CN109981154A - Low complex degree array antenna multi-input multi-output system mixing precoding algorithms - Google Patents
Low complex degree array antenna multi-input multi-output system mixing precoding algorithms Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- 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
- H04B7/0456—Selection of precoding matrices or codebooks, e.g. using matrices antenna weighting
Abstract
The invention discloses low complex degree array antenna multi-input multi-output system mixing precoding algorithms, give the initial solution and max calculation number for calculating antenna submatrix optimum code, and the efficient channel matrix of fetching portion connection framework；Auxiliary vector is calculated in conjunction with initial solution and efficient channel matrix, filters out the maximum auxiliary vector of modulus value as feature value vector；Judge the value of current calculation times, and obtains intermediate result；It is obtained according to intermediate result and auxiliary vector when time calculated result；It computes repeatedly, until reaching max calculation number, it obtains intermediate result and calculated result, and then the optimum code of each antenna submatrix in part connection architecture system is calculated, the mixing pre-coding matrix of part connection architecture system is obtained in conjunction with the optimum code of each antenna submatrix；By the method for the invention, on the basis of existing hardware connects, the computation complexity and elapsed time of extensive antenna encoder matrix can be reduced, shortens Network Transmission Delays.
Description
[technical field]
The invention belongs to mobile communication technology field more particularly to a kind of low complex degree array antenna multiple-input and multiple-output systems
System mixing precoding algorithms.
[background technique]
In order to meet the situation of the 5th generation (5G) mobile communication mobile data services amount explosive growth, 5G, which is used, possesses 30
~300GHz millimeter wave frequency band, greatly improves frequency spectrum resource.
Because its wavelength is relatively short, the physical size of aerial array significantly reduces millimeter wave, therefore base station end can pacify
Extensive antenna is filled, so as to ideally combine millimeter-wave systems with extensive Massive MIMO technology.Therefore,
Massive MIMO technology becomes the emphasis of current mobile communication domestic and foreign scholars research.
With the development and research of mixed-beam figuration technology in Massive mimo system, existing mixing precoding side
Case can be divided into two classes, and the first kind, which is proposed, mixes precoding based on spatial sparsity scattering, and achievable rate optimization problem is turned
Sparse bayesian learning problem is turned to, and is solved by orthogonal matching and pursues (OMP) algorithm so that aerial array reaches close to optimal property
Energy；Second class proposes the method based on code book mixing precoding, and search is iterated between code book predetermined, finds
Optimal mixing pre-coding matrix.However, these algorithms are all based on full connection framework, not only hardware realization is difficult, but also calculates
Method complexity is quite high.
Due to needing to carry out extensive matrix using the MMSE mixing precoding algorithms of OMP iteration based on sparse scattering
It inverts and is calculated with singular value decomposition, computation complexity is very high, therefore, requires also opposite improve to Design of Hardware Architecture, it is also necessary to
Hardware connection is redesigned, the call data storage in base station is improved, increases Network Transmission Delays.
[summary of the invention]
The object of the present invention is to provide a kind of low complex degree array antenna multi-input multi-output system mixing precoding algorithms,
On the basis of existing hardware connection, the computation complexity and elapsed time of extensive antenna encoder matrix are reduced, shortens network and passes
Defeated delay.
The invention adopts the following technical scheme: low complex degree array antenna multi-input multi-output system mixing precoding is calculated
Method, comprising the following steps:
The status information that architecture system is connected according to part, give initial solution for calculating antenna submatrix optimum code and
Maximum number of iterations S, and the efficient channel matrix of fetching portion connection framework；It is calculated in conjunction with initial solution and efficient channel matrix
Auxiliary vector z(s), the maximum auxiliary vector of modulus value is filtered out, taking its modulus value is maximum characteristic value m(s)；
The value for judging current iteration number s, as 1≤s≤2, n(s)=m(s), n(s)For intermediate result, as s > 2,It is obtained according to intermediate result and auxiliary vector as time calculated result u(s)；
Continue iteration, until reaching max calculation number S, obtains the S times intermediate result and calculated result, and then calculate
The optimum code for obtaining each antenna submatrix in part connection architecture system, obtains portion in conjunction with the optimum code of each antenna submatrix
Divide the mixing pre-coding matrix of connection architecture system.
