Energy-Efficient Joint Trajectory and Reflecting Design in IRS-Enabled UAV Edge Computing

Intelligent Reflecting Surface (IRS) enabled Unmanned Aerial Vehicle (UAV) edge computing, a new communication technology, can provide sufficient capacity for edge computing system. However, due to the Line-of-Sight (LoS) or the Non Line of Sight (NLoS) of communicating environments will impact transmitting rate or delay, the Intelligent Reflective Surface (IRS) can be utilized to compensate the channel fading in the IRS-enabled UAV edge computing. In this paper, the joint problem of IRS phase shift, UAV trajectory and power allocation in the system is investigated, aiming to maximize the energy efficient. The corresponding optimization problem, which consists of mixed integer nonlinear programming problem, is formulated. To solve the problem, the original problem is decomposed into two subproblems, and an iterative method framework based on ConVex optimization and Deep Reinforcement Learning (CV-DRL) is proposed. Given the UAV trajectory and IRS phase shift, the Convex optimization algorithm is used to solve the power allocation schemes. Then, given the power allocation schemes, the Double Deep Q Network (Double DQN) and Deep Deterministic Policy Gradient (DDPG) algorithms are utilized to solve the problem of optimal UAV trajectory and IRS phase shift. The simulation results demonstrate that our proposed method outperforms other schemes in terms of energy efficiency, providing significant enhancements