大规模MIMO通信中基于Jacobi预迭代的改进Gauss-Seide算法

Improved Gauss-Seide Algorithm Based on Jacobi Pre-iteration in Massive MIMO Communication

在大规模MIMO系统中,现有的高斯?赛德尔(Gauss?Seide,GS)算法相较于最小均方误差(Minimum mean?square error,MMSE)算法,GS的复杂度较低,但其检测性能相比而言较差。本文提出一种适用于大规模MIMO系统上行链路检测的基于雅克比预迭代改进的高斯?赛德尔(Jacobi?improved Gauss?Seide,JA?IGS)检测算法,该算法首先通过引入雅可比(Jacobi,JA)预迭代器来优化迭代初始解,然后对传统的GS进行线性优化,在增加较低复杂度情况下,检测性能和收敛速度有明显提升。仿真结果表明,与传统GS和JA检测算法相比,该算法具有较低的误码率(Bit error ratio,BER)和较高的计算效率。

In massive MIMO systems,the existing Gauss-Seide(GS) algorithm has lower complexity than the minimum mean-square error(MMSE)algorithm,while the detection performance is worse. This paper proposes a Jacobi-improved Gauss-Seide(JA-IGS) detection algorithm suitable for the uplink detection of massive MIMO systems. The algorithm first optimizes the iterative initial solution by introducing a Jacobi(JA)pre-iterator. Then the traditional GS is optimized linearly. With the increase of lower complexity,the detection performance and convergence speed are significantly improved. Simulation results show that compared with traditional GS and JA detection algorithms,the algorithm has a lower Bit error ratio(BER)and higher computational efficiency.