Energy Efficiency Optimization of Aerial IRS-Assisted UAV-MEC Systems

Mobile Edge Computing (MEC) powered by Unmanned Aerial Vehicles (UAVs) can provide ubiquitous communication and computing services for the Internet of Things (IoT). Integrating Intelligent Reflecting Surface (IRS) with UAVs can improve network service performance. In this paper, an energy efficiency optimization algorithm for aerial IRS assisted UAV-MEC networks is proposed to maximize the energy efficiency of the system by jointly optimizing the transmit power, computational resources, IRS phase shift, and UAV flight trajectory. Because the problem is a non-convex fraction, and there are complex coupling relations among the variables. Therefore, an iterative algorithm with a dual-loop structure is used to solve the problem, where the outer loop is based on the Dinkelbach method and the inner loop decomposes the problem into three sub-problems. The simulation results show that the proposed algorithm can balance the calculation rate and energy consumption, has good convergence, and is better than other comparison schemes.