2023/08/02 by Wei Li, Zihuai Lin, Li, W. +5
Computer Science · Engineering · #Advanced Wireless Communication Technologies #Antenna Design and Analysis #Cooperative Communication and Network Coding #FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2308.01425
openalex publication_date 2023/08/02 · openalex created_date 2023/08/18 · openalex updated_date 2026/07/28
As an emerging communication auxiliary technology, reconfigurable intelligent surface (RIS) is expected to play a significant role in the upcoming 6G networks. Due to its total reflection characteristics, it is challenging to implement conventional channel estimation algorithms. This work focuses on RIS-assisted MIMO communications. Although many algorithms have been proposed to address this issue, there are still ample opportunities for improvement in terms of estimation accuracy, complexity, and applicability. To fully exploit the structured sparsity of the multiple-input-multiple-output (MIMO) channels, we propose a new channel estimation algorithm called unitary approximate message passing sparse Bayesian learning with partial common support identification (UAMPSBL-PCI). Thanks to the mechanism of PCI and the use of UAMP, the proposed algorithm has a lower complexity while delivering enhanced performance relative to existing channel estimation algorithms. Extensive simulations demonstrate its excellent performance in various environments.