University of Oulu

N. Ginige, K. B. Shashika Manosha, N. Rajatheva and M. Latva-aho, "Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairments," 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2021, pp. 561-566, doi: 10.1109/PIMRC50174.2021.9569694

Untrained DNN for channel estimation of RIS-assisted multi-user OFDM system with hardware impairments

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Author: Ginige, Nipuni1; Manosha, K. B. Shashika1; Rajatheva, Nandana1;
Organizations: 1Center for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2022-02-09


Reconfigurable intelligent surface (RIS) is an emerging technology for improving performance in fifth-generation (5G) and beyond networks. Practically channel estimation of RIS-assisted systems is challenging due to the passive nature of the RIS. The purpose of this paper is to introduce a deep learning-based, low complexity channel estimator for the RIS-assisted multi-user single-input-multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system with hardware impairments. We propose an untrained deep neural network (DNN) based on the deep image prior (DIP) network to denoise the effective channel of the system obtained from the conventional pilot-based least-square (LS) estimation and acquire a more accurate estimation. We have shown that our proposed method has high performance in terms of accuracy and low complexity compared to conventional methods. Further, we have shown that the proposed estimator is robust to interference caused by the hardware impairments at the transceiver and RIS.

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Series: IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications workshops
ISSN: 2166-9570
ISSN-E: 2166-9589
ISSN-L: 2166-9570
ISBN: 978-1-7281-7586-7
ISBN Print: 978-1-7281-7587-4
Pages: 561 - 566
DOI: 10.1109/PIMRC50174.2021.9569694
Host publication: 32nd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2021
Conference: IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported by the Academy of Finland 6Genesis Flagship (grant no. 318927).
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
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