This paper investigates a covert visible light communication (VLC) system, where a transmitter (Alice) aims to send confidential information to a legitimate user (Bob) while avoiding being detected by an unauthorized warden (Willie). To achieve this, we propose an optimization framework that controls both reflection by mirror array-based reconfigurable intelligent surface (mRIS) and refraction by liquid crystal-based RIS (LC-RIS) to reconstruct a desired covert optical transmission environment. Considering the covertness constraint with the strongest Willie aided by LC-RIS, we jointly optimize the rotation configuration of mRIS, the parameter of Bob's LC-RIS, and the average optical intensity of Alice by a proposed stepwise decoupling method. The simulation results show that the deployment of LC-RIS on Bob's receiver has a more significant impact on enhancing covert VLC performance compared to the deployment of mRIS.

In next-generation wireless networks, providing secure transmission and delay guarantees are two critical goals. However, either of them requires a concession on the transmission rate. In this paper, we consider a visible light network consisting of multiple access points and multiple users. Our first objective is to mathematically evaluate the achievable rate under constraints on delay and security. The second objective is to provide a cell formation with customized statistical delay and security guarantees for each user. First, we propose a user-centric design called secure cell formation, in which artificial noise is considered, and flexible user scheduling is determined. Then, based on the effective capacity theory, we derive the statistical-delay-constrained secrecy rate and formulate the cell formation problem as a stochastic optimization problem (OP). Further, based on the Lyapunov optimization theory, we transform the stochastic OP into a series of evolutionary per-slot drift-plus-penalty OPs. Finally, a modified particle swarm optimization algorithm and an interference graph-based user-centric scheduling algorithm are proposed to solve the OPs. We obtain a dynamic independent set of scheduled users as well as secure cell formation parameters. Simulation results show that the proposed algorithm can achieve a better delay-constrained secrecy rate than the existing cell formation approaches.

Traditional wireless positioning techniques often suffer from accuracy degradation in indoor environments due to multipath effects and occlusion. To address this issue, this paper proposes an indoor positioning method for visible light communication (VLC) combined with intelligent reflective surface (IRS) assistance to improve the positioning accuracy and stability in complex environments. This work proposes the concepts of a virtual source and virtual receiver based on IRS and conducts positioning optimization by combining the measurements of the time difference of arrival (TDOA) and angle of arrival (AOA). The research adopts a semi-positive definite relaxation (SDR) optimization method to efficiently solve the nonlinear optimization problem, ensuring the global convergence and accuracy of the algorithm. Meanwhile, the weights of the positioning results of the virtual light source and the real light source are dynamically adjusted by using the distance residual, thereby reducing the influence of measurement noise. Monte Carlo simulation experiments demonstrate the advantages of the proposed method in terms of the positioning error and robustness compared with traditional positioning algorithms in the environment of large noise interference. The experimental results demonstrate the efficacy of the method in addressing multipath and occlusion issues, while also exhibiting notable adaptability and stability across diverse hardware configurations.

This letter addresses the physical-layer security (PLS) challenge in multi-input multi-output (MIMO) visible light communication (VLC) systems. Existing solutions often overlook VLC-specific constraints and fail to jointly optimize key parameters. To overcome this, we propose an optical intelligent reflecting surface (OIRS)-assisted tri-level optimization framework that simultaneously handles power allocation, OIRS assignment, and orientation control. A block coordinate descent approach incorporating convex approximation, greedy heuristics, and metaheuristic search is developed. Simulation results confirm significant secrecy rate improvement and robustness under various conditions and imperfect channel knowledge.