Toward Low-Delay and Energy-Efficient UAV-Assisted MEC Systems Through Intelligent Resource Allocation

Abstract

Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) has emerged as a promising paradigm for supporting computation-intensive and delay-sensitive applications. However, efficient task offloading and resource allocation remain a challenging problem due to the need to jointly minimize the maximum processing delay and energy consumption of User Device (UD) in dynamic environments. Existing solutions often suffer from training instability, limited exploration capabilities, and slow convergence, limiting their ability to achieve optimal task offloading and resource allocation decisions. To address these challenges, this paper proposes a Prioritized Adaptive Weighting based on Deep Deterministic Policy Gradient (PAW-DDPG) as an enhanced Deep Deterministic Policy Gradient (DDPG) algorithm to minimize both processing delay and energy consumption by jointly optimizing user scheduling, partial-task offloading, and UAV trajectory. The proposed algorithm introduces a state-aware normalization mechanism to stabilize the learning process, a new pre-training initialization technique that populates the Experience Replay Buffer (ERB) before learning to accelerate convergence and improve policy quality, and a hybrid noise exploration scheme that enhances exploration efficiency. Furthermore, to achieve an effective balance between delay and energy consumption, a novel adaptive weighting mechanism based on a modified Exponential Moving Average (EMA) algorithm is proposed. Simulation results demonstrate that the proposed PAW-DDPG algorithm outperforms DDPG and all baseline algorithms, achieving performance gains over DDPG of 4–18%, 9–18%, 8–26%, and 7–18% under varying task sizes, UAV computing capabilities, user device computing capabilities, and numbers of user devices, respectively.