Deep-Learning-Aided Detection for Reconfigurable Intelligent Surfaces

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....

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Bibliographic Details
Published in:arXiv.org 2020-07
Main Authors: Khan, Saud, Khan, Komal S, Haider, Noman, Soo Young Shin
Format: Article
Language:English
Subjects:
Angle of reflection
Bit error rate
Deep learning
Error detection
Reconfigurable intelligent surfaces
Symbols
Online Access:Get full text
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Summary: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.
ISSN:2331-8422