Secure UAV-IRS Communication: A Hybrid Genetic Algorithms and Gradient Descent Approach

In the dynamic realm of wireless communication, Unmanned Aerial Vehicles (UAVs) have gained increasing prominence due to their exceptional capabilities, which include expensive coverage of large areas and access to challenging and hazardous locations. When employed as mobile base stations or relays, UAVs have shown remarkable enhancements in system throughput and reliability. In addition, Intelligent Reflecting Surfaces (IRS) present a very low cost solution that efficiently enhances wireless communication quality using passive modulation arrays. Nevertheless, the use of UAVs for malicious intents can also introduce heightened security challenges such as communication eavesdropping. In response to these challenges, we present in this paper, a communication framework that incorporates a UAV equipped with an adaptive IRS aiming to enhance the communication secrecy between the Base Station (BS) and Bob in the presence of several UAV eavesdroppers. We formulate the objective as an optimization problem that aims to maximize the secrecy rate while considering the mobility constraints. To address this complex non-convex problem, our innovative solution harnesses a hybrid approach that combines Genetic Algorithms and Gradient Descent techniques, resulting in an efficient computation of suboptimal reflection angles and UAV trajectories for IRS-equipped UAVs towards a more secure communication.