CN109981154A - Low complex degree array antenna multi-input multi-output system mixing precoding algorithms - Google Patents
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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 ] A method for producing a semiconductor device
The invention belongs to the technical field of mobile communication, and particularly relates to a low-complexity array antenna multi-input multi-output system hybrid precoding algorithm.
[ background of the invention ]
In order to meet the situation of explosive increase of the mobile data traffic of the fifth generation (5G), the 5G adopts a millimeter wave frequency band with 30-300 GHz, and the frequency spectrum resource is greatly improved.
The physical size of the antenna array is greatly reduced due to the relatively short wavelength of the millimeter wave, so that a large-scale antenna can be installed at a base station end, and a millimeter wave system and a large-scale Massive MIMO technology can be perfectly combined. Therefore, Massive MIMO technology is the focus of research by researchers at home and abroad in mobile communication at present.
With the development and research of a hybrid beam forming technology in a Massive MIMO system, the existing hybrid precoding scheme can be divided into two types, the first type provides space sparsity based scattering hybrid precoding, the reachable rate optimization problem is converted into a sparse approximation problem, and an Orthogonal Matching Pursuit (OMP) algorithm is used for enabling an antenna array to achieve near-optimal performance; the second category proposes a codebook-based hybrid precoding method, which performs iterative search among predefined codebooks to find an optimal hybrid precoding matrix. However, these algorithms are based on a fully-connected architecture, which is difficult to implement in hardware and has a relatively high complexity.
Because the MMSE hybrid precoding algorithm based on sparse scattering and adopting OMP iteration needs inversion and singular value decomposition calculation of a large-scale matrix, and the calculation complexity is very high, the requirement on hardware structure design is relatively improved, hardware connection needs to be redesigned, the requirement on data storage in a base station is improved, and network transmission delay is increased.
[ summary of the invention ]
The invention aims to provide a low-complexity array antenna multi-input multi-output system hybrid precoding algorithm, which reduces the calculation complexity and the consumed time of a large-scale antenna coding matrix and shortens the network transmission delay on the basis of the existing hardware connection.
The invention adopts the following technical scheme: the mixed precoding algorithm of the low-complexity array antenna multiple-input multiple-output system comprises the following steps:
according to the state information of a part of connection framework systems, giving an initial solution and a maximum iteration number S for calculating the optimal coding of an antenna subarray, and acquiring an effective channel matrix of the part of connection framework; computing an auxiliary vector z in combination with the initial solution and the effective channel matrix(s)Screening out the auxiliary vector with maximum module value, and taking the characteristic value m with maximum module value(s)；
Judging the value of the current iteration times s, when s is more than or equal to 1 and less than or equal to 2, n(s)＝m(s)，n(s)For intermediate results, when s > 2,obtaining a current calculation result u according to the intermediate result and the auxiliary vector(s)；
And continuing iteration until the maximum calculation times S is reached, obtaining the intermediate result and the calculation result of the S time, further calculating to obtain the optimal code of each antenna subarray in the partial connection architecture system, and obtaining the mixed pre-coding matrix of the partial connection architecture system by combining the optimal code of each antenna subarray.
Further, the effective channel matrix passesTo obtain the result that, among them,and B, taking the effective channel matrix as A, taking the antenna vector matrix as A, and taking the channel matrix as H.
Further, the auxiliary vector is passedIs given, wherein z(s)For the auxiliary vector calculated s, u(s-1)Is the result of s-1 th calculation.
Furthermore, the calculation results are compared before screening the characteristic value vectors, the same calculation results are combined into one calculation result, and an auxiliary vector set to be screened is obtainedWhere i is the number of different auxiliary vectors in the s auxiliary vectors.
By passingAuxiliary vector set to be screenedThe screening is carried out, and the screening is carried out,and selecting the maximum module value corresponding to the auxiliary vector as the maximum characteristic value.
Further, byCalculating a calculation result, wherein u(s)Is the result of the s-th calculation.
Further, the optimal coding calculation method for each antenna subarray specifically includes:
intermediate result n(s)Maximum singular value sigma assigned to effective channel matrix1By passingCalculating a first right singular value v of the effective channel matrix1；
By passingAndrespectively calculating the optimal digital precoding of the nth row of a digital precoding matrix W and the optimal analog precoding of the nth antenna subarray of an analog precoding matrix F in a part of connection architecture systems;
by passingAnd calculating to obtain the optimal code of the nth antenna subarray in the partial connection framework system.
