电子测量与仪器学报

代涛,李正权,王舟明,邢松.基于改进 Richardson 的大规模 MIMO 信号检测算法[J].电子测量与仪器学报,2023,37(2):171-178

基于改进 Richardson 的大规模 MIMO 信号检测算法

Massive MIMO signal detection based on improved Richardson method

DOI：

英文关键词:Richardson iteration massive MIMO steepest descent matrix inversion

基金项目:未来网络科研基金项目(FNSRFP-2021-YB-11)、 111 引智计划基金资助项目(B23008)、常州市 5G+工业互联网融合应用重点实验室项目(CM20223015)资助

作者	单位
代涛	1. 江南大学物联网工程学院
李正权	1. 江南大学物联网工程学院，2. 江苏省未来网络创新研究院，3. 江苏理工学院常州市 5G+工业互联网融合应用重点实验室
王舟明	1. 江南大学物联网工程学院
邢松	4. 加利福尼亚州立大学信息系统系

Author	Institution
Dai Tao	1. School of Internet of Things Engineering, Jiangnan University
Li Zhengquan	1. School of Internet of Things Engineering, Jiangnan University, 2. Jiangsu Future Networks Innovation Institute,3. Changzhou Key Laboratory of 5G + Industrial Internet Fusion Application, Jiangsu University of Technology
Wang Zhouming	1. School of Internet of Things Engineering, Jiangnan University
Xing Song	4. Information Systems Department, California State University, Los Angeles

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中文摘要:

在大规模多输入多输出(multiple-input multiple-output,MIMO)系统信号检测中,最小均方误差( minimum mean square error,MMSE)算法可以得到近似最优检测性能,然而该算法需要高维矩阵求逆,其复杂度很高,无法保证信号的实时检测。因此提出一种改进 Richardson 信号检测方法,利用最速下降法和整体修正法改进 Richardson 算法性能,最速下降法可以提供更有效地搜索路径,得到不同近似解,并且为了提高求解精度,利用整体修正法对不同近似解进行修正,使算法收敛速度更快,同时将算法复杂度数量级由 O(K 3 ) 降低到 O(K 2 ) 。仿真结果表明,该算法只需 3 次迭代就可接近 MMSE,在降低复杂度的同时提高了误码率性能。

英文摘要:

In the detection of massive multiple-input multiple-output systems, the minimum mean square error algorithm can obtain approximately optimal detection performance, its complexity is very high and cannot guarantee the real-time detection of the signal. An improved Richardson signal detection method is proposed, which uses the steepest descent and the whole-correction method to improve the performance of the Richardson algorithm. The steepest descent can provide more efficient search paths and obtain different approximate solutions, in order to improve the accuracy of the algorithm, the whole-correction method is used to modify the different approximate solutions, so that the convergence speed is faster, and the complexity of the algorithm is reduced from the order of magnitude O(K 3 ) to O(K 2 ) . Simulation results show that the proposed algorithm can approach MMSE with only 3 iterations, which reduces the complexity and improves the BER performance.

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