基于Gauss-Seidel和共轭梯度迭代的高性能联合检测算法  

A high-performance joint detection algorithm based on Gauss-Seidel and conjugate gradient iteration

作　　者：申东[1] 王佳豪 谭昕 曾若程 SHEN Dong;WANG Jiahao;TAN Xin;ZENG Ruocheng(School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学电子与信息工程学院,兰州730070

出　　处：《空天预警研究学报》2025年第2期79-85,共7页JOURNAL OF AIR & SPACE EARLY WARNING RESEARCH

基　　金：宁夏自然科学基金项目(2023AAC03741);甘肃省科技计划项目重点研发计划项目(23YFGA0047)。

摘　　要：针对最小均方误差(MMSE)检测应用于大规模多输入多输出(MIMO)信号检测会产生较大运算量的问题,提出了一种基于Gauss-Seidel(GS)迭代算法和共轭梯度(CG)迭代算法的联合检测改进算法(GSCG).首先,利用CG迭代算法给出良好的初始检测方向,并降低计算复杂度;然后,结合雅可比(JA)迭代算法,优化迭代初始解,使算法在保证原来检测性能的基础上加快收敛速度;最后,设计了一种有效的条件数最小化预处理,将原GS迭代有效地转化为具有相同解的新迭代,实现算法的快速迭代收敛.仿真结果表明,在不同的信道相关场景下,与其他迭代算法相比,本文算法误码率性能更优,收敛速度更快,所需迭代次数更少.A joint detection improvement algorithm(GSCG)based on Gauss-Seidel(GS)iterative algorithm and conjugate gradient(CG)iterative algorithm is proposed to address the problem of large computational complexity in the application of minimum mean square error(MMSE)detection for large-scale multiple-input multiple-output(MIMO)signal detection.Firstly,the CG iterative algorithm is used to provide a good initial detection direction and reduce computational complexity.Then,combined with the Jacobi(JA)iterative algorithm,the iterative initial solution is optimized to accelerate convergence speed while ensuring the original detection performance.Finally,an effective condition number minimization preprocessing is designed to effectively transform the original GS iteration into a new iteration with the same solution,achieving fast iterative convergence of the algorithm.The simulation results show that compared with other iterative algorithms,the proposed algorithm has better bit error rate performance,faster convergence speed,and requires fewer iterations in different channel related scenarios.

关 键 词：大规模MIMO CGJA迭代 条件数最小化预处理 Gauss-Seidel迭代 共轭梯度 

分 类 号：TN957[电子电信—信号与信息处理]