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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

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Author: Dampahalage, Dilin1; Shashika Manosha, K. B.1; Rajatheva, Nandana1;
Organizations: 1Centre for Wireless Communications, Univeristy of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022091959499
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-09-19
Description:

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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Series: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
ISSN: 1520-6149
ISSN-E: 2379-190X
ISSN-L: 1520-6149
ISBN: 978-1-6654-0540-9
ISBN Print: 978-1-6654-0541-6
Pages: 8827 - 8831
DOI: 10.1109/ICASSP43922.2022.9746793
OADOI: https://oadoi.org/10.1109/ICASSP43922.2022.9746793
Host publication: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conference: 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
Subjects:
Intelligent reflecting surfaces
channel estimation
mmWave communications
sparse recovery
supervised learning
Article
Funding: This work was supported by the Academy of Finland 6Genesis Flagship (grant 318927).
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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