From latency bottlenecks to seamless edge: AD3PG-powered joint optimization of UAV trajectory and task offloading

Abstract

This paper addresses latency challenges in cloud-based task offloading caused by geographical server-user disparities by integrating Unmanned Aerial Vehicles (UAVs) with Mobile Edge Computing (MEC). We propose a dynamic UAV-assisted framework that optimizes real-time parameter adjustments to minimize system latency. Key challenges include joint task offloading, UAV trajectory optimization, and User Equipment (UE) occlusion detection under mobility constraints. To resolve these, we transform the problem into a Markov Decision Process (MDP) and develop an enhanced Adaptive Delayed Deep Deterministic Policy Gradient (AD3PG) algorithm, which improves upon DDPG by incorporating delayed updates and neural network tuning. The algorithm dynamically optimizes three critical aspects: UAV-UE connectivity establishment, occlusion-aware dual noise power configuration, and adaptive task offloading ratios. Extensive simulations demonstrate AD3PG’s superiority over baselines such as DDPG and Twin-Delayed DDPG (TD3) in reducing total system latency by 13.2–35.3 % under dynamic scenarios (e.g., 8 UEs with 100MB total task volume). Specifically, AD3PG achieves a task completion delay of 83.5–88 s across varying UE quantities, outperforming DDPG (89–115 s) and TD3 (87–95 s). These results validate the proposed framework’s efficacy for latency-sensitive applications in UAV-MEC systems.