Efficient 3D Aerial Base Station Placement Considering Users Mobility by Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based method for optimal placement of aerial base stations in mobile user environments, enhancing network QoS by dynamically adapting to user movement.

Contribution

It proposes a Q-learning approach for aerial-BS placement that considers user mobility, improving network performance in dynamic scenarios.

Findings

Reinforcement learning effectively improves aerial-BS placement.

The method increases network QoS in simulations.

It finds optimal positions in discrete environments.

Abstract

This paper considers an aerial base station (aerial-BS) assisted terrestrial network where user mobility is taken into account. User movement changes the network dynamically which may result in performance loss. To avoid this loss, guarantee a minimum quality-of-service (QoS) and possibly increase the QoS, we add an aerial-BS to the network. For a fair comparison between the conventional terrestrial network and the aerial-BS assisted one, we keep the total number of BSs identical in both networks. Obtaining the best performance in such networks highly depends on the optimal placement of the aerial-BS. To this end, an algorithm which can rely on general and realistic assumptions and can decide where to go based on the past experiences is required. The proposed approach for this goal is based on a discounted reward reinforcement learning which is known as Q-learning. Simulation results show this method provides an effective placement strategy which increases the QoS of wireless networks when it is needed and promises to find the optimum position of the aerial-BS in discrete environments.