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

TL;DR

This paper proposes a reinforcement learning-based algorithm for user association and resource allocation in multi-UAV enabled mobile edge computing, improving efficiency and energy consumption in flexible, on-demand network scenarios.

Contribution

It introduces a novel RL-based method to optimize user association and resource allocation in UAV-enabled MEC, addressing large-scale challenges effectively.

Findings

RLAA achieves near-optimal performance compared to exhaustive search in small scale.

The algorithm outperforms typical methods in large-scale scenarios.

Significant energy savings and efficiency improvements are demonstrated.

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.