CN105915477B - Extensive MIMO detection method and hardware structure based on GS method - Google Patents
Extensive MIMO detection method and hardware structure based on GS method Download PDFInfo
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- CN105915477B CN105915477B CN201610243953.XA CN201610243953A CN105915477B CN 105915477 B CN105915477 B CN 105915477B CN 201610243953 A CN201610243953 A CN 201610243953A CN 105915477 B CN105915477 B CN 105915477B
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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 kind of extensive MIMO detection method and hardware structure based on GS method, channel matrix and received signal vector respectively enter Neumann series expansion module and GS method module after preprocessing module；Wherein, preprocessing module carries out the calculating of Gram matrix, the calculating of MMSE filtering matrix and matched filtering；Neumann series expansion module takes MMSE filtering matrix to carry out 2 approximations and inverts and multiplied to GS alternative manner initial solution with matched filtering output phase；GS method module takes MMSE filtering matrix and matched filtering to export and carries out GS iterative solution system of linear equations respectively as coefficient matrix and constant vector, and each iteration output is stored in register, as signal detecting result.Inventive algorithm complexity is lower, and required the number of iterations is less, and hardware consumption is less, while greatly improving throughput.
Description
Technical field
The invention belongs to computer communication field, be related to a kind of extensive mimo system uplink signal detection method and
Hardware structure.
Background technique
Extensive MIMO (Large-scale Multiple-Input Multiple-Output or Massive MIMO)
System is proposed by researchers such as AT&T Labs, U.S. Thomas L.Marzetta earliest.The study found that working as the base station of cell
When number of antennas tends to be infinite, the negative effects such as additive white Gaussian noise and Rayleigh fading all be can be ignored, and data pass
Defeated rate can be greatly improved.In extensive mimo system, base station configures a large amount of antenna, and number of antennas usually has several
Ten, several hundred or even thousands of, be 1~2 order of magnitude of existing mimo system number of antennas or more, and the user that base station is serviced
Equipment (User Equipment, UE) number is far fewer than antenna for base station number；Base station is serviced simultaneously using the same running time-frequency resource
Several UE sufficiently excavate the spatial degrees of freedom of system.
Although extensive MIMO has superior performance, the huge amplification of antenna magnitude brings computation complexity
Index rises.Existing plurality of articles propose the algorithm and framework of extensive MIMO uplink signal detection at present, main
Computation complexity be that a K × kth moment battle array is inverted, wherein K be number of users.Accurate matrix inversion technique, such as
Cholesky decomposition method complexity is O (K3).So when the high number of K, such inversion approach brings huge meter
Calculate complexity and hardware consumption.
In recent years, the researchers such as Linglong Dai proposed the inspection of the soft output based on Gauss-Seidel (GS) method
Method of determining and calculating, which mainly uses GS alternative manner to solve system of linear equations, accurate so as to avoid the higher matrix of complexity
It inverts, required calculation amount is (i+1) K2+ 4K, wherein i is the number of iterations.But their method convergence rate is not fast enough, and
And do not provide the particular hardware framework of the detection algorithm based on GS method.
Summary of the invention
Goal of the invention: aiming at the shortcomings in the prior art, the present invention proposes that a kind of complexity is low, the extensive MIMO of high efficiency
Linearity test method and hardware structure.
Technical solution: the present invention proposes a kind of extensive MIMO linearity test method based on GS method, including following step
It is rapid:
Step 1: by channel matrix H and receiving signal y by preprocessing module, obtain matched filter output yMF=HHy
With MMSE filtering matrix W=G+NoIK, wherein Gram matrix G=HHH, NoFor noise variance, IkFor unit battle array, ()HTurn for conjugation
Operation is set, pays attention to W=D+L+LH, wherein D is diagonal matrix, and L is inferior triangular flap；
Step 2: by matrix D and LHNeumann series expansion unit is inputted, 2 approximation inverse matrixes of matrix W are obtainedWherein E is the non-diagonal battle array in W, and the initial solution for GS iteration
Step 3: by yMF,D,LHAnd s0GS method module is inputted, solution is iterated, i-th iteration output is si=(D+
L)-1(yMF-LHsi-1), i=1,2 ..., the estimated result of signal as to be detected.
Further, the number of iterations i in the step 3 is 1~4 time.
