# Deep-Learning-Aided Detection for Reconfigurable Intelligent Surfaces

arXiv: https://arxiv.org/abs/1910.09136 · HTML: https://mlsanity.com/paper/1910.09136/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.03793/) (2022)
- [Deep Learning Based RIS Channel Extrapolation with Element-grouping](https://mlsanity.com/paper/2105.06850/) (2021)

## Abstract (arXiv)

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.