Supervised Learning Based Sparse Channel Estimation For RIS Aided Communications
Dilin Dampahalage, K. B. Shashika Manosha, Nandana Rajatheva, Matti Latva-aho
Abstract
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 formu-late 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.
BibTeX
@inproceedings{icassp2022_supervisedlearni,
title = {Supervised Learning Based Sparse Channel Estimation For RIS Aided Communications},
author = {Dilin Dampahalage and K. B. Shashika Manosha and Nandana Rajatheva and Matti Latva-aho},
booktitle = {ICASSP 2022},
year = {2022}
}