The present invention also proposes a kind of hardware structure of extensive MIMO linearity test based on GS method, including pretreatment mould
Block, Neumann series expansion module and GS method module；Wherein, the preprocessing module is for calculating Gram matrix and matching
Filtering, including matched filter array, the Gram matrix computing array being made of systolic arrays；The Neumann series expansion
Module for take MMSE filtering matrix carry out 2 approximations invert and with matched filtering output phase it is multiplied to GS alternative manner it is initial
Solution is based on reciprocal unit, a vectorial addition array and three kinds of different multiplier arrays of look-up table (LUT) including one
mul1,mul2,mul3；The GS method unit is for taking MMSE filtering matrix and matched filtering to export respectively as coefficient matrix
GS is carried out with constant vector and iteratively solves system of linear equations, and each iteration is exported in deposit register, as signal detection
As a result, including the systolic arrays inv of a solution triangle battle array inverse matrix, two Matrix-Vector multiplication arrays, a vectorial addition
Array and one group of register, the signal for eventually passing through detection will be written in register, be convenient for the behaviour such as decoding of next step
Make.
Further, the multiplier array mul1 includes 2K multiplier, and wherein K is number of users.
Further, the multiplier array mul2 includes 4K multiplier, 2K adder.
Further, the ripple multiplier array mul3 includes 2K register, 4K multiplier and 4K adder.
Further, the systolic arrays inv for solving triangle battle array inverse matrix includes using 2 kinds of processing units (PE)
6K register, 4K multiplier and 4K adder and 1 reciprocal unit.
The present invention considers that filtering matrix W is Hermitian positive definite in extensive mimo system uplink MMSE detection
Battle array solves system of linear equations detection algorithm basis as a whole using GS alternative manner, effectively prevents the high matrix of complexity
Inversion process is very suitable to realization within hardware, greatly reduces hardware complexity, specifically, complex multiplication needed for algorithm
Method number is (i+3) K2, i is usually smaller, therefore algorithm complexity is O (K2).On the other hand, since initial solution is to alternative manner
Convergence rate have a significant impact, the present invention is exported using Neumann series approximation inverse matrix and matched filter to be multiplied
The initial solution for being similar to accurately solve is obtained, to significantly improve alternative manner convergence rate.
The utility model has the advantages that compared with prior art, emphasis of the present invention considers computation complexity and algorithm performance, and this hair
Bright hardware complexity is lower；Meanwhile the accuracy of available arbitrary accuracy is iterated to calculate, the change of the number of iterations is flexible,
Better flexibility is provided for the different occasion of performance requirement, and the adjustment of accuracy at this time is only related with the number of iterations, i.e.,
Only there is certain relationship with handling capacity size, has no effect on hardware architecture.The present invention also substantially increases throughput.
Detailed description of the invention
Fig. 1 is the hardware structure schematic diagram of the extensive MIMO detection algorithm of the invention based on GS method；
Fig. 2 is the structural schematic diagram of Neumann series expansion unit vector grade of the invention；
Fig. 3 is of the invention for solving the systolic arrays inv structural schematic diagram of triangle battle array inverse matrix；
Fig. 4 is that transmitting antenna number is 8, when receiving antenna number is 128, signal detection algorithm of the present invention and other detection algorithms
Ber curve comparison diagram；
Fig. 5 is that transmitting antenna number is 16, and when receiving antenna number is 128, signal detection algorithm of the present invention and other detections are calculated
The ber curve comparison diagram of method；
Fig. 6 is to detect signal using the GS alternative manner of GS alternative manner and other initial solutions of initial solution of the present invention
Ber curve comparison diagram.
Specific embodiment
Below with reference to specific implementation case, the present invention is furture elucidated, it should be understood that these embodiments are merely to illustrate this hair
Bright rather than limit the scope of the invention, after the present invention has been read, those skilled in the art are to of the invention various etc.
The modification of valence form falls within the application range as defined in the appended claims.
An extensive mimo channel model is established in the present embodiment carries out simulated operation.In extensive mimo system,
Generally there is N > > K (antenna for base station number N is much larger than transmitting antenna number, i.e. number of users K).The parallel biography that K different user first generates
Defeated bit stream passes through channel coding respectively and is encoded, and is then mapped to constellation symbol, and takes planisphere set energy normalizing
Change.If s=[s1,s2,s3,…,sk]TIt indicates signal vector, the transmission symbol generated respectively from K user is contained in s, use
16-QAM mode maps.H indicates that dimension is N × K channel matrix, therefore the received signal vector y at uplink base station end can be with table
It is shown as:
Y=Hs+n
Wherein, the dimension of y is N × 1, and n is the additive white noise vector that N × 1 is tieed up, and it is N that element, which obeys zero-mean variance,o
Gaussian Profile.Uplink multiuser signal detection task is exactly from receiver received vector y=[y1,y2,y3,…,yN]TEstimate
Meter transmission signal code s.
