Active 3D Double-RIS-Aided Multi-User Communications: Two-Timescale-Based Separate Channel Estimation via Bayesian Learning

Double-reconfigurable intelligent surface (RIS) is a promising technique, achieving substantial gain improvement compared to single-RIS techniques. However, in double-RIS-aided systems, accurate channel estimation more challenging than single-RIS-aided systems. This work solves the problem of double-RIS-based based on active RIS architectures with only one radio frequency (RF) chain. Since slow time-varying channels, i.e., BS-RIS 1, 2, and 1-RIS 2 can be obtained architectures, novel multi-user two-timescale protocol proposed minimize pilot overhead. First, we propose an uplink training scheme for estimation, which effectively address double-reflection problem. With channels’ sparisty, low-complexity Singular Value Decomposition Multiple Measurement Vector-Based Compressive Sensing (SVD-MMV-CS) framework line-of-sight (LoS)-aided off-grid MMV expectation maximization-based generalized approximate message passing (M-EM-GAMP) algorithm parameter recovery. For fast estimated large-timescale measurements-augmentation-estimate (MAE) developed decrease Additionally, comprehensive analysis overhead computing complexity conducted. Finally, simulation results demonstrate effectiveness our strategy Bayesian CS framework.