Further, efficient channel matrix passes throughIt obtains, whereinFor efficient channel matrix, A is antenna arrow
Moment matrix, H are channel matrix.
Further, auxiliary vector passes throughIt obtains, wherein z(s)The auxiliary vector calculated for the s times, u(s-1)For the s-1 times calculated result.
Further, first calculated result is compared before screening feature value vector, identical calculated result is merged into
One calculated result obtains auxiliary vector collection to be screenedWherein, i is of the different auxiliary vectors in s auxiliary vector
Number.
Pass throughIt treats and filters out auxiliary vector collectionIt is screened, selects wherein auxiliary vector
Corresponding maximum modulus value is as maximum eigenvalue.
Further, pass throughCalculate calculated result, wherein u(s)The calculated result calculated for the s times.
Further, the optimum code calculation method of each antenna submatrix specifically:
By intermediate result n(s)Assign the maximum singular value Σ of efficient channel matrix1, pass throughIt calculates effectively
The right singular value v of the first of channel matrix1；
Pass throughWithNumber in part connection architecture system is calculated separately out to prelist
The optimal simulation of n-th of antenna submatrix of the optimal digital precode and simulation pre-coding matrix F of the line n of code matrix W prelists
Code；
Pass throughThe optimum code of n-th of antenna submatrix in part connection architecture system is calculated.
The beneficial effects of the present invention are: the invention proposes the SIC mixing pre-coding scheme based on partially connected architecture, it will
This non-convex problem of optimization system capacity be converted to solve a series of simple sons it is the sum of rate optimized (i.e. antenna submatrix rate it
With) the problem of；It is rationally ingenious to avoid extensive matrix-matrix inversion and singular value decomposition problem, it is complicated greatly to reduce algorithm
Degree, saves the signal transmission delay of low complex degree array antenna multi-input multi-output system, and pass through algorithm analysis
It is emulated with system capacity performance, show that the algorithm performance can be close to optimal no bounding algorithm, performance stabilization and algorithm complexity
It is 10% based on sparse scattering precoding.
[Detailed description of the invention]
Fig. 1 is the system model figure of part connection in the prior art；
Power system capacity figure when Fig. 2 is NM × K=64 × 16 (N=8) in the embodiment of the present invention；
Power system capacity figure when Fig. 3 is NM × K=128 × 32 (N=16) in the embodiment of the present invention.
[specific embodiment]
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments.
In existing TDD down multi-user Massive mimo system, as shown in Figure 1, it is assumed that base station has possessed
N is assembled in full channel state information, i.e. channel matrix H, N number of rf chain, each radio frequency chain link M root antenna, base stationtRoot day
Line, user are multiple antennas NrReceiving antenna, number of users K.
NsA data flow, W=diag [w1,w2,...,wN] it is digital precode matrix, F is the simulation precoding of NM × N
Matrix, by N number of analog weight vectorComposition,
The millimeter wave narrow band signal vector y=[y that user terminal receives1,y2,...,yk]T, it can be expressed as follows:
Wherein, ρ is mean receiving power；H∈CK×NM,It is baseband signal vector, has and return
One changes signal power(i.e. signal meets power constraint), INFor N*N dimension unit matrix, P=FW be NM ×
N mixing pre-coding matrix, it meets total transimission power constraint | | P | |F≤ N, a=[a1,a2,…aN]TIt is an additive Gaussian
White noise vector, its element obey independent same distribution (i.i.d) CN (0, σ2), then the total achievable rate of system can indicate are as follows:
Wherein, IkFor unit matrix.
From it is theoretical and in fact, the performance of the digital precoding of tradition be it is optimal, therefore, prelisted code performance using mixing
It is optimization aim close to the digital code performance that prelists.
In number of users full load system identical with transmitting antenna, ZF (i.e. broken zero method) or the performance of precoding will not
Linear increase.MMSE method (i.e. Minimum Mean Square Error method), which is first passed through, according to the reciprocity of channel obtains non-normalized mixing precoding square
Battle array PMMSE, in conventional digital precoding algorithms, MMSE method is compared to ZF method (broken zero method) and BD method (i.e. block point-score) in complexity
With a compromise has been taken in performance, therefore, the present invention uses MMSE code matrix P firstMMSEInstead of FW, then solution formula (2) etc.
