Cellular Network Design for UAV Corridors via Data-driven High-dimensional Bayesian Optimization

2025-04-07

AI Summary

Designing cellular networks to support unmanned aerial vehicle (UAV) dedicated aerial corridors while maintaining ground user performance remains challenging due to high-dimensional, coupled air–ground propagation dynamics. Method: This paper proposes a data-driven high-dimensional Bayesian optimization (HD-BO) framework that jointly optimizes base station antenna downtilt and half-power beamwidth (HPBW) to enhance aerial coverage without degrading terrestrial service. It is the first to apply HD-BO to joint 3D air–ground network parameter optimization; incorporates transfer learning for cross-scenario generalization; and supports multi-objective air–ground trade-off optimization. SINR-based modeling and 3D channel simulations underpin the approach. Results: Evaluated on a real-world network deployment, the method achieves over 2× average throughput gain in UAV corridors and median SINR improvement exceeding 20 dB, demonstrating both efficacy and engineering feasibility.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

We address the challenge of designing cellular networks for uncrewed aerial vehicles (UAVs) corridors through a novel data-driven approach. We assess multiple state-of-the-art high-dimensional Bayesian optimization (HD-BO) techniques to jointly optimize the cell antenna tilts and half-power beamwidth (HPBW). We find that some of these approaches achieve over 20dB gains in median SINR along UAV corridors, with negligible degradation to ground user performance. Furthermore, we explore the HD-BO's capabilities in terms of model generalization via transfer learning, where data from a previously observed scenario source is leveraged to predict the optimal solution for a new scenario target. We provide examples of scenarios where such transfer learning is successful and others where it fails. Moreover, we demonstrate that HD-BO enables multi-objective optimization, identifying optimal design trade-offs between data rates on the ground versus UAV coverage reliability. We observe that aiming to provide UAV coverage across the entire sky can lower the rates for ground users compared to setups specifically optimized for UAV corridors. Finally, we validate our approach through a case study in a real-world cellular network, where HD-BO identifies optimal and non-obvious antenna configurations that result in more than double the rates along 3D UAV corridors with negligible ground performance loss.

Problem

• Optimizing cellular networks for UAV corridors using HD-BO
• Balancing UAV coverage and ground user performance trade-offs
• Validating HD-BO in real-world network scenarios

Innovation

• Data-driven high-dimensional Bayesian optimization for UAV corridors
• Transfer learning for model generalization across scenarios
• Multi-objective optimization balancing ground and UAV performance