Enhancing PLS of Indoor IRS-VLC Systems for Colluding and Non-Colluding Eavesdroppers
Rashid Iqbal, Ahmed Zoha, Salama Ikki, Muhammad Ali Imran, and Hanaa Abumarshoud

TL;DR
This paper introduces a deep reinforcement learning approach to optimize IRS element allocation in indoor VLC systems, considering realistic time delays, to significantly enhance physical layer security against colluding and non-colluding eavesdroppers.
Contribution
It models IRS-induced time delays in indoor VLC and develops a PPO-based optimization method for secrecy capacity maximization, addressing both colluding and non-colluding eavesdroppers.
Findings
Secrecy capacity improves by over 100% in worst-case scenarios.
PPO-based IRS allocation outperforms all-IRS-element allocation.
Time-delay-aware IRS control enhances security in practical VLC environments.
Abstract
Most intelligent reflecting surface (IRS)-aided indoor visible light communication (VLC) studies ignore the time delays introduced by reflected paths, even though these delays are inherent in practical wideband systems. In this work, we adopt a realistic assumption of IRS-induced time delay for physical layer security (PLS) enhancement. We consider an indoor VLC system where an IRS is used to shape the channel so that the reflected signals add constructively at the legitimate user and create intersymbol interference at eavesdroppers located inside the coverage area. The resulting secrecy capacity maximisation over the IRS element allocation is formulated as a complex combinatorial optimisation problem and is solved using deep reinforcement learning with proximal policy optimisation (PPO). The approach is evaluated for both colluding eavesdroppers, which combine their received signals,…
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Taxonomy
TopicsOptical Wireless Communication Technologies · Advanced Wireless Communication Technologies · PAPR reduction in OFDM