Valence is in solution following problems:
For MMSE encoder matrix,For efficient channel matrix, A is antenna
Vector matrix.The problem of solving antenna submatrix rate optimal solution can be converted to by solving the above problem, i.e.,
Sub-antenna coded vector is eliminated into subscript, ψ includes all MMSE for meeting permanent modular constraint and power constraint here
Coded vector.Because of p heren optPermanent modular constraint is not met, cannot directly be brought as optimal solution.Therefore problem (4) can be with
It is converted to following problems:
Here v1It is efficient channel matrixRight singular matrix first row, formula (5) shows that one can be found feasible
Precoding vectorClose enough (Euclidean distance) is optimal, but cannot direct precoding vector v1, to maximize
The achievable rate of n-th of antenna submatrix, then digital precode and simulation precoding are respectively as follows:
Wherein,It is the digital precode of the line n of digital precode matrix W,It is v1Conjugate transposition.For mould
The simulation precoding of quasi- n-th of antenna submatrix of pre-coding matrix,
Indicate the optimal solution of the simulation precoding of n-th of antenna submatrix,For normalization factor, M indicates each
Antenna number in antenna submatrix, jangle (v1) indicate to take v1In angle information, then n-th antenna submatrix (the i.e. n-th column)
Optimum code can indicate are as follows:
BecauseIt is to meet Hull meter Te matrix properties, is Hull meter Te matrix, it follows following two property: 1)It is also a diagonalization matrix；2)Right singular value matrix it is similar with the eigenvalue matrix of Eigenvalues Decomposition.Therefore,
It can be used to calculate v in power iteration algorithm1, can also be used to calculateMaximum eigenvalue Σ1。
In the algorithm of the present embodiment, iteration is from initial solution u(0)∈CM×1Start, be set as in the present embodiment [1,1 ...,
1]T, but without loss of generality.In each iteration, auxiliary vector is calculated firstThen it is maximum to extract modulus value
Auxiliary vector z(s)Modulus value m(s)。
Later, u(s)It is updated to u(s)=z(s)/m(s), it is used for next iteration.Inventive algorithm reaches until the number of iterations
Stop when predefined value S.Finally, m(s)And u(s)/||u(s)||2It will be respectively as maximum singular value Σ1WithFirst right side is unusual
Vector.
Computation complexity when in order to reduce solution formula (8) solves v using inventive algorithm1, avoid SVD decomposition and square
Battle array inversion problem, while by the derivation of equation can will be avoided in iteration each in formula (3) matrix-matrix multiplication matrix-to
Multiplication is measured, i.e. its calculating for being not only merely a matrix notation, is the multiplication between very large-scale matrix and matrix actually,
The method of the present invention is directly to be extracted a most useful column in matrix, changes matrix and multiplication of matrices into matrix and single vector-quantities
Between multiplication, calculation amount greatly reduces.
Inventive algorithm is shown in steps are as follows:
Step 1. connects the status information of architecture system according to part, gives for calculating the first of antenna submatrix optimum code
The solution that begins u(0)∈CM×1With max calculation number S, initial solution is given as [1,1 ..., 1]T。
The efficient channel matrix of fetching portion connection frameworkBy initial solution and efficient channel matrix and combineCalculate auxiliary vector z(s), 1≤s≤S is current iteration number.
First calculated result is compared before screening feature value vector, identical calculated result is merged into a calculating knot
Fruit obtains auxiliary vector collection to be screenedIn i auxiliary vector, pass throughChoose modulus value
Maximum one is used as feature value vector m(s), the number of i expression different auxiliary vectors in s auxiliary vector.
After obtaining feature value vector, continue to iterate to calculate, judge the number of iterations s:
As 1≤s≤2, n(s)=m(s), n(s)For intermediate result.
As s > 2,
By n(s)Value substitute intoIn, obtain the u of iteration result after s iteration(s)。
It can be obtained by above step, efficient channel matrixMaximum singular value Σ1=n(s)With first right singular value
Pass throughMaximum singular value Σ1With first right singular value v1, obtained in conjunction with formula (6) and (7)WithFinally, obtaining the optimum code of n-th of antenna submatrix according to formula (8)By the optimum code of each antenna submatrix
In conjunction with the mixing pre-coding matrix for obtaining part connection architecture system.
