空天预警研究学报2025，Vol.39Issue(2)：79-85,7.DOI:10.3969/j.issn.2097-180X.2025.02.001
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
摘要
Abstract
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 re-duce 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 con-vergence 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Key words
large-scale MIMO/CGJA iteration/condition number minimization preprocessing/Gauss-Seidel iteration/conjugate gradient iteration分类
信息技术与安全科学引用本文复制引用
申东,王佳豪,谭昕,曾若程..基于Gauss-Seidel和共轭梯度迭代的高性能联合检测算法[J].空天预警研究学报,2025,39(2):79-85,7.基金项目
宁夏自然科学基金项目(2023AAC03741) （2023AAC03741）
甘肃省科技计划项目重点研发计划项目(23YFGA0047) （23YFGA0047）
