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, autonomous aerial vehicles (AAVs)-assisted mobile-edge computing (MEC) systems have emerged as a promising solution by providing power and computing resources to user devices (UDs). This article introduces a multicluster fair service AAV-assisted wireless powered MEC system aimed at addressing the challenges between energy efficiency and fair service. We formulate a long-term multiobjective energy efficiency optimization problem (LMEEOP) to tradeoff UD energy consumption, AAV 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 subproblems: 1) AAV flight trajectory design; 2) offloading decision generation; and 3) 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 AAV flight trajectory and offloading decision are combined to achieve efficient resource allocation. The proposed approach provides a robust solution for improving multicluster fair service and energy efficiency optimization in AAV-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.