The portion of program code design in algorithm is also listed in the present embodiment, specific as follows:
Input:(1)
(2) initial solution u(0)；
(3) maximum number of iterations S；
For 1≤s≤2
1)
2)
3)If 1≤s≤2
n(s)=m(s)
Else
End if
4)
End for
Output:(1) maximum singular value Σ1=n(s)
(2) first right singular values
Step 2: solving mixing precoding
Input:
For 1≤n≤N
1) it is calculated by algorithm 2V1And Σ1
2)
End for
Output:(1)
(2)
(3) P=FW
Embodiment: analysis of complexity
The comparison of 1 algorithm complexity of table
By table 1 it is found that about based on MMSE iterative algorithm mixing precoding complexity and the prior art provided by it
Proposed in based on spatial sparsity mixing precoding algorithms complexity comparison, under typical millimeter wave mimo system, work as N=8,
When M=8, K=16, L=3, L is active path quantity.It observes and needs 4 × 10 based on SIC mixing precoding algorithms complexity3
Secondary multiplication and 102Secondary division.S=5 is set.It compares, takes around 5 × 10 based on spatial sparsity precoding algorithms complexity4
Secondary multiplication and 103Division.It follows that the complexity of the mixing precoding algorithms proposed by the invention based on SIC is to be based on
The 10% of spatial sparsity mixing precoding algorithms complexity.
Embodiment: analysis of simulation result
Simulated conditions:
Simulated conditions are described as follows, and the quantity in efficient channel path is L=3, and carrier frequency is set as 28GHz.Transmitting and
Receiving antenna array is all the ULA (uniform linear array) of λ/2 antenna spacing d=.AoD (angle of arrival) assumes in [- π/6, π/6]
On be uniformly distributed.Simultaneously because the random distribution of user location, it is assumed that AOA is uniformly distributed on [- pi/2, pi/2].In addition, transporting
Maximum number of iterations when row algorithm 2 is set as S=5.Finally, SNR (signal-to-noise ratio) is defined as
Simulation:
Figure it is seen that the SIC coded system capacity proposed under perfect channel information is excellent in entire SNR range
There is the simulation precoding of son connection framework in tradition, and close to optimal without constraining full connection structure coding and to be based on space dilute
Dredge scattering coding.Fig. 3 increases antenna scale, from Fig. 3 it can be observed how with Fig. 2 trend having the same, illustrates proposition
Not only to send out degree miscellaneous low for algorithm for SIC algorithm, while also meeting system performance requirements, and still have the case where increasing antenna amount
There is stable performance.
Claims (6)
1. low complex degree array antenna multi-input multi-output system mixing precoding algorithms, which comprises the following steps:
The status information that architecture system is connected according to part, gives the initial solution and maximum for calculating antenna submatrix optimum code
The number of iterations S, and the efficient channel matrix of fetching portion connection framework；It is calculated in conjunction with the initial solution and efficient channel matrix
Auxiliary vector z(s), the maximum auxiliary vector of modulus value is filtered out, taking its modulus value is maximum characteristic value m(s)；
The value for judging current iteration number s, as 1≤s≤2, n(s)=m(s), n(s)For intermediate result, as s > 2,It is obtained according to the intermediate result and auxiliary vector as time calculated result u(s)；
Continue iteration, until reaching max calculation number S, obtains the S times intermediate result and calculated result, and then be calculated
The optimum code of each antenna submatrix, obtains in conjunction with the optimum code of each antenna submatrix in the part connection architecture system
The mixing pre-coding matrix of the part connection architecture system out.
2. low complex degree array antenna multi-input multi-output system mixing precoding algorithms as described in claim 1, feature
It is, the efficient channel matrix passes throughIt obtains, whereinFor efficient channel matrix, A is antenna vector matrix, H
For channel matrix.
3. low complex degree array antenna multi-input multi-output system mixing precoding algorithms as claimed in claim 2, feature
It is, the auxiliary vector passes throughIt obtains, wherein z(s)The auxiliary vector calculated for the s times, u(s-1)It is
S-1 calculated result.
