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In this paper, multi-unmanned aerial vehicle (UAV) enabled mobile edgecomputing (MEC), i.e., UAVE is studied, where several UAVs are deployed asflying 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 situationsand on-demand services, due to its high flexibility, low cost and easydeployment features. However, operation of UAVE faces several challenges, twoof which are how to achieve both 1) the association between multiple UEs andUAVs and 2) the resource allocation from UAVs to UEs, while minimizing theenergy consumption for all the UEs. To address this, we formulate the aboveproblem into a mixed integer nonlinear programming (MINLP), which is difficultto be solved in general, especially in the large-scale scenario. We thenpropose a Reinforcement Learning (RL)-based user Association and resourceAllocation (RLAA) algorithm to tackle this problem efficiently and effectively.Numerical results show that the proposed RLAA can achieve the optimalperformance with comparison to the exhaustive search in small scale, and haveconsiderable performance gain over other typical algorithms in large-scalecases.

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