RL-Based User Association and Resource Allocation for Multi-UAV enabled MEC

Wang, L., Huang, P., Wang, K., Zhang, G., Zhang, L. , Aslam, N. and Yang, K. (2019) RL-Based User Association and Resource Allocation for Multi-UAV enabled MEC. In: 15th International Wireless Communication and Mobile Computing Conference (IWCMC 2019), Tangier, Morocco, 24-28 Jun 2019, ISBN 9781538677483 (doi: 10.1109/IWCMC.2019.8766458)

Abstract

In this paper, multi-unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC), i.e., UAVE is studied, where several UAVs are deployed as flying MEC platform to provide computing resource to ground user equipments (UEs). Compared to the traditional fixed location MEC, UAV enabled MEC (i.e., UAVE) is particular useful in case of temporary events, emergency situations and on-demand services, due to its high flexibility, low cost and easy deployment features. However, operation of UAVE faces several challenges, two of which are how to achieve both 1) the association between multiple UEs and UAVs and 2) the resource allocation from UAVs to UEs, while minimizing the energy consumption for all the UEs. To address this, we formulate the above problem into a mixed integer nonlinear programming (MINLP), which is difficult to be solved in general, especially in the large-scale scenario. We then propose a Reinforcement Learning (RL)-based user Association and resource Allocation (RLAA) algorithm to tackle this problem efficiently and effectively. Numerical results show that the proposed RLAA can achieve the optimal performance with comparison to the exhaustive search in small scale, and have considerable performance gain over other typical algorithms in large-scale cases.

• Item Type: Conference Proceedings
• Additional Information: This work was supported in part by the Zhongshan City Team Project (Grant No. 180809162197874), National Natural Science Foundation of China (Grant No. 61620106011 and 61572389) and UK EPSRC NIRVANA project (Grant No. EP/L026031/1).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhang, Professor Lei
• Authors: Wang, L., Huang, P., Wang, K., Zhang, G., Zhang, L., Aslam, N., and Yang, K.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• ISSN: 2376-6492
• ISBN: 9781538677483
• Copyright Holders: Copyright © 2019 IEEE
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher