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.
Supervised learning based sparse channel estimation for RIS aided communications
|Author:||Dampahalage, Dilin1; Shashika Manosha, K. B.1; Rajatheva, Nandana1;|
1Centre for Wireless Communications, Univeristy of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2022091959499
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2022-09-19
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.
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
|Pages:||8827 - 8831|
2022 IEEE International Conference on Acoustics, Speech, and Signal Processing : Proceedings
2022 IEEE International Conference on Acoustics, Speech, and Signal Processing
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported by the Academy of Finland 6Genesis Flagship (grant 318927).
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
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