# Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairments

arXiv: https://arxiv.org/abs/2107.07423 · HTML: https://mlsanity.com/paper/2107.07423/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 13 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks](https://mlsanity.com/paper/1810.03982/) (2018)
- [Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial](https://mlsanity.com/paper/2007.02759/) (2020)
- [Massive MIMO Channel Estimation with an Untrained Deep Neural Network](https://mlsanity.com/paper/1908.00144/) (2019)
- [Deep Channel Learning For Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems](https://mlsanity.com/paper/2001.11085/) (2020)
- [Rayleigh Fading Modeling and Channel Hardening for Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2009.04723/) (2020)
- [Intelligent Reflecting Surface-Enhanced OFDM: Channel Estimation and Reflection Optimization](https://mlsanity.com/paper/1909.03272/) (2019)
- [An Optimal Channel Estimation Scheme for Intelligent Reflecting Surfaces based on a Minimum Variance Unbiased Estimator](https://mlsanity.com/paper/1909.09440/) (2019)

## Built on by (most cited first, 1 of 1)

- [A CNN-based End-to-End Learning for RIS-assisted Communication System](https://mlsanity.com/paper/2503.13976/) (2025)

## Abstract (arXiv)

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.