Data-Driven Optimization of Multi-Generational Cellular Networks: A Performance Classification Framework for Strategic Infrastructure Management

TL;DR

This paper analyzes cellular network data to identify deployment patterns, infrastructure gaps, and under-utilized towers, providing insights for strategic upgrades and efficient resource management in multi-generational networks.

Contribution

It introduces a performance classification framework using a signal-density metric to guide infrastructure upgrades and optimize cellular network deployment strategies.

Findings

Legacy 2G/3G infrastructure persists in urban centers

Many towers are under-utilized, indicating cost-saving opportunities

Identifies 'non-4G demand zones' served by outdated technology

Abstract

The exponential growth in mobile data demand necessitates intelligent management of telecommunications infrastructure to ensure Quality of Service (QoS) and operational efficiency. This paper presents a comprehensive analysis of a multigenerational cellular network dataset, sourced from the OpenCelliD project, to identify patterns in network deployment, utilization, and infrastructure gaps. The methodology involves geographical, temporal, and performance analysis of 1,818 cell tower entries, predominantly Long Term Evolution (LTE), across three countries with a significant concentration in Pakistan. Key findings reveal the long-term persistence of legacy 2G/3G infrastructure in major urban centers, the existence of a substantial number of under-utilized towers representing opportunities for cost savings, and the identification of specific "non-4G demand zones" where active user bases are served by outdated technologies. By introducing a signal-density metric, we distinguish between absolute over-utilization and localized congestion. The results provide actionable intelligence for Mobile Network Operators (MNOs) to guide strategic LTE upgrades, optimize resource allocation, and bridge the digital divide in underserved regions.