Deep-Learning-Aided Detection for Reconfigurable Intelligent Surfaces

TL;DR

This paper introduces a deep learning-based method for symbol detection in RIS-assisted communications that eliminates the need for pilot signals, reducing overhead and improving bit-error rate performance.

Contribution

It proposes a novel DL network that estimates channels and detects symbols in RIS systems without pilot signaling, enhancing efficiency and accuracy.

Findings

Outperforms traditional detectors in bit-error rate

Eliminates pilot signaling overhead

Effective in RIS-assisted communication scenarios

Abstract

This paper presents a deep learning (DL) approach for estimating and detecting symbols in signals transmitted through reconfigurable intelligent surfaces (RIS). The proposed network utilizes fully connected layers to estimate channels and phase angles from a reflected signal received through an RIS. Because the proposed network can estimate and detect symbols without any pilot signaling, this method reduces the overhead required for transmission. The improvements achieved by this method are quantified in terms of the bit-error rate, outperforming traditional detectors.