Multi-UAV Path Planning for Mobile Edge Computing With High-Density Mobile Devices

This paper addresses the challenges of unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) in high-density user mobility scenarios, a field that has not been extensively explored in current research. We introduce a novel deep reinforcement learning (DRL) framework, named “j-PPO+EN-ConvNTM”, specifically designed to optimize MEC performance in urban environments with user mobility. The framework integrates spatiotemporal data modeling and spatial transformer network (STN) through an enhance Convolution Neural Turing Machine (EN-ConvNTM) module, which includes a three-dimensional external memory. It also features a joint continuous and discrete action decision-making module, termed joint proximal policy optimization (j-PPO). This design enables effective handling of the dynamic and complex nature of urban mobility patterns. The proposed approach extends the PPO technique to accommodate joint continuous and discrete action decisions, thereby enhancing UAV adaptability and efficiency in providing MEC services. Extensive simulations demonstrate significant improvements over all baseline models, particularly in terms of equilibrium efficiency and service continuity in high-density scenarios. Our research addresses a critical gap in existing UAV-assisted MEC studies, which primarily focus on static or low-mobility user scenarios, and supports the development of more robust and efficient smart city applications, meeting the real-world demands of modern urban infrastructures.