Amplitude Correlation and Structured Sparsity Inspired Compressed Sensing for Channel Estimation in RIS-Aided MU-MISO Systems

作者：Weijie Jin, Jing Zhang, Chao-Kai Wen, Shi Jin · 发表于：IEEE Transactions on Wireless Communications · 年份：2025 · DOI：10.1109/twc.2025.3592787 · 被引用次数：3 · 研究领域：Sparse and Compressive Sensing Techniques、Blind Source Separation Techniques、Advanced MEMS and NEMS Technologies

Reconfigurable intelligent surfaces (RISs) enhance communication performance by adjusting the propagation directions of incident signals. However, joint beamforming design requires the acquisition of channel state information, often leading to significant pilot overhead in RIS-assisted systems, particularly when the number of reflective elements is large. In this study, we analyze the characteristics of the cascaded channel and propose a method that combines amplitude correlation with existing structured sparsity. Leveraging these characteristics, we first derive an on-grid channel estimation method, demonstrating the effectiveness of incorporating additional characteristics in cascaded channel estimation. We then extend the proposed algorithm to off-grid channel estimation by refining the coarsely estimated channel using alternating optimization and gradient descent. Furthermore, we adapt the algorithm to enhance estimation accuracy with the support of digital twin (DT) technology, utilizing a few pilots to refine the channel generated by DT. Simulation results show up to a 5 dB improvement in normalized mean squared error compared to state-of-the-art channel estimation algorithms that employ structured sparsity. Additionally, with DT assistance, the proposed algorithm achieves nearly a two-fold performance improvement over traditional algorithms that do not incorporate amplitude correlation and structured sparsity.