A Machine Learning-Based Hybrid Approach for Safeguarding VLC-Enabled Drone Systems

This paper explores the physical layer security performance of collaborative drone fleets enabled by visible light communication (VLC) in a multi-eavesdropper scenario, where multiple drones leverage VLC to serve terrestrial users. To strengthen system security, we formulate a sum worst-case secrecy rate maximization problem. To address the non-convex optimization challenge of this problem, we develop two innovative Q-learning-based position decision algorithms (Q-PDA and Q-PDA-lite) with a dynamic reward mechanism, allowing drones to adaptively optimize their positions. Additionally, we propose an enhanced Tabu Search-based grouping algorithm (TS-GA) to establish the suboptimal user equipment (UE)–drone association by balancing candidate solution exploration and tabu constraint exploitation. Simulation results demonstrate that the proposed Q-PDA and Q-PDA-lite achieve worst-case secrecy rates significantly exceeding those of Random-PDA and K-means-PDA. While Q-PDA-lite exhibits 2% lower performance than Q-PDA, it offers reduced complexity. Additionally, the proposed TS-GA achieves a worst-case secrecy rate that substantially outperforms random grouping, UE-channel-gain-based grouping, and channel-gain-based grouping. Collectively, the hybrid approach integrating Q-PDA and TS-GA achieves 10% near-global optimality with guaranteed convergence, while preserving computational efficiency. Furthermore, this hybrid approach outperforms other combinations in terms of security metrics.