Mathematical Cell Deployment Optimization for Capacity and Coverage of Ground and UAV Users

TL;DR

This paper introduces a mathematical framework for optimizing cell deployment and antenna configurations in wireless networks with ground and UAV users, improving coverage and capacity through strategic placement and tuning.

Contribution

The paper presents a novel mathematical framework supporting deterministic node deployment and joint optimization of antenna parameters and base station locations for heterogeneous 3D user populations.

Findings

Optimizing BS placement and orientation outperforms antenna tuning alone.

Joint GUE and UAV optimization enhances UAV service without harming GUE performance.

Framework effectively balances coverage and capacity in heterogeneous environments.

Abstract

We present a general mathematical framework for optimizing cell deployment and antenna configuration in wireless networks, inspired by quantization theory. Unlike traditional methods, our framework supports networks with deterministically located nodes, enabling modeling and optimization under controlled deployment scenarios. We demonstrate our framework through two applications: joint fine-tuning of antenna parameters across base stations (BSs) to optimize network coverage, capacity, and load balancing, and the strategic deployment of new BSs, including the optimization of their locations and antenna settings. These optimizations are conducted for a heterogeneous 3D user population, comprising ground users (GUEs) and uncrewed aerial vehicles (UAVs) along aerial corridors. Our case studies highlight the framework's versatility in optimizing performance metrics such as the coverage-capacity trade-off and capacity per region. Our results confirm that optimizing the placement and orientation of additional BSs consistently outperforms approaches focused solely on antenna adjustments, regardless of GUE distribution. Furthermore, joint optimization for both GUEs and UAVs significantly enhances UAV service without severely affecting GUE performance.