大规模MIMO系统中的联合预编码算法

SORMI-Newton Joint Precoding Algorithm in Massive MIMO Systems

在大规模多输入多输出(Massive MIMO)系统中,针对正则化迫零(RZF)预编码算法矩阵求逆复杂度较高的问题,现有的牛顿(Newton)迭代法可有效减小复杂度。但Newton迭代法收敛速度慢且迭代初始值计算复杂。论文基于超松弛(SOR)迭代法的思想得到一种矩阵求逆方法即超松弛矩阵求逆(SORMI)迭代法,并将其迭代结果作为Newton迭代法的初始值,提出SORMI-Newton联合预编码算法。仿真结果表明,与Newton和SORMI迭代法相比,联合算法可提供更精确的迭代初始值,从而加快了收敛速度,能够以较少迭代次数达到RZF预编码误码率性能。

In Massive Multiple Input Multiple Output(Massive MIMO)systems,the existing Newton iterative method can effectively reduce the computation complexity of the Regularized Zero Forcing(RZF)precoding algorithm in which complicated matrix inversion is required. However,the Newton iterative method has slow convergence speed and it needs complicated calculation to search initial values. In order to solve this problem,by using Successive Over Relaxation(SOR)iterative method,a matrix inversion method,namely Successive Over Relaxation Matrix Inverse(SORMI)iteration method,is derived in this paper to obtain the initial values for Newton iterative method. Correspondingly,the SORMI-Newton joint precoding algorithm is presented. Simulation results show that compared with the Newton and SORMI iterative method,the joint precoding algorithm can provide appropriate initial iterative values to increase the convergence speed,and therefore it can achieve the bit error rate performance which is comparable to that of the RZF algorithm with fewer iterations.