大规模MIMO系统低复杂度混合迭代信号检测  

Low complexity hybrid iterative algorithm based signal detection in massive MIMO system

作　　者：赵书锋[1] 申滨[1] 杨芙蓉 

机构地区：[1]重庆邮电大学移动通信重点实验室,重庆400065 [2]四川省通信科研规划设计有限责任公司,四川成都610041

出　　处：《电信科学》2017年第7期39-46,共8页Telecommunications Science

基　　金：国家科技重大专项基金资助项目(No.2016ZX03001010-004)~~

摘　　要：在大规模MIMO系统上行链路信号检测算法中,最小均方误差(MMSE)算法能获得接近最优的线性检测性能。但是,传统的MMSE检测算法涉及高维矩阵求逆运算,由于复杂度过高而使其在实际应用中难以快速有效地实现。基于最速下降(steepest descent,SD)算法和高斯—赛德尔(Gauss-Seidel,GS)迭代的方法提出了一种低复杂度的混合迭代算法,利用SD算法为复杂度相对较低的GS迭代算法提供有效的搜索方向,以加快算法收敛的速度。同时,给出了一种用于信道译码的比特似然比(LLR)近似计算方法。仿真结果表明,通过几次迭代,给出的算法能够快速收敛并接近MMSE检测性能,并将算法复杂度降低一个数量级,保持在O(K^2)。Among the uplink signal detection algorithms for massive MIMO systems, the minimum mean square error （MMSE） algorithm can achieve the near-optimal linear detection performance. However, conventional MMSE usually in- volves high complexity due to the required matrix inversion of large-size matrix, which makes it hard to implement in realis- tic applications. Based on joint steepest descent （SD） algorithm and Gauss-Seidel iteration, a low complexity hybrid iterative detection algorithm was proposed. The SD algorithm was employed to obtain an efficient searching direction for the fol- lowing Gauss-Seidel to speed up convergence. Meanwhile, an approximated method was also proposed to compute the bit log-likelihood ratio （LLR） for soft channel decoding. Simulation results verify that the proposed algorithm can converge ra- pidly and achieve its performance quite close to that of the MMSE algorithm with only a small number of iterations. Mean- while, the complexity is reduced by an order of magnitude, which is kept consistently ofO（K2） .

关 键 词：大规模MIMO 最小均方误差 矩阵求逆 最速下降 Gauss-Seidel迭代 软输出 

分 类 号：TN929.5[电子电信—通信与信息系统]