Deep Reinforcement Learning-Based Joint Trajectory Design and Resource Allocation for Secure and Energy-Efficient UAV Networks

Unmanned Aerial Vehicles (UAVs) have been extensively used recently for wireless networks. However, such networks encounter several challenges that remain unsolved. In this paper, we address the issue of joint optimization of trajectory design and resource allocation in UAV-based wireless networks in the presence of eavesdroppers. We first formulate an optimization problem with the objective to maximize a utility function defined in terms of secrecy rate, energy utilization efficiency, and interference. Due to the high dimensionality and non-convex nature of the formulated problem, we propose a Proximal Policy Optimization (PPO)-based Deep Reinforcement Learning (DRL) algorithm to solve the problem and learn the environment. Our proposed PPO algorithm solves the problem by jointly controlling the 3D position of UAVs, power, and energy harvesting. Simulation results demonstrate the efficiency of the proposed algorithm in solving the problem, learning the environment dynamics, and its superiority over some existing conventional and DRL-based methods.