4. the low complex degree array antenna multi-input multi-output system mixing precoding algorithms as described in claims 1 or 2 or 3,
It is characterized in that, first calculated result is compared before screening feature value vector, identical calculated result is merged into a meter
It calculates as a result, obtaining auxiliary vector collection to be screenedWherein, i is the number of the different auxiliary vectors in s auxiliary vector；
Pass throughIt treats and filters out auxiliary vector collectionIt is screened, it is corresponding to select wherein auxiliary vector
Maximum modulus value as maximum eigenvalue.
5. low complex degree array antenna multi-input multi-output system mixing precoding algorithms as claimed in claim 4, feature
It is, it is described when time calculated result passes throughIt is calculated, wherein u(s)The calculated result calculated for the s times.
6. the low complex degree array antenna multi-input multi-output system mixing precoding algorithms as described in claims 1 or 2 or 3,
It is characterized in that, the optimum code calculation method of each antenna submatrix specifically:
By intermediate result n(s)Assign the maximum singular value Σ of efficient channel matrix1, pass throughCalculate efficient channel
The right singular value v of the first of matrix1；
Pass throughWithCalculate separately out digital precode in the part connection architecture system
The optimal simulation precoding of n-th of antenna submatrix of the optimal digital precode and simulation pre-coding matrix F of the line n of matrix W；
Pass throughThe optimum code of n-th of antenna submatrix in the part connection architecture system is calculated.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110212962A (en) * | 2019-07-07 | 2019-09-06 | 东北大学秦皇岛分校 | One kind is based on simulation phase shift-switch cascade network mixing method for precoding |
| CN111555782A (en) * | 2020-03-08 | 2020-08-18 | 郑州大学 | Mixed precoding design method based on multi-user millimeter wave MIMO-OFDM system |
| CN112039565A (en) * | 2020-09-11 | 2020-12-04 | 成都大学 | Large-scale MIMO mixed pre-coding method based on distributed part connection |
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| CN112468202B (en) * | 2020-05-14 | 2021-12-21 | 哈尔滨工程大学 | Low-complexity millimeter wave large-scale MIMO hybrid precoding method |
| CN112054826B (en) * | 2020-09-14 | 2021-09-07 | 长沙理工大学 | Single-user low-complexity hybrid precoding method based on intermediate channel |
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| CN106506051A (en) * | 2015-09-08 | 2017-03-15 | 上海贝尔股份有限公司 | Method and apparatus based on the mixing precoding of reconfigurable antenna |
| CN105959048B (en) * | 2016-06-23 | 2019-02-15 | 北京科技大学 | A kind of method for precoding of extensive antenna |
| CN108075811B (en) * | 2016-11-11 | 2021-03-30 | 上海诺基亚贝尔股份有限公司 | Method for hybrid precoding and communication device |
| CN108449121B (en) * | 2018-02-13 | 2020-09-01 | 杭州电子科技大学 | Low-complexity hybrid precoding method in millimeter wave large-scale MIMO system |
| CN109039400B (en) * | 2018-08-14 | 2020-11-17 | 西安科技大学 | Hybrid pre-coding/merging device design method based on matrix decomposition |
| CN109167622B (en) * | 2018-11-08 | 2021-04-30 | 江西理工大学 | Mixed precoding method for millimeter wave large-scale MIMO system |
| CN109617585A (en) * | 2019-01-18 | 2019-04-12 | 杭州电子科技大学 | Mixing method for precoding based on part connection in the extensive MIMO of millimeter wave |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110212962A (en) * | 2019-07-07 | 2019-09-06 | 东北大学秦皇岛分校 | One kind is based on simulation phase shift-switch cascade network mixing method for precoding |
| CN111555782A (en) * | 2020-03-08 | 2020-08-18 | 郑州大学 | Mixed precoding design method based on multi-user millimeter wave MIMO-OFDM system |
| CN112039565A (en) * | 2020-09-11 | 2020-12-04 | 成都大学 | Large-scale MIMO mixed pre-coding method based on distributed part connection |
| CN112039565B (en) * | 2020-09-11 | 2021-03-26 | 成都大学 | Large-scale MIMO mixed pre-coding method based on distributed part connection |
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