Physical Layer Security for IRS-UAV-Assisted Cell-Free Massive MIMO Systems
Xuan-Toan Dang, Hieu V. Nguyen, Oh‐Soon Shin
Abstract
An intelligent reflecting surface (IRS) is a promising technology for future wireless communication. It comprises many hardware-efficient passive elements. The applications of unmanned aerial vehicles (UAVs) have expanded beyond military missions owing to their mobility, maneuverability, flexibility, ease of deployment, and cost-effectiveness. Combining IRS with UAVs, known as UAV-mounted IRSs (IRS-UAV), has gained significant attention owing to the unique advantages offered by both technologies. Wireless communication systems face critical challenges in physical layer security, particularly cell-free massive multiple-input multiple-output (MIMO) systems (CFMM). This study investigated physical layer security (PLS) in an IRS-UAV-assisted CFMM, involving multiple IRS-UAVs, access points, users, and passive eavesdroppers. To maximize the average secrecy downlink rate, this study proposes an optimization algorithm using deep reinforcement learning based on a deep deterministic policy gradient (DDPG) that achieves at least one locally optimal solution. However, this approach results in a relatively high computational complexity. A second Approach Is introduced to address this: an alternating optimization algorithm combining inner approximation (IA) methods and an advanced DDPG algorithm with a warm-up technique. The simulation results demonstrated the efficiency of both approaches in resolving complex optimization problems. Furthermore, the numerical findings confirmed that the proposed alternating optimization algorithm exhibited competitive performance and significantly reduced computational complexity compared with the DDPG-based approach.