Designing Cellular Networks for UAV Corridors via Bayesian Optimization

TL;DR

This paper introduces a Bayesian optimization approach to redesign cellular networks for improved 3D UAV connectivity, balancing UAV and ground user performance.

Contribution

It presents a novel methodology for optimizing cellular network parameters specifically for UAV corridors using Bayesian optimization techniques.

Findings

Achieved a 23.4dB increase in mean SINR for UAVs.

Maintained or slightly improved ground user SINR performance.

Demonstrated the framework's flexibility to optimize various network functions.

Abstract

As traditional cellular base stations (BSs) are optimized for 2D ground service, providing 3D connectivity to uncrewed aerial vehicles (UAVs) requires re-engineering of the existing infrastructure. In this paper, we propose a new methodology for designing cellular networks that cater for both ground users and UAV corridors based on Bayesian optimization. We present a case study in which we maximize the signal-to-interference-plus-noise ratio (SINR) for both populations of users by optimizing the electrical antenna tilts and the transmit power employed at each BS. Our proposed optimized network significantly boosts the UAV performance, with a 23.4dB gain in mean SINR compared to an all-downtilt, full-power baseline. At the same time, this optimal tradeoff nearly preserves the performance on the ground, even attaining a gain of 1.3dB in mean SINR with respect to said baseline. Thanks to its ability to optimize black-box stochastic functions, the proposed framework is amenable to maximize any desired function of the SINR or even the capacity per area.