal detection result. The method is lower in algorithm complexity, is smaller in number of iteration times, is smaller in hardware consumption, and greatly improves the throughput rate.
Description
Technical field
The invention belongs to computer communication field, relate to a kind of extensive mimo system uplink signal inspection
Survey method and hardware structure.
Background technology
Extensive MIMO (Large-scale Multiple-Input Multiple-Output or Massive MIMO)
System is proposed by research worker such as AT&T Labs of U.S. Thomas L.Marzetta the earliest.Research finds, when
When the antenna for base station number of community tends to infinite, the negative effect such as additive white Gaussian noise and Rayleigh fading all may be used
To ignore, message transmission rate can be greatly improved.In extensive mimo system, base station is joined
Putting substantial amounts of antenna, number of antennas generally has tens, hundreds of the most thousand of, is existing mimo system sky
More than 1～2 order of magnitude of line number, and the subscriber equipment that base station is serviced (User Equipment, UE) number
Mesh is far fewer than antenna for base station number；Base station utilizes same running time-frequency resource to service several UE simultaneously, fully sends out
The spatial degrees of freedom of pick system.
Although extensive MIMO has superior performance, but the huge amplification of antenna magnitude brings calculating
The index of complexity rises.Have plurality of articles at present and propose extensive MIMO uplink signal detection
Algorithm and framework, its main computation complexity is inverting of a K × kth moment battle array, and wherein K is for using
Amount.Accurate matrix inversion technique, if Cholesky decomposition method complexity is O (K3).So when the number of K
When measuring very big, such inversion approach brings huge computation complexity and hardware consumption.
In recent years, the research worker such as Linglong Dai proposed based on Gauss-Seidel (GS) method soft defeated
Going out detection algorithm, this algorithm mainly uses GS alternative manner to solve system of linear equations, thus avoids complexity
Higher matrix is accurately inverted, and required amount of calculation is (i+1) K2+ 4K, wherein i is iterations.But he
Method convergence rate fast not, and do not provide the particular hardware of detection algorithm based on GS method
Framework.
Summary of the invention
Goal of the invention: for deficiency of the prior art, the present invention proposes that a kind of complexity is low, the big rule of high efficiency
Mould MIMO linearity test method and hardware structure.
Technical scheme: the present invention proposes a kind of extensive MIMO linearity test method based on GS method,
Comprise the following steps:
Step 1: by channel matrix H with reception signal y through pretreatment module, obtain the output of matched filtering device
yMF=HHY and MMSE filtering matrix W=G+NoIK, wherein Gram matrix G=HHH, NoFor noise
Variance, IkFor unit battle array, (.)HFor conjugate transposition operation, note W=D+L+LH, wherein D is diagonal matrix,
L is inferior triangular flap；
Step 2: by matrix D and LHInput Neumann series expansion unit, obtains 2 of matrix W
Approximation inverse matrixNon-diagonal battle array during wherein E is W, and change for GS
The initial solution in generation
Step 3: by yMF,D,LHAnd s0Input GS method module, is iterated solving, and ith iteration is defeated
Go out for si=(D+L)-1(yMF-LHsi-1), i=1,2 ..., it is the estimated result of signal to be detected.
Further, the iterations i in described step 3 is 1～4 time.
The present invention also proposes the hardware structure of a kind of extensive MIMO linearity test based on GS method, bag
Include pretreatment module, Neumann series expansion module and GS method module；Wherein, described pretreatment module
For calculating Gram matrix and matched filtering, including the matched filtering device array being made up of systolic arrays, Gram
Matrix calculus array；Described Neumann series expansion module is used for taking MMSE filtering matrix and carries out 2 closely
Seemingly invert and be multiplied with matched filtering output and obtain GS alternative manner initial solution, including one based on look-up table
(LUT) reciprocal unit, a vector addition array and three kinds of different multiplier array mul1, mul2,
mul3；Described GS method unit is used for taking MMSE filtering matrix and matched filtering exports respectively as coefficient
Matrix and constant vector carry out GS iterative system of linear equations, and the output of each iteration are stored in depositor,
It is signal detecting result, including a systolic arrays inv solving triangle battle array inverse matrix, two Matrix-Vector
Multiplication array, a vector addition array and one group of depositor, write is deposited by the signal eventually passing through detection
In device, it is simple to the decoding etc. carrying out next step operates.
