基于压缩感知的智能反射面信道估计  

Channel estimation on intelligent reflecting surface based on compressed sensing

作　　者：刘刚[1] 李雨航 杨庆鑫 郭漪 LIU Gang;LI Yuhang;YANG Qingxin;GUO Yi(School of Telecommunications Engineering,Xidian University,Xi’an 710071,China)

机构地区：[1]西安电子科技大学通信工程学院,陕西西安710071

出　　处：《系统工程与电子技术》2024年第7期2490-2497,共8页Systems Engineering and Electronics

基　　金：国家自然科学基金(62171354);陕西省自然科学基础研究计划(2024JC-YBWS-533)资助课题。

摘　　要：针对智能反射面(intelligent reflecting surface,IRS)辅助的通信系统中稀疏度未知信道的估计问题,提出了一种基于压缩感知的稀疏自适应信道估计算法。首先,研究了正交匹配追踪(orthogonal matching pursuit,OMP)算法下信道的残差l2范数与输入的信道稀疏度之间的关系,得出了OMP算法恢复稀疏度未知信道的迭代终止条件;然后,提出了一种二阶段稀疏自适应信道估计算法,在第一阶段估计信道稀疏度,在第二阶段增加或删减支撑集原子,最终使得恢复的信道向量误差最小。仿真结果表明,与经典的最小二乘法、已知稀疏度的OMP算法、稀疏自适应匹配追踪(sparsity adaptive matching pursuit,SAMP)算法相比,提出的算法性能良好,鲁棒性强。A sparse adaptive channel estimation algorithm based on compressed sensing is presented to estimate sparse unknown channels in intelligent reflecting surface(IRS)assisted communication systems.First,the relationship between the l 2 norm of the channel residual under the orthogonal matching pursuit(OMP)algorithm and the sparsity of the channel is studied,and the iteration termination conditions for the OMP to restore the sparsity of unknown channels are obtained.Then,a two-stage sparse adaptive channel estimation algorithm is presented.In the first stage,the channel sparseness is estimated,and in the second stage,the supporting set atoms are added or deleted to minimize the channel vector errors recovered.The simulation results show that the performance and robustness of the proposed algorithm are better than those of classical least squares,OMP with known sparsity,and sparse adaptive matching pursuit(SAMP).

关 键 词：智能反射面 压缩感知 稀疏自适应 信道估计 

分 类 号：TN929.5[电子电信—通信与信息系统]