Enhancing PLS of Indoor IRS-VLC Systems for Colluding and Non-Colluding Eavesdroppers

2025-12-22

AI Summary

To address physical-layer security threats posed by colluding and non-colluding eavesdroppers in indoor visible light communication (VLC) systems, this paper proposes, for the first time, leveraging the inherent time-delay characteristics of intelligent reflecting surfaces (IRSs) to enhance secrecy capacity. Specifically, we jointly optimize IRS element allocation and delay responses to achieve constructive signal superposition at legitimate users while inducing inter-symbol interference (ISI) at eavesdroppers. We formulate a secrecy capacity maximization framework tailored to multi-eavesdropper scenarios—encompassing both cooperative and non-cooperative eavesdropping—and integrate IRS channel modeling, wideband delay analysis, and deep reinforcement learning (using the Proximal Policy Optimization algorithm) for solution. Under worst-case channel conditions, our approach improves secrecy capacity by 107% and 235% over a full-resource-allocation baseline for cooperative and non-cooperative eavesdropping scenarios, respectively.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search
• Computer Vision: Learning & Optimization for CV

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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, and non-colluding eavesdroppers, which act independently. Simulation results are shown for various simulation setups, which demonstrate significant secrecy capacity gains. In a worst-case scenario, where the eavesdroppers have stronger channels than the legitimate user, the proposed PPO-based IRS allocation improves secrecy capacity by 107% and 235% in the colluding and non-colluding cases, respectively, compared with allocating all IRS elements to the legitimate user. These results demonstrate that time-delay-based IRS control can provide a strong secrecy advantage in practical indoor VLC scenarios.

Problem

• Enhances physical layer security in IRS-VLC systems against eavesdroppers.
• Maximizes secrecy capacity via deep reinforcement learning for IRS allocation.
• Addresses time delays in IRS reflections for practical wideband VLC.

Innovation

• Using IRS-induced time delays to enhance physical layer security
• Applying deep reinforcement learning with PPO for IRS allocation
• Optimizing secrecy capacity for both colluding and non-colluding eavesdroppers