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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

External departments

  • Central South University
  • China University of Mining and Technology
  • University of Glasgow
  • University of Essex
  • University of Electronic Science and Technology of China

Details

Original languageEnglish
Title of host publication2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages741-746
Number of pages6
ISBN (Electronic)9781538677476
ISBN (Print)9781538677483
DOIs
Publication statusE-pub ahead of print - 22 Jul 2019
Event15th International Wireless Communications and Mobile Computing Conference: Connecting the IoT - Kenzi Solazur Hotel, Tangier, Morocco
Duration: 24 Jun 201928 Jun 2019
http://iwcmc.org/2019/

Conference

Conference15th International Wireless Communications and Mobile Computing Conference
Abbreviated titleIWCMC 2019
CountryMorocco
CityTangier
Period24/06/1928/06/19
Internet address
Publication type

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

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