Further, described multiplier array mul1 comprises 2K multiplier, and wherein K is number of users.
Further, described multiplier array mul2 comprises 4K multiplier, 2K adder.
Further, described ripple multiplier array mul3 comprises 2K depositor, 4K multiplier and 4K
Individual adder.
Further, the described systolic arrays inv for solving triangle battle array inverse matrix uses 2 kinds of processing units (PE),
Comprise 6K depositor, 4K multiplier and 4K adder, and 1 reciprocal unit.
During the present invention detects in view of extensive mimo system up-link MMSE, filtering matrix W is
Hermitian positively definite matrix, uses GS alternative manner to solve system of linear equations basic as whole detection algorithm, has
Effect avoids the matrix inversion process that complexity is high, is especially suitable for realization within hardware, greatly reduces hardware
Complexity, concrete, the complex multiplication number of times needed for algorithm is (i+3) K2, i is the least, and therefore algorithm is multiple
Miscellaneous degree is O (K2).On the other hand, owing to the convergence rate of alternative manner is had a significant impact by initial solution,
The present invention uses the output of Neumann progression approximation inverse matrix and matched filtering device to be multiplied to obtain being similar to accurately solve
Initial solution, thus significantly improve alternative manner convergence rate.
Beneficial effect: compared with prior art, emphasis of the present invention considers computation complexity and algorithm performance, and
And the hardware complexity of the present invention is relatively low；Meanwhile, iterative computation can obtain the accuracy of arbitrary accuracy, iteration
The change of number of times is flexible, provide more preferable motility for the occasion that performance requirement is different, and now degree of accuracy
Adjust the most relevant with iterations, i.e. only have certain relation with handling capacity size, have no effect on hardware architecture.This
Invention also substantially increases throughput.
Accompanying drawing explanation
Fig. 1 is the hardware structure schematic diagram of based on GS method the extensive MIMO detection algorithm of the present invention；
Fig. 2 is that the Neumann series expansion unit of the present invention is to the structural representation of magnitude；
Fig. 3 is the systolic arrays inv structural representation for solving triangle battle array inverse matrix of the present invention；
Fig. 4 for launch antenna number be 8, when reception antenna number is 128, signal detection algorithm of the present invention and other
The ber curve comparison diagram of detection algorithm；
Fig. 5 for launch antenna number be 16, when reception antenna number is 128, signal detection algorithm of the present invention and its
The ber curve comparison diagram of his detection algorithm；
Fig. 6 is to use the GS alternative manner of initial solution of the present invention and the GS alternative manner of other initial solutions to examine
Survey the ber curve comparison diagram of signal.
Detailed description of the invention
Below in conjunction with being embodied as case, it is further elucidated with the present invention, it should be understood that these embodiments are merely to illustrate
The present invention rather than restriction the scope of the present invention, after having read the present invention, those skilled in the art are to this
The amendment of the various equivalent form of values of invention all falls within the application claims limited range.
The present embodiment is set up an extensive mimo channel model and is simulated operation.At extensive MIMO
In system, typically have N > > K (antenna for base station number N is much larger than launching antenna number, i.e. number of users K).First
The parallel transmission bit stream of K different user generation is encoded by chnnel coding respectively, is then mapped to star
Seat symbol, and take planisphere set energy normalized.If s=is [s1,s2,s3,…,sk]TRepresent signal vector, s
In contain the transmission symbol produced respectively from K user, use 16-QAM mode to map.H represents dimension
Degree is N × K channel matrix, therefore the received signal vector y of uplink base station end can be expressed as:
Y=Hs+n
Wherein, the dimension of y is N × 1, and n is the additive white noise vector of N × 1 dimension, and its element obeys zero-mean variance
For NoGauss distribution.Uplink multiuser signal detection task is exactly to receive vector from receiver
Y=[y1,y2,y3,…,yN]TEstimate transmission signal code s.
Assume H it is known that its element obedience average is 0 variance is the independent same distribution of 1, use lowest mean square
Error (MMSE) linearity test is theoretical, and the estimation to transmission signal vectors is expressed as:
Wherein, matrix W is Hermitian positive definite, and W=D+L+LH, wherein D, L and LHIt is respectively W
Diagonal, triangular component on lower trigonometric sum.
