Title: DDPG-based joint energy and offloading optimisation in UAV-aided mobile edge computing

Authors: Chuangchun Qin; Yongzhi Ran; Fei Wang; Junwei Luo

Addresses: The College of Artificial Intelligence and Big Data, Chongqing Polytechnic University of Electronic Technology, Chongqing, China ' The College of Electronic and Information Engineering, Southwest University, Chongqing, China ' The College of Electronic and Information Engineering, Southwest University, Chongqing, China ' China Mobile Group Chongqing Co., Ltd., Chongqing, China

Abstract: In this paper, we consider an unmanned aerial vehicles (UAV)-aided mobile edge computing (MEC) system, where a fixed-wing UAV provides computation resources for terminal devices (TDs). The UAV can effectively establish line-of-sight communication links with TDs. However, the limitations of energy capacity and transmission coverage of UAV and TDs are still challenges in UAV-aided MEC. The energy capacity affects the service lifetime of the UAV-aided MEC and the transmission coverage has an impact on the quality of service. To address these issues, we study a joint energy and offloading optimisation problem, where we aim to minimise the energy consumption of UAV and maximise the total offloaded data volume of TDs by optimising TDs' transmission power and UAV's flight angle and speed. We propose a deep deterministic policy gradient (DDPG)-based algorithm to solve this problem. Simulation results show that our proposed algorithm has good convergence and is better than other algorithms.

Keywords: mobile edge computing; MEC; unmanned aerial vehicles; UAV; energy consumption; offloaded data volume; flight trajectory; deep deterministic policy gradient; DDPG.

DOI: 10.1504/IJAHUC.2026.155455

International Journal of Ad Hoc and Ubiquitous Computing, 2026 Vol.52 No.3, pp.135 - 144

Received: 31 Mar 2025
Accepted: 30 Jun 2025
Published online: 03 Aug 2026 *

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