Q-Learning Based Aerial Base Station Placement for Fairness Enhancement in Mobile Networks

TL;DR

This paper proposes a reinforcement learning approach for adaptive 3D placement of aerial base stations to improve fairness in mobile networks with user mobility, addressing an NP-hard optimization challenge.

Contribution

It introduces a novel RL-based method for fast, adaptive aerial-BS placement that enhances fairness in dynamic environments, close to optimal solutions.

Findings

Increases fairness among users in dynamic environments.

Achieves near-optimal placement with reasonable computation time.

Demonstrates effectiveness through simulation results.

Abstract

In this paper, we use an aerial base station (aerial-BS) to enhance fairness in a dynamic environment with user mobility. The problem of optimally placing the aerial-BS is a non-deterministic polynomial-time hard (NP-hard) problem. Moreover, the network topology is subject to continuous changes due to the user mobility. These issues intensify the quest to develop an adaptive and fast algorithm for 3D placement of the aerial-BS. To this end, we propose a method based on reinforcement learning to achieve these goals. Simulation results show that our method increases fairness among users in a reasonable computing time, while the solution is comparatively close to the optimal solution obtained by exhaustive search.