Real-Time and Security-Aware Precoding in RIS-Empowered Multi-User Wireless Networks

TL;DR

This paper introduces DUNet, a deep-unfolding framework that enhances secrecy rate in RIS-enabled multi-user wireless networks by combining optimization techniques with deep learning for faster, accurate precoding.

Contribution

It presents a novel deep-unfolding-based approach for secure precoding in RIS networks, integrating AO and KKT conditions to improve speed and performance.

Findings

DUNet achieves similar accuracy to AO-based solutions.

DUNet is approximately 25.6 times faster than traditional AO methods.

The framework effectively maximizes secrecy rate in RIS-empowered networks.

Abstract

In this letter, we propose a deep-unfolding-based framework (DUNet) to maximize the secrecy rate in reconfigurable intelligent surface (RIS) empowered multi-user wireless networks. To tailor DUNet, first we relax the problem, decouple it into beamforming and phase shift subproblems, and propose an alternative optimization (AO) based solution for the relaxed problem. Second, we apply Karush-Kuhn-Tucker (KKT) conditions to obtain a closed-form solutions for the beamforming and the phase shift. Using deep-unfolding mechanism, we transform the closed-form solutions into a deep learning model (i.e., DUNet) that achieves a comparable performance to that of AO in terms of accuracy and about 25.6 times faster.