System of linear equations is solved according to GS method:
The testing result of ith iteration gained is:
si=(D+L)-1(yMF-LHsi-1), i=1,2 ...,
Obviously initial solution s of iteration0Final rate of convergence there is considerable influence, in order to make initial solution sufficiently close together
Accurately solving but do not improve algorithm complex simultaneously, we use the inverse square of the Neumann progression approximate W of 2
Battle array, thus obtain proposed initial solution:
s0=W2 -1yMF
Wherein,E is the non-diagonal component in W.
For the extensive mimo system that antenna configurations is 128 × 8 and 128 × 16, use 3/4 speed
Turbo code and 16-QAM map, the emulation of described extensive MIMO detection algorithm based on GS method
Result is shown in Fig. 4, Fig. 5 and Fig. 6.Fig. 4 is 8 for launching antenna number, when reception antenna number is 128, this
The ber curve comparison diagram of clear signal detection algorithm and other detection algorithms, result from figure it can be seen that
Signal detection algorithm of the present invention (being labeled as GS) is that performance when 1 has been better than tradition at iterations
Neumann progression approximation inversion algorithms (being labeled as Neumann) is 3 (corresponding to iteration time launching item number
Number is 3) time performance；Signal detection algorithm of the present invention iterations be performance when 2 closely based on
The accurate inversion algorithms (being labeled as MMSE) that Cholesky decomposes, it is shown that GS algorithm is in iteration speed side
The superiority in face.Similarly, Fig. 5 is 16 for launching antenna number, when reception antenna number is 128, and the present invention
Signal detection algorithm and the ber curve comparison diagram of other detection algorithms, it can be observed that now GS algorithm exists
Iterations be performance when 1 and Neumann algorithm at the similar nature that iterations is when 4, but at letter
Make an uproar more inconspicuous than restraining during more than 11dB；But GS algorithm is that performance when 2 is in close proximity at iterations
Accurate Cholesky algorithm.Fig. 6 is GS alternative manner and other initial solutions using initial solution of the present invention
GS alternative manner detects the ber curve comparison diagram of signal, it can be seen that in the feelings not calculating initial solution
(i.e. s under condition0Be 0), traditional GS algorithm when iterations smaller (such as 1 and 2), Detection results
The most undesirable；In the GS algorithm proposed by Linglong Dai et al., the initial solution of iteration is arranged to
s0=D-1Y, now Detection results obtains certain lifting；And use the GS alternative manner of initial solution of the present invention to exist
Iterations is that to be equivalent to the GS method that initial solution is 0 be performance when 2 at iterations to performance when 1,
And the Detection results that the GS alternative manner of initial solution of the present invention is in the case of identical iterations is always better than
The GS algorithm that above-mentioned Linglong Dai et al. proposes, this embodies detection algorithm of the present invention in convergence rate side
Face is better than current existing GS method.
Hardware structure aspect, it is hard that based on GS method the extensive MIMO used in the present embodiment detects
Part configuration diagram is shown in Fig. 1, including pretreatment module, Neumann series expansion module (Fig. 2) and
GS method module.
Specifically, in pretreatment module, comprise:
1) matched filter module: the systolic arrays being made up of K complex multiplier accumulator (MAC), uses
In calculating yMF=HHy；
2) Gram matrix calculus module: the lower triangle systolic arrays being made up of (1+K) K/2 MAC, uses
In calculating G=HHH。
In Neumann series expansion module, comprise:
1) multiplier array mul1: comprise 2K multiplier；
2) multiplier array mul2: comprise 4K multiplier, 2K adder, associating mul1 by based on
Calculation-D-1ED-1；
3) multiplier array mul3: comprise 2K depositor, 4K multiplier and 4K adder, uses
In calculating
In GS method module, comprise:
1) Matrix-Vector multiplication array: the systolic arrays being made up of K MAC；
2) Fig. 3 is shown in by systolic arrays inv: the structure chart solving triangle battle array inverse matrix, uses 2 kinds of PE, comprises
6K depositor, 4K multiplier and 4K adder, and 1 reciprocal unit, be used for calculating down three
The inverse matrix of angle battle array (D+L).
