智能反射面辅助MU-MISO系统宽带信道估计算法

A Broadband Channel Estimation Algorithm for Intelligent Reflecting Surface Aided MU-MISO Systems

为适应移动通信系统的宽带化应用,针对智能反射面(Intelligent Reflecting Surface,IRS)辅助多用户多输入单输出系统信道估计问题,提出了一种基于稀疏矩阵分解的改进正交匹配追踪(Orthogonal Matching Pursuit,OMP)算法。首先,考虑基站导频约束及IRS相移约束,建立一个压缩感知模型。然后,通过自适应地细化变换矩阵的分辨率,避免稀疏信号的细节丢失;再利用信道在不同子载波处共享的公共稀疏性,对多个子频带进行联合估计。最后利用级联信道的双结构稀疏性,将信道估计扩展至多用户场景。仿真结果表明,与传统压缩感知算法相比,所提算法能以较小的导频开销获得更高的准确性。

In order to adapt to the broadband application of mobile communication system,an improved Orthogonal Matching Pursuit(OMP)algorithm based on sparse matrix decomposition is proposed for the channel estimation in an Intelligent Reflecting Surface(IRS)assisted multiple users multiple input single output(MU-MISO)system.Firstly,considering the base station pilot constraint and IRS phase shift constraint,a compressed sensing problem is established.Secondly,the resolution of the transform matrix is adaptively refined to avoid the loss of details of sparse signals.Then,the common sparsity of the channel shared at different subcarriers is used to jointly estimate multiple sub-bands.Finally,using the dual structure sparsity of cascaded channels,the channel estimation is extended to multi-user scenarios.Simulation results show that compared with the traditional compressed sensing algorithms,the proposed algorithm can achieve higher accuracy.