The invention has the beneficial effects that: the invention provides a SIC mixed pre-coding scheme based on a partial connection structure, which converts the non-convex problem of optimizing the system capacity into the problem of solving a series of simple sum of sub-rate optimization (namely the sum of antenna sub-array rates); the problems of large-scale matrix-matrix inversion and singular value decomposition are reasonably and skillfully avoided, the algorithm complexity is greatly reduced, the signal transmission delay of a low-complexity array antenna multi-input multi-output system is saved, the algorithm performance can be close to the optimal unconstrained algorithm through algorithm complexity analysis and system capacity performance simulation, the performance is stable, and the algorithm complexity is 10% of that of sparse scattering precoding.
[ description of the drawings ]
FIG. 1 is a diagram of a prior art model of a partially connected system;
fig. 2 is a diagram of system capacity when NM × K is 64 × 16(N is 8) according to an embodiment of the present invention;
fig. 3 is a system capacity map when NM × K is 128 × 32(N is 16) according to the embodiment of the present invention.
[ detailed description ] embodiments
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
In the existing tdd downlink multi-user Massive MIMO system, as shown in fig. 1, it is assumed that a base station has complete channel state information, i.e., a channel matrix H, N radio frequency chains, each radio frequency chain is connected to M antennas, and the base station is equipped with N antennastRoot antenna, user being multiple antennas NrThe number of users of the receiving antenna is K.
NsA data stream, W ═ diag [ W1,w2,...,wN]Is a digital precoding matrix, F is an NMXN analog precoding matrix, consisting of N analog weight vectorsThe components of the composition are as follows,
millimeter wave narrowband signal vector y received by user terminal is [ y ═ y1,y2,...,yk]TIt can be expressed as follows:
wherein ρ is an average received power; h is belonged to CK×NM，Is a baseband transmission signal vector with normalized signal power(i.e., the signal satisfies the power constraint), INIs an identity matrix of dimension N x N, and P FW is an NM XN hybrid precoding matrix that satisfies the total transmission power constraint | | | P | | non-magnetic circuitsF≤N，a＝[a1,a2,…aN]TIs an additive white Gaussian noise vector whose elements obey independent equal distribution (i.i.d) CN (0, sigma)2) Then the total achievable rate of the system can be expressed as:
wherein, IkIs an identity matrix.
Theoretically and practically, the performance of the traditional all-digital precoding is optimal, and therefore, the optimization target of the hybrid precoding performance close to the all-digital precoding performance is adopted.
In a fully loaded system where the number of users is the same as the number of transmit antennas, the performance of ZF (i.e., nulling) or precoding does not increase linearly. Firstly obtaining a non-normalized mixed precoding matrix P by an MMSE (minimum mean square error) method according to the reciprocity of the channelMMSEIn the traditional digital precoding algorithm, the MMSE method is a compromise between the ZF method (zero-breaking method) and the BD method (namely the block division method) in complexity and performance, so that the invention firstly adopts an MMSE code matrix PMMSEInstead of FW, solve the problemEquation (2) is equivalent to solving the following problem:
in order to code the matrix for the MMSE,and A is an antenna vector matrix. Solving the above problem can be converted into a problem of solving an antenna subarray rate optimal solution, i.e. a solution of the antenna subarray rate
The sub-antenna coding vectors are de-upscaled where ψ comprises all MMSE coding vectors that satisfy the constant modulus constraint and the power constraint. Because of p heren optIt does not conform to the constant modulus constraint and cannot be directly taken as the optimal solution. Therefore, the problem (4) can be converted into the following problem:
where v is1Is an effective channel matrixThe first column of the right singular matrix, equation (5) shows that a feasible precoding vector can be foundPrecoding vectors v that are sufficiently close (euclidean distance) to optimal, but not usable directly1And maximizing the achievable rate of the nth antenna subarray, the digital precoding and the analog precoding are respectively as follows:
wherein,is the digital precoding of the nth row of the digital precoding matrix W,is v1The conjugate transpose of (c).For analog precoding of the nth antenna sub-array of the analog precoding matrix,
represents the optimal solution for the analog precoding of the nth antenna sub-array,for normalization factor, M represents the number of antennas in each antenna subarray, jangle (v)1) Express and take v1The optimal coding of the nth antenna sub-array (i.e. the nth column) can be expressed as:
because of the fact thatIs to satisfy the properties of a hermitian matrix, i.e. is a hermitian matrix, which follows the following two properties: 1)is also a diagonalizable matrix; 2)the right singular value matrix of (a) is similar to the eigenvalue matrix of the eigenvalue decomposition. Therefore, the power iteration algorithm can be used for calculating v1Can also be used for calculatingMaximum eigenvalue Σ of1。
In the algorithm of the present embodiment, the iteration is from the initial solution u(0)∈CM×1Initially, the present embodiment is set to [1, 1., 1 ·]TWithout loss of generality. In each iteration, an auxiliary vector is first calculatedThen extracting the auxiliary vector z with the maximum module value(s)Modulus value m of(s)。
Then u(s)Is updated to u(s)＝z(s)/m(s)For the next iteration. The algorithm of the invention stops until the number of iterations reaches a predefined value S. Finally, m(s)And u(s)/||u(s)||2Will be respectively taken as maximum singular value ∑1Andthe first right singular vector.
