  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.
