Gu, Huixian;
Zhao, Liqiang;
Liang, Kai;
Zheng, Gan;
Wong, Kai-Kit;
Chae, Chan-Byoung;
(2025)
Task Offloading and Position Optimization for Large Scale Unmanned Aerial Vehicle Networks: A Mean Field Learning Approach.
IEEE Transactions on Cognitive Communications and Networking
10.1109/TCCN.2025.3641515.
(In press).
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Abstract
Unmanned aerial vehicle (UAV) networks have emerged as promising enablers in sixth generation (6G) communication system because they can support delay-sensitive and energy-constrained applications. However, the limited resources of UAVs and the high computational complexity of traditional methods complicate task offloading and position optimization. At scale, the task offloading and position optimization decisions yield non-stationary interactions among many agents, while standard multi-agent deep reinforcement learning (MADRL) suffers from poor scalability as the joint action space grows exponentially with the number of UAVs. We formulate joint task offloading and 2D position control as a Markov game that minimizes a weighted energy-delay cost per UAV under practical flight constraints (finite horizontal range, collision avoidance, and an elevation-angle limit) and resource constraints. We then develop a mean-field actor-critic (MFAC) framework that aggregates neighbors’ influence into a mean action and conditions both the actor and the critic on local observations and the mean action. By approximating the interactions among a large number of agents through aggregating the influence of others into a mean action representation, the input dimensionality of the critic part is reduced from M+KP to M+2P, yielding an approximately K-fold reduction and becoming independent of the agent population size compared to traditional MADRL methods. Numerical results demonstrate that our proposed algorithm can achieve an 80% reduction in the number of episodes, a 70% reduction in training time, a 38% reduction in energy consumption and a 28% reduction in task delay compared to state-of-the-art approaches, particularly under large-scale UAV deployment scenarios.
| Type: | Article |
|---|---|
| Title: | Task Offloading and Position Optimization for Large Scale Unmanned Aerial Vehicle Networks: A Mean Field Learning Approach |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TCCN.2025.3641515 |
| Publisher version: | https://doi.org/10.1109/TCCN.2025.3641515 |
| Language: | English |
| Additional information: | This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Autonomous aerial vehicles , Optimization , Trajectory , Servers , Resource management , Heuristic algorithms , Delays , Games , Trajectory planning , Training |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10219522 |
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