In order to reduce the computational complexity in solving equation (8), the algorithm of the present invention is used to solve v1The invention avoids SVD decomposition and matrix inversion problems, and simultaneously avoids matrix-matrix multiplication matrix-vector multiplication in each iteration in formula (3) through formula derivation, namely, the invention not only just calculates a matrix symbol, but also multiplies a very large-scale matrixThe method is converted into multiplication between a matrix and a single vector, and the calculation amount is greatly reduced.
The algorithm steps of the invention are as follows:
step 1, according to the state information of a partial connection architecture system, giving an initial solution u for calculating the optimal coding of an antenna subarray(0)∈CM×1And a maximum number of calculations S, the initial solution being given as [1,1, …, 1%]T。
Obtaining an effective channel matrix for a partially connected architectureBy combining the initial solution with the effective channel matrixCalculating an auxiliary vector z(s)S is more than or equal to 1 and less than or equal to S and is the current iteration frequency.
Comparing the calculation results before screening the characteristic value vectors, merging the same calculation results into one calculation result to obtain an auxiliary vector set to be screenedIn the i auxiliary vectors, bySelecting one with the maximum modulus value as a characteristic value vector m(s)And i represents the number of different auxiliary vectors among the s auxiliary vectors.
After the characteristic value vector is obtained, iteration calculation is continued, and the iteration times s are judged:
when s is more than or equal to 1 and less than or equal to 2, n(s)＝m(s)，n(s)An intermediate result.
When s > 2, the compound is a compound having a structure,
n is to be(s)Substitution of value of (1)In (1), obtaining the iteration result u after the s iteration(s)。
Through the steps, the effective channel matrix can be obtainedMaximum singular value Σ of1＝n(s)And a first right singular value
By passingMaximum singular value Σ of1And a first right singular value v1Combining the formulas (6) and (7) to obtainAndfinally, the optimal coding of the nth antenna subarray is obtained according to the formula (8)And combining the optimal codes of each antenna subarray to obtain a mixed precoding matrix of a part of connection architecture systems.
In this embodiment, some program code designs in the algorithm are also listed, specifically as follows:
Input：(1)
(2) initial solution u(0)；
(3) The 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 ∑1＝n(s)
(2) First right singular value
Step 2: solving hybrid precoding
Input:
For 1≤n≤N
1) Calculation by Algorithm 2V is1Sum-sigma1
2)
End for
Output：(1)
(2)
(3)P＝FW
Example (b): complexity analysis
TABLE 1 Algorithm complexity comparison
As can be seen from table 1, the complexity ratios provided by the above methods for the hybrid precoding complexity based on MMSE iterative algorithm and the hybrid precoding algorithm based on spatial sparsity proposed in the prior art are that, in a typical millimeter wave MIMO system, when N is 8, M is 8, K is 16, and L is 3, L is the number of effective paths. Observe that SIC-based hybrid precoding algorithm complexity requires 4 × 103The sub-multiplication sum 102And (5) division. Set S5. In comparison, the complexity of the pre-coding algorithm based on space sparsity needs to be about 5 multiplied by 104The sub-multiplication sum 103And (6) division. Therefore, the complexity of the SIC-based hybrid precoding algorithm provided by the invention is based on the space sparsityThe hybrid precoding algorithm is 10% of the complexity.
