REQIBA: Regression and Deep Q-Learning for Intelligent UAV Cellular User to Base Station Association

TL;DR

REQIBA is a novel AI-based method enabling UAVs to intelligently connect to cellular base stations, significantly improving their throughput by leveraging environmental data and deep reinforcement learning.

Contribution

The paper introduces REQIBA, a new approach combining regression and deep Q-learning for UAV base station association, addressing unique aerial network challenges.

Findings

REQIBA can double UAV throughput compared to heuristic schemes.

Environmental factors like UAV height and building density affect performance.

REQIBA adapts to urban environments for improved connectivity.

Abstract

Unmanned Aerial Vehicles (UAVs) are emerging as important users of next-generation cellular networks. By operating in the sky, UAV users experience very different radio conditions than terrestrial users, due to factors such as strong Line-of-Sight (LoS) channels (and interference) and Base Station (BS) antenna misalignment. As a consequence, the UAVs may experience significant degradation to their received quality of service, particularly when they are moving and are subject to frequent handovers. The solution is to allow the UAV to be aware of its surrounding environment, and intelligently connect into the cellular network taking advantage of this awareness. In this paper we present REgression and deep Q-learning for Intelligent UAV cellular user to Base station Association (REQIBA), a solution that allows a UAV flying over an urban area to intelligently connect to underlying BSs, using information about the received signal powers, the BS locations, and the surrounding building topology. We demonstrate how REQIBA can as much as double the total UAV throughput, when compared to heuristic association schemes similar to those commonly used by terrestrial users. We also evaluate how environmental factors such as UAV height, building density, and throughput loss due to handovers impact the performance of our solution.