Assuming that H is it is known that it is the independent same distribution that 0 variance is 1 that its element, which obeys mean value, using least mean-square error (MMSE)
Linearity test is theoretical, indicates the estimation of transmission signal vectors are as follows:
Wherein, matrix W is Hermitian positive definite, and W=D+L+LH, wherein D, L and LHThe respectively diagonal line of W, lower three
Angle and upper triangular component.
System of linear equations is solved according to GS method:
The resulting testing result of i-th iteration are as follows:
si=(D+L)-1(yMF-LHsi-1), i=1,2 ...,
The initial solution s of obvious iteration0There is larger impact to final rate of convergence, in order to keep initial solution close enough accurate
Algorithm complexity is solved but does not improve simultaneously, we use the inverse matrix of 2 Neumann series approximate Ws, to obtain being proposed
Initial solution:
s0=W2 -1yMF
Wherein,E is the non-diagonal component in W.
The extensive mimo system for being 128 × 8 and 128 × 16 for antenna configuration, using 3/4 rate Turbo code and
16-QAM mapping, the simulation result of the extensive MIMO detection algorithm based on GS method are shown in Fig. 4, Fig. 5 and Fig. 6.Fig. 4 is hair
Penetrating antenna number is 8, when receiving antenna number is 128, the ber curve pair of signal detection algorithm of the present invention and other detection algorithms
Than figure, the result of Cong Tuzhong can be seen that performance of the signal detection algorithm of the present invention (labeled as GS) when the number of iterations is 1
Through (corresponding to the number of iterations for 3 in expansion item number better than tradition Neumann series approximation inversion algorithms (being labeled as Neumann)
For 3) when performance；The very close Cholesky that is based on of performance of the signal detection algorithm of the present invention when the number of iterations is 2 is decomposed
Accurate inversion algorithms (be labeled as MMSE), it is shown that superiority of the GS algorithm in terms of iteration speed.Similarly, Fig. 5 is hair
Penetrating antenna number is 16, when receiving antenna number is 128, the ber curve pair of signal detection algorithm of the present invention and other detection algorithms
Than figure, it can be observed that performance and Neumann algorithm of the GS algorithm when the number of iterations is 1 is when the number of iterations is 4 at this time
Similar performance, but signal-to-noise ratio be greater than 11dB when restrain it is unobvious；But performance of the GS algorithm when the number of iterations is 2 is very close
In accurate Cholesky algorithm.Fig. 6 is the iteration side GS of the GS alternative manner and other initial solutions using initial solution of the present invention
Method detects the ber curve comparison diagram of signal, it can be seen that (the i.e. s in the case where not calculating initial solution0For 0), tradition
GS algorithm at the number of iterations smaller (such as 1 and 2), detection effect is very undesirable；It is mentioned by Linglong Dai et al.
In GS algorithm out, the initial solution of iteration is arranged to s0=D-1Y, detection effect obtains certain promoted at this time；And use this hair
It is 2 that performance of the GS alternative manner of bright initial solution when the number of iterations is 1, which is equivalent to GS method that initial solution is 0 in the number of iterations,
When performance, and detection effect of the GS alternative manner of initial solution of the present invention in the case where identical the number of iterations is always better than
The GS algorithm that above-mentioned Linglong Dai et al. is proposed, this embodies detection algorithm of the present invention in terms of convergence rate better than current
Existing GS method.
In terms of hardware structure, the hardware structure signal of the extensive MIMO detection based on GS method used in the present embodiment
Figure is shown in Fig. 1, including preprocessing module, Neumann series expansion module (Fig. 2) and GS method module.