Example (b): analysis of simulation results
Simulation conditions are as follows:
the simulation conditions are described below, where the number of active channel paths is L-3 and the carrier frequency is set to 28 GHz. The transmit and receive antenna arrays are both ULA (uniform linear arrays) with an antenna spacing d of λ/2. AoD (angle of arrival) is assumed to be [ - π/6, π/6]Are uniformly distributed. Meanwhile, due to the random distribution of the user positions, the AOA is assumed to be in [ -pi/2, pi/2]Are uniformly distributed. In addition, the maximum number of iterations when running algorithm 2 is set to S-5. Finally, SNR (Signal-to-noise ratio) is defined as
And (3) system performance simulation:
as can be seen from fig. 2, the proposed SIC coding system capacity under perfect channel information is superior to the conventional analog precoding with sub-concatenated architecture over the entire SNR range, and approaches the optimal unconstrained full concatenated coding and spatial sparse scattering based coding. Fig. 3 increases the size of the antenna, and it can be observed from fig. 3 that the same trend as fig. 2 is provided, which illustrates that the SIC algorithm proposed not only has low complexity, but also meets the system performance requirements, and still has stable performance under the condition of increasing the number of antennas.
Claims (6)
1. The hybrid precoding algorithm of the low-complexity array antenna multi-input multi-output system is characterized by comprising the following steps:
according to the state information of a part of connection framework systems, giving an initial solution and a maximum iteration number S for calculating the optimal coding of an antenna subarray, and acquiring an effective channel matrix of the part of connection framework; computing an auxiliary vector z in combination with the initial solution and the effective channel matrix(s)Screening out the auxiliary vector with maximum module value, and taking the characteristic value m with maximum module value(s)；
Judging the currentThe value of the iteration times s is that when s is more than or equal to 1 and less than or equal to 2, n(s)＝m(s)，n(s)For intermediate results, when s > 2,obtaining a current calculation result u according to the intermediate result and the auxiliary vector(s)；
And continuing iteration until the maximum calculation times S is reached, obtaining an intermediate result and a calculation result of the S time, further calculating to obtain the optimal code of each antenna subarray in the partial connection architecture system, and obtaining a mixed pre-coding matrix of the partial connection architecture system by combining the optimal code of each antenna subarray.
2. The low complexity array antenna multiple-input multiple-output system hybrid precoding algorithm of claim 1, wherein the effective channel matrix is formed byTo obtain the result that, among them,and B, taking the effective channel matrix as A, taking the antenna vector matrix as A, and taking the channel matrix as H.
3. The low complexity array antenna multiple-input multiple-output system hybrid precoding algorithm of claim 2, wherein the auxiliary vector passes throughIs given, wherein z(s)For the auxiliary vector calculated s, u(s-1)Is the result of s-1 th calculation.
4. The low complexity array antenna multiple input multiple output system hybrid precoding algorithm of claim 1 or 2 or 3, wherein the filtering eigenvaluesComparing the calculation results before the vectors, merging the same calculation results into one calculation result to obtain an auxiliary vector set to be screenedWherein i is the number of different auxiliary vectors in the s auxiliary vectors;
by passingAuxiliary vector set to be screenedAnd screening, and selecting the maximum module value corresponding to the auxiliary vector as the maximum characteristic value.
5. The low complexity array antenna multiple input multiple output system hybrid precoding algorithm of claim 4, wherein the current computation result is passedIs calculated to obtain, wherein u(s)Is the result of the s-th calculation.
6. The hybrid precoding algorithm for low-complexity array antenna mimo systems as claimed in claim 1, 2 or 3, wherein the optimal coding calculation method for each antenna subarray specifically comprises:
intermediate result n(s)Maximum singular value sigma assigned to effective channel matrix1By passingCalculating a first right singular value v of the effective channel matrix1；
By passingAndrespectively calculating the optimal digital precoding of the nth row of the digital precoding matrix W and the optimal analog precoding of the nth antenna subarray of the analog precoding matrix F in the partial connection architecture system;
by passingAnd calculating to obtain the optimal code of the nth antenna subarray in the partial connection architecture system.
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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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| 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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