MIMO中基于预处理的广义加权Gauss-Seide算法

Generalized Weighted Gauss-Seide Iterative Algorithm based on Preprocessing in MIMO System

最小均方误差(MMSE)检测器在大规模多输入多输出(MIMO)系统中实现了较优的误码率(BER)性能,但其涉及高复杂度的大规模矩阵求逆运算,因此对硬件要求很高。针对这一问题,文章提出了一种预处理的广义加权高斯赛德尔(GS)(GW-PGS)迭代算法。在该算法中,首先提出了基于预处理的初始化方案,在不增加额外复杂度的情况下加快了收敛速度。此外,文章还提出了自适应的加权因子方案。实验结果表明,与传统GS算法相比,文章所提算法能够有效降低BER和计算复杂度。

The Minimum Mean-Square Error(MMSE)detector can achieve excellent Bit Error Rate(BER)performance in massive Multiple-Input Multiple-Output(MIMO)systems.However,it involves high complexity large-scale matrix inversion operation with high degree,resulting in very high hardware requirements.To solve this problem,the article proposes a Generalized Weighted-Preconditioned Gauss-Seide(GS)(GW-PGS)iterative algorithm.In this algorithm,an initialization scheme based on preprocessing is first proposed,which speeds up the convergence speed without adding additional complexity.In addition,this paper proposes an adaptive weighting factor scheme.The experimental results show that compared with the traditional GS algorithm,the algorithm proposed in this paper can effectively reduce the BER and computational complexity.