基于IOC-CSMP的OFDM系统稀疏信道快速重构算法  

Sparse channel fast reconstruction algorithm for OFDM system based on IOC-CSMP

作　　者：崔伟[1] 于颖[1] 于海霞 陈超[1] 李云鹏[1] CUI Wei;YU Ying;YU Haixia;CHEN Chao;LI Yunpeng(College of Avaiation,Avaiation University of Air Force,Changchun 130022,China)

机构地区：[1]空军航空大学航空作战勤务学院,吉林长春130022

出　　处：《通信学报》2023年第2期52-58,共7页Journal on Communications

基　　金：国家自然科学基金资助项目(No.61571462);空军航空大学中青年骨干支持计划基金资助项目(No.HDZQN2020-012)。

摘　　要：针对信道路径数量未知时正交频分复用(OFDM)系统信道估计问题,提出了一种基于内积运算优化与稀疏度更新约束的压缩采样匹配追踪快速重构算法。通过构建与更新选择向量,利用与选择向量中非零值索引对应的原子向量参与内积运算来降低运算量;基于压缩采样与回溯策略来优化原子,利用匹配追踪完成信道估计,通过相邻两次信道估计值的能量差来更新稀疏度并约束算法停止,保证算法快速收敛。仿真结果表明,所提算法具有比最小二乘、最小均方差、稀疏度自适应匹配追踪和自适应正则化压缩采样匹配追踪算法更好的信道估计性能,且比2种自适应方法消耗更少的信道估计时间。A fast reconstruction algorithm based on inner product optimization and sparsity updating constraint was proposed for OFDM system channel estimation when the number of channel paths was unknown.By constructing and updating the selection vector,the inner product operation was reduced by using the atoms corresponding to the non-zero index of the selection vector.The atoms were optimized based on compressed sampling and backtracking strategies,and the channel estimation was completed by matching pursuit.The sparsity update and the stop condition for the algorithm was achieved by the energy difference between the two adjacent channel estimation so as to ensure fast convergence of the algorithm.The simulation results show that the proposed algorithm has better channel estimation performance than the least square algorithm,minimum mean square error algorithm,sparsity adaptive matching pursuit algorithm and adaptive regularized compressed sampling matching pursuit algorithm,and consumes less channel estimation time than the two adaptive methods.

关 键 词：压缩采样 内积运算 回溯策略 稀疏度自适应 信道估计 

分 类 号：TN92[电子电信—通信与信息系统]