2022/11/29 by Songjie Yang, Yang, Songjie, Wanting Lyu +7 · 1 citation
Engineering · #Advanced Wireless Communication Technologies #FOS: Computer and information sciences #FOS: Electrical engineering #Indoor and Outdoor Localization Technologies #Information Theory (cs.IT) #Signal Processing (eess.SP) #Underwater Vehicles and Communication Systems #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2211.16183
openalex publication_date 2022/11/29 · openalex created_date 2022/12/11 · openalex updated_date 2026/07/28
Double-reconfigurable intelligent surface (RIS) is a promising technique, achieving a substantial gain improvement compared to single-RIS techniques. However, in double-RIS-aided systems, accurate channel estimation is more challenging than in single-RIS-aided systems. This work solves the problem of double-RIS-based channel estimation based on active RIS architectures with only one radio frequency (RF) chain. Since the slow time-varying channels, i.e., the BS-RIS 1, BS-RIS 2, and RIS 1-RIS 2 channels, can be obtained with active RIS architectures, a novel multi-user two-timescale channel estimation protocol is proposed to minimize the pilot overhead. First, we propose an uplink training scheme for slow time-varying channel estimation, which can effectively address the double-reflection channel estimation problem. With channels' sparisty, a low-complexity Singular Value Decomposition Multiple Measurement Vector-Based Compressive Sensing (SVD-MMV-CS) framework with the line-of-sight (LoS)-aided off-grid MMV expectation maximization-based generalized approximate message passing (M-EM-GAMP) algorithm is proposed for channel parameter recovery. For fast time-varying channel estimation, based on the estimated large-timescale channels, a measurements-augmentation-estimate (MAE) framework is developed to decrease the pilot overhead.Additionally, a comprehensive analysis of pilot overhead and computing complexity is conducted. Finally, the simulation results demonstrate the effectiveness of our proposed multi-user two-timescale estimation strategy and the low-complexity Bayesian CS framework.