2022/02/24 by Dilin Dampahalage, K. B. Shashika Manosha, Dampahalage, Dilin +5 · 1 citation
Engineering · #Indoor and Outdoor Localization Technologies #Advanced Wireless Communication Technologies #Millimeter-Wave Propagation and Modeling
paper · pdf · doi:10.48550/arxiv.2202.11997
An reconfigurable intelligent surface (RIS) can be used to establish line-of-sight (LoS) communication when the direct path is compromised, which is a common occurrence in a millimeter wave (mmWave) network. In this paper, we focus on the uplink channel estimation of a such network. We formulate this as a sparse signal recovery problem, by discretizing the angle of arrivals (AoAs) at the base station (BS). On-grid and off-grid AoAs are considered separately. In the on-grid case, we propose an algorithm to estimate the direct and RIS channels. Neural networks trained based on supervised learning is used to estimate the residual angles in the off-grid case, and the AoAs in both cases. Numerical results show the performance gains of the proposed algorithms in both cases.