Multiobjective Optimization of Energy Efficiency and Fairness in AAV-Assisted Wireless Powered MEC Systems: A DRL-Based Approach

With the increasing demand for real-time data processing in disaster relief and remote areas, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) systems have emerged as a promising solution by providing power and computing resources to user devices (UDs). This paper introduces a multi-cluster fair service UAV-assisted wireless powered MEC system aimed at addressing the challenges between energy efficiency and fair service. We formulate a long-term multi-objective energy efficiency optimization problem (LMEEOP) to trade off UD energy consumption, UAV energy consumption, and offline rate. However, this problem is a mixed integer nonlinear programming problem and difficult to solve. Thus, we propose a trajectory limited online joint optimization approach (TLOJOA), which decomposes LMEEOP into three sub-problems: UAV flight trajectory design, offloading decision generation, and resource allocation. Specifically, a Self-Attention combined proximal policy optimization (PPO) algorithm captures long-range dependencies to balance energy consumption and offline rate. Additionally, a fast offloading strategy generation algorithm with offloading importance ranking is proposed to improve UD energy efficiency. Finally, the UAV flight trajectory and offloading decision are combined to achieve efficient resource allocation. The proposed approach provides a robust solution for improving multi-cluster fair service and energy efficiency optimization in UAV-assisted MEC systems, especially for mission-critical scenarios with constrained resources. Simulation results validate that the proposed approach can achieve outstanding system performance compared to other benchmark approaches, especially in fairness of service.