Specifically, in preprocessing module, include:
1) matched filter module: the systolic arrays being made of K complex multiplier accumulator (MAC), for calculating yMF=
HHy；
2) Gram matrix computing module: the lower triangle systolic arrays being made of (1+K) K/2 MAC, for calculating G=
HHH。
In Neumann series expansion module, include:
1) multiplier array mul1: including 2K multiplier；
2) multiplier array mul2: including 4K multiplier, 2K adder, joint mul1 is for calculating-D-1ED-1；
3) multiplier array mul3: including 2K register, 4K multiplier and 4K adder, for calculating
In GS method module, include:
1) Matrix-Vector multiplication array: the systolic arrays being made of K MAC；
2) solve the systolic arrays inv of triangle battle array inverse matrix: structure chart is shown in Fig. 3, using 2 kinds of PE, comprising 6K register,
4K multiplier and 4K adder and 1 reciprocal unit, for calculating the inverse matrix of inferior triangular flap (D+L).
Claims (7)
1. a kind of extensive MIMO linearity test method based on GS method, this method is based on Gauss-Saden that iteration, feature
It is, includes the following steps:
Step 1: by channel matrix H and receiving signal y by preprocessing module, obtain matched filter output 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, pays attention to W=D+L+LH, wherein D is diagonal matrix, and L is inferior triangular flap；
Step 2: by matrix D and LHNeumann series expansion unit is inputted, 2 approximation inverse matrixes of matrix w are obtainedWherein E is the non-diagonal battle array in W, and the initial solution for GS iteration
Step 3: by yMF, D, LHAnd s0GS method module is inputted, solution is iterated, i-th iteration output is si=(D+L)-1
(yMF-LHsi-1), i=1,2 ..., signal as to be detected estimated result.
2. the extensive MIMO linearity test method according to claim 1 based on GS method, which is characterized in that the step
The number of iterations i in rapid 3 is 1~4 time.
3. a kind of hardware structure of the extensive MIMO linearity test based on GS method, it is characterised in that: including preprocessing module,
Neumann series expansion module and GS method module；
Wherein, the preprocessing module is for calculating Gram matrix and matched filtering, including the matching filter being made of systolic arrays
Wave device array, Gram matrix computing array；
The Neumann series expansion module is for taking 2 approximations of MMSE filtering matrix progress to invert and export with matched filtering
Multiplication obtains GS alternative manner initial solution, including one based on the reciprocal unit of look-up table (LUT), a vectorial addition array with
And three kinds of different multiplier array mul1, mul2, mul3；
The GS method unit is for taking MMSE filtering matrix and matched filtering to export respectively as coefficient matrix and constant vector
It carries out GS and iteratively solves system of linear equations, and each iteration is exported in deposit register, as signal detecting result, including one
A systolic arrays inv for solving triangle battle array inverse matrix, two Matrix-Vector multiplication arrays, a vectorial addition array and one
Group register, the signal for eventually passing through detection will be written in register, be convenient for the decoded operation of next step.
4. the hardware structure of the extensive MIMO linearity test according to claim 3 based on GS method, it is characterised in that:
The multiplier array mul1 includes 2K multiplier, and wherein K is number of users.
5. the hardware structure of the extensive MIMO linearity test according to claim 3 based on GS method, it is characterised in that:
The multiplier array mul2 includes 4K multiplier, 2K adder, and wherein K is number of users.
6. the hardware structure of the extensive MIMO linearity test according to claim 3 based on GS method, it is characterised in that:
The ripple multiplier array mul3 includes 2K register, 4K multiplier and 4K adder, and wherein K is number of users.
7. the hardware structure of the extensive MIMO linearity test according to claim 3 based on GS method, it is characterised in that:
The systolic arrays inv for solving triangle battle array inverse matrix includes 6K register, 4K using 2 kinds of processing units (PE)
Multiplier and 4K adder and 1 reciprocal unit, wherein 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 |
| CN107222246B (en) * | 2017-05-27 | 2020-06-16 | 东南大学 | Efficient large-scale MIMO detection method and system with approximate MMSE 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 |
| CN109525296B (en) * | 2018-10-17 | 2021-09-07 | 东南大学 | Large-scale MIMO detection method and device based on 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 |
| CN111162828B (en) * | 2019-12-12 | 2022-03-25 | 重庆邮电大学 | Low-complexity signal detection method of large-scale MIMO system |
| CN111193534B (en) * | 2020-01-08 | 2021-04-06 | 重庆邮电大学 | Low-complexity signal detection method in large-scale MIMO system |
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| CN103685106A (en) * | 2013-12-31 | 2014-03-26 | 电子科技大学 | Combined precoding method based on correlation in wireless sensor network |
| CN104954056A (en) * | 2015-06-05 | 2015-09-30 | 东南大学 | Hardware framework and method for matrix inversion in large-scale MIMO linear detection |
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