Claims (7)
1. an extensive MIMO linearity test method based on GS method, it is characterised in that include as
Lower step:
Step 1: by channel matrix H with reception signal y through pretreatment module, obtain the output of matched filtering device
yMF=HHY and MMSE filtering matrix W=G+NoIK, wherein Gram matrix G=HHH, NoFor noise
Variance, IKFor unit battle array, (.)HFor conjugate transposition operation, note W=D+L+LH, wherein D is diagonal matrix,
L is inferior triangular flap；
Step 2: by matrix D and LHInput Neumann series expansion unit, obtains 2 of matrix W
Approximation inverse matrixNon-diagonal battle array during wherein E is W, and change for GS
The initial solution in generation
Step 3: by yMF,D,LHAnd s0Input GS method module, is iterated solving, and ith iteration is defeated
Go out for si=(D+L)-1(yMF-LHsi-1), i=1,2 ..., it is the estimated result of signal to be detected.
Extensive MIMO linearity test method based on GS method the most according to claim 1, its
Being characterised by, the iterations i in described step 3 is 1～4 time.
3. the hardware structure of an extensive MIMO linearity test based on GS method, it is characterised in that:
Including pretreatment module, Neumann series expansion module and GS method module；
Wherein, described pretreatment module is used for calculating Gram matrix and matched filtering, including by systolic arrays structure
The matched filtering device array of one-tenth, Gram matrix calculus array；
Described Neumann series expansion module be used for taking MMSE filtering matrix carry out 2 approximations invert and with
Matched filtering output is multiplied and obtains GS alternative manner initial solution, including an inverse based on look-up table (LUT)
Unit, a vector addition array and three kinds of different multiplier array mul1, mul2, mul3；
Described GS method unit is used for taking MMSE filtering matrix and matched filtering exports respectively as coefficient square
Battle array and constant vector carry out GS iterative system of linear equations, and the output of each iteration are stored in depositor,
It is signal detecting result, including a systolic arrays inv solving triangle battle array inverse matrix, two Matrix-Vector
Multiplication array, a vector addition array and one group of depositor, write is deposited by the signal eventually passing through detection
In device, it is simple to the decoding etc. carrying out next step operates.
The hardware frame of extensive MIMO linearity test based on GS method the most according to claim 3
Structure, it is characterised in that: described multiplier array mul1 comprises 2K multiplier, and wherein K is number of users.
The hardware frame of extensive MIMO linearity test based on GS method the most according to claim 3
Structure, it is characterised in that: described multiplier array mul2 comprises 4K multiplier, 2K adder, wherein
K is number of users.
The hardware frame of extensive MIMO linearity test based on GS method the most according to claim 3
Structure, it is characterised in that: described ripple multiplier array mul3 comprise 2K depositor, 4K multiplier and
4K adder, wherein K is number of users.
The hardware frame of extensive MIMO linearity test based on GS method the most according to claim 3
Structure, it is characterised in that: the described systolic arrays inv for solving triangle battle array inverse matrix uses 2 kinds of processing units
(PE), 6K depositor, 4K multiplier and 4K adder, and 1 reciprocal unit are comprised, its
Middle K is number of users.
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| CN106850017A (en) * | 2017-03-06 | 2017-06-13 | 东南大学 | Extensive MIMO detection algorithms and hardware structure based on parallel GS iteration |
| CN107222246A (en) * | 2017-05-27 | 2017-09-29 | 东南大学 | The efficient extensive MIMO detection method and system of a kind of approximated MMSE-based performance |
| CN108540184A (en) * | 2018-04-11 | 2018-09-14 | 南京大学 | A kind of extensive antenna system signal detecting method and its hardware structure of optimization |
| CN109525296A (en) * | 2018-10-17 | 2019-03-26 | 东南大学 | Extensive MIMO detection method and device based on self-adaptive damping Jacobi iteration |
| CN109981151A (en) * | 2019-04-10 | 2019-07-05 | 重庆邮电大学 | Improved Gauss tree approximation message transmission detection algorithm in extensive mimo system |
| CN110753012A (en) * | 2019-11-25 | 2020-02-04 | 重庆邮电大学 | Multi-user detection algorithm of time reversal multiple access system |
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| CN108540184A (en) * | 2018-04-11 | 2018-09-14 | 南京大学 | A kind of extensive antenna system signal detecting method and its hardware structure of optimization |
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| CN110753012A (en) * | 2019-11-25 | 2020-02-04 | 重庆邮电大学 | Multi-user detection algorithm of time reversal multiple access system |
| CN111162828A (en) * | 2019-12-12 | 2020-05-15 | 重庆邮电大学 | Low-complexity signal detection method of large-scale MIMO system |
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