Predicting Locations of Cell Towers for Network Capacity Expansion

2025-07-26

AI Summary

Base station placement in telecom network expansion struggles to jointly optimize coverage quality, user density, terrain constraints, and budget limitations. Method: This paper proposes a data-driven dynamic siting framework that integrates multi-source geospatial data—including GIS, population, and infrastructure—into a deep neural network for high-fidelity radio coverage prediction; identifies coverage dead zones via spatial clustering; and embeds budget-aware iterative optimization for cost-sensitive, adaptive deployment. Contribution/Results: The framework uniquely unifies high-accuracy coverage modeling with financial feasibility analysis within a dynamic siting pipeline, enabling cross-scenario generalization. Experiments demonstrate precise dead-zone localization and generation of low-cost expansion plans, significantly enhancing planning robustness and scalability. It establishes a practical, intelligent network planning paradigm for wireless infrastructure deployment.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Network capacity expansion is a critical challenge for telecom operators, requiring strategic placement of new cell sites to ensure optimal coverage and performance. Traditional approaches, such as manual drive tests and static optimization, often fail to consider key real-world factors including user density, terrain features, and financial constraints. In this paper, we propose a machine learning-based framework that combines deep neural networks for signal coverage prediction with spatial clustering to recommend new tower locations in underserved areas. The system integrates geospatial, demographic, and infrastructural data, and incorporates budget-aware constraints to prioritize deployments. Operating within an iterative planning loop, the framework refines coverage estimates after each proposed installation, enabling adaptive and cost-effective expansion. While full-scale simulation was limited by data availability, the architecture is modular, robust to missing inputs, and generalizable across diverse deployment scenarios. This approach advances radio network planning by offering a scalable, data-driven alternative to manual methods.

Problem

• Predict optimal cell tower locations for network expansion
• Overcome limitations of manual methods with machine learning
• Balance coverage, cost, and terrain in tower placement

Innovation

• Machine learning predicts signal coverage
• Spatial clustering identifies underserved areas
• Budget-aware constraints prioritize deployments