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

2025-02-02

AI Summary

To address coverage imbalance, capacity limitations, and load heterogeneity in 3D heterogeneous cellular networks serving both ground users (GUEs) and unmanned aerial vehicles (UAVs), this paper pioneers the integration of quantization theory into base station (BS) deployment optimization, establishing a deterministic node modeling-based joint optimization framework. Methodologically, it unifies 3D channel modeling, nonlinear optimization, and a co-design algorithm jointly optimizing BS locations, antenna orientations, and radiation parameters. Departing from conventional single-dimensional optimization paradigms, our approach achieves the first holistic performance trade-off between GUEs and UAVs: UAV average capacity increases by 42%, while GUE performance degradation remains below 3%. This outperforms antenna-only tuning schemes significantly. The work provides a theoretically grounded and empirically verifiable methodology for 3D air-ground integrated network deployment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Wireless Network Optimization
• 3D User Environment
• Coverage and Capacity Enhancement

Innovation

• Quantitative Theory
• Wireless Network Optimization
• 3D User Environment Adaptability