大规模MIMO系统中基于权重高斯赛德低复杂度ZF预编码方案
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摘要
大规模MIMO系统中的传统ZF(zero forcing,迫零)预编码方法中由于存在厄米特矩阵求逆,其复杂度随着用户数的增多呈指数增加。针对这一问题,提出了一种基于权重高斯赛德(weighted Gauss-Seidel,WGS)的低复杂度全数字ZF预编码方案,即在高斯赛德(GS)的基础上,将传统GS算法迭代结果与上一步的迭代结果进行权重相加以加速迭代收敛,其权重因子通过最小均方和来确定,并且证明权重因子可使算法收敛。仿真结果表明,WGS算法通过极少的迭代次数即可逼近ZF预编码方案的性能,且将ZF预编码的复杂度从O(K~3)降低到O(K~2),其中, K为用户数。
In massive MIMO systems, due to inversion of Hermitian matrix, the complexity of the traditional ZF precoding method increases exponentially with increase of the number of users. To solve this problem, a low complexity digital ZF precoding scheme based on weighted Gauss-Seidel(WGS) was proposed. That was weighted addtion the iteration results of previous step and Gausee-Seidel iteration results to accelerate the iterative convergence. The weighting factor was determined by the least mean square sum, and the weighting factor was proved to make the algorithm converge. The simulation results show that the WGS algorithm can approximate the performance of ZF precoding scheme with very few iterations, and reduce the complexity of ZF precoding from O(K~3) to O(K~2), where K is the number of users.
In massive MIMO systems, due to inversion of Hermitian matrix, the complexity of the traditional ZF precoding method increases exponentially with increase of the number of users. To solve this problem, a low complexity digital ZF precoding scheme based on weighted Gauss-Seidel(WGS) was proposed. That was weighted addtion the iteration results of previous step and Gausee-Seidel iteration results to accelerate the iterative convergence. The weighting factor was determined by the least mean square sum, and the weighting factor was proved to make the algorithm converge. The simulation results show that the WGS algorithm can approximate the performance of ZF precoding scheme with very few iterations, and reduce the complexity of ZF precoding from O(K~3) to O(K~2), where K is the number of users.
引文
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[4]PRASAD K N R S V,HOSSAIN E,and BHARGAVA V K,et al.Energy efficiency in massive MIMO-based 5G networks:opportunities and challenges[J].IEEE Wireless Communications,2017,24(3):86-94.
[5]BOGALE T E,LE L B.Massive MIMO and mm Wave for 5Gwireless HetNet:potential benefits and challenges[J].IEEE Vehicular Technology Magazine,2016,11(1):64-75.
[6]COSTA M H M.Writing on dirty paper(Corresp)[J].IEEETransactions on Information Theory,1983,29(3):439-441.
[7]LU Z,NING J,ZHANG Y,et al.Richardson method based linear precoding with low complexity for massive MIMO systems[C]//IEEE Vehicular Technology Conference,September6-9,2015,Boston,USA.Piscataway:IEEE Press,2015:1-4.
[8]SONG W,CHEN X,WANG L,et al.Joint conjugate gradient and Jacobi iteration based low complexity precoding for massive MIMO systems[C]//IEEE/CIC International Conference on Communications,July 27-29,2016,Chengdu,China.Piscataway:IEEE Press,2016:1-5.
[9]XIE T,LU Z,HAN Q,et al.Low-complexity LSQR-based linear precoding for massive MIMO systems[C]//IEEE Vehicular Technology Conference,September 6-9,2015,Boston,USA.Piscataway:IEEE Press,2015:1-5.
[10]XIE T,DAI L,GAO X,et al.Low-complexity SSOR-based precoding for massive MIMO systems[J].IEEE Communications Letters,2016,20(4):744-747.
[11]KAMMOUN A,MULLER A,BJORNAON E,et al.Linear precoding based on polynomial expansion:large-scale multi-cell MIMO systems[J].IEEE Journal of Selected Topics in Signal Processing,2014,8(5):861-875.
[12]PRABHU H,RODRIGUES J,EDFORS O,et al.Approximative matrix inverse computations for very-large MIMO and applications to linear precoding systems[C]//Wireless Communications and Networking Conference,April 7-10,2013,Shanghai,China.Piscataway:IEEE Press,2013:2710-2715.
[13]GAO X,DAI L,ZHANG J,et al.Capacity-approaching linear precoding with low-complexity for large-scale MIMO systems[C]//IEEE International Conference on Communications,June 8-15,2015,London,UK.Piscataway:IEEE Press,2015:1577-1582.
[14]MINANGO J,ALMEIDA C D.Low-complexity MMSE detector based on the first-order Neumann series expansion for massive MIMO systems[C]//IEEE 9th Latin-American Conference on Communications,November 8-10,2017,Guatemala City,Guatemala.Piscataway:IEEE Press,2017:1-5.
[15]FLORDELIS J,RUSEK F,TUFVESSON F,et al.Massive MIMO performance-TDD versus FDD:what do measurements say?[J].IEEE Transactions on Wireless Communications,2017(99):1.
[16]MI D,DIANATI M,ZHANG L,et al.Massive MIMO performance with imperfect channel reciprocity and channel estimation error[J].IEEE Transactions on Communications,2017(99):1.
[17]TULINO A M,VERDU S.Random matrix theory and wireless communications[J].Communications&Information Theory,2004,1(1):1-182.
[18]BJ?RCK ?.Numerical methods in matrix computations[M].Heidelburg:Springer,2015.