Predicting Locations of Cell Towers for Network Capacity Expansion

TL;DR

This paper introduces a machine learning framework that combines neural networks and spatial clustering to optimize cell tower placement for network expansion, considering real-world factors and budget constraints.

Contribution

It presents a novel, scalable, data-driven approach integrating geospatial, demographic, and infrastructural data for adaptive cell tower planning.

Findings

Framework effectively predicts coverage gaps.

Incorporates budget-aware deployment prioritization.

Modular design adaptable to various scenarios.

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.