End-to-End Deep Learning IRS-assisted Communications Systems

Abstract

In this paper, we are re-modelling the intelligent reflecting surfaces (IRS) assisted communication systems using the auto-encoder (AE) deep learning (DL) technique to represent the classical IRS system as an end-to-end communication system. The cascaded channels from source to sink through the IRS have been transformed to a deep neural network (DNN) that learns how to reduce the wireless environment impairments effect by optimizing the representation of transmitted symbols. The proposed system design shows superior symbol error rate (SER) performance under the AWGN channel compared to both classical IRS and conventional AE end - to-end systems. The relation between improvement of performance and the capability of the proposed AE to learn optimized presentation for transmitted symbols is being explained through observing and comparing the baseline AE constellations learning with the ones that the proposed model learned.