RIS辅助无线系统中基于压缩感知的稀疏度自适应级联信道估计方法研究
Research on Sparse Adaptive Cascade Channel Estimation Method Based on Compressed Sensing in RIS Assisted Wireless System
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摘要: 为了解决可重构智能超表面(Reconfigurable Intelligent Surface, RIS)辅助无线通信系统中级联信道的估计问题,本文提出了一种基于压缩感知(Compressive Sensing, CS)的自适应双结构稀疏正交匹配追踪算法(Adaptive Double-Structured Orthogonal Matching Pursuit, ADS-OMP)。已有的双结构稀疏正交匹配追踪算法(Double-Structured Orthogonal Matching Pursuit, DS-OMP)利用级联信道的双结构稀疏特性即行稀疏特性和列稀疏特性来提高算法估计性能,但需要已知相关信道稀疏度信息。本文提出的ADS-OMP算法在现有的DS-OMP算法基础上设计合理的判决准则和迭代阈值来估计相关稀疏度,从而能在角域级联信道的行稀疏度和列稀疏度均未知的情况下完成级联信道估计,有效克服了现有DS-OMP算法对相关信道稀疏度的依赖,算法实用性更强。仿真结果表明,本文提出的ADS-OMP算法和已有DS-OMP算法估计性能一致,算法复杂度在同一数量级上,前者复杂度略微提升。Abstract: In order to solve the problem of cascaded channel estimation in reconfigurable intelligent surface (RIS) assisted wireless communication system, an adaptive double structured orthogonal matching pursuit (ADS-OMP) algorithm based on compressed sensing (CS) is proposed in this paper.Existing dual-structured sparse orthogonal matching pursuit (DS-OMP) algorithm used the dual-structured sparseness of cascade channels, i.e., row sparseness and column sparseness, to improve algorithm estimation performance, but required known channel sparseness information.Based on the existing DS-OMP algorithm, the ADS-OMP algorithm proposed in this paper designed reasonable judgment criteria and iteration thresholds to estimate the relative sparsity, so that the estimation of the cascade channel can be completed without knowing the row sparsity and column sparsity of the cascade channel in the angular domain. It effectively overcomes the dependence of the existing DS-OMP algorithm on the relative channel sparsity, which makes the proposed algorithm more practical. Simulation results show that the performance of the ADS-OMP algorithm proposed in this paper is consistent with that of the existing DS-OMP algorithm. Both algorithms are on the same order of the complexity, and the complexity of the ADS-OMP algorithm is slightly increased.