RIS-Aided Covert Communications with Statistical CSI: A Multiport Network Theory Approach

Fuente: arXiv
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Autores principales: Abrardo, Andrea, Bartoli, Giulio
Formato: Preprint
Publicado: 2024
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author Abrardo, Andrea
Bartoli, Giulio
author_facet Abrardo, Andrea
Bartoli, Giulio
contents A novel framework for covert communications aided by Reconfigurable Intelligent Surfaces (RIS) is proposed. In this general framework, the use of multiport network theory for modelling the RIS consider various aspects that traditional RIS models in communication theory often overlook, including mutual coupling between elements and the impact of structural scattering. Moreover, the transmitter has only limited knowledge about the channels of the warden and the intended receiver. The proposed approach is validated through numerical results, demonstrating that communication with the legitimate user is successfully achieved while satisfying the covertness constraint.
format Preprint
id arxiv_https___arxiv_org_abs_2412_09085
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle RIS-Aided Covert Communications with Statistical CSI: A Multiport Network Theory Approach
Abrardo, Andrea
Bartoli, Giulio
Signal Processing
A novel framework for covert communications aided by Reconfigurable Intelligent Surfaces (RIS) is proposed. In this general framework, the use of multiport network theory for modelling the RIS consider various aspects that traditional RIS models in communication theory often overlook, including mutual coupling between elements and the impact of structural scattering. Moreover, the transmitter has only limited knowledge about the channels of the warden and the intended receiver. The proposed approach is validated through numerical results, demonstrating that communication with the legitimate user is successfully achieved while satisfying the covertness constraint.
title RIS-Aided Covert Communications with Statistical CSI: A Multiport Network Theory Approach
topic Signal Processing
url https://arxiv.org/abs/2412.09085