Supervised learning based sparse channel estimation for RIS aided communications
Dampahalage, Dilin; Shashika Manosha, K. B.; Rajatheva, Nandana; Latva-Aho, Matti (2022-04-27)
D. Dampahalage, K. B. Shashika Manosha, N. Rajatheva and M. Latva-Aho, "Supervised Learning Based Sparse Channel Estimation For RIS Aided Communications," ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 8827-8831, doi: 10.1109/ICASSP43922.2022.9746793.
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https://urn.fi/URN:NBN:fi-fe2022091959499
Tiivistelmä
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.
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