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

Maryam Sabahat, M. Umar Khan

TL;DR

An analysis of a multigenerational cellular network dataset sourced from the OpenCelliD project to identify patterns in network deployment, utilization, and infrastructure gaps provides actionable intelligence for Mobile Network Operators to guide strategic LTE upgrades, optimize resource allocation, and bridge the digital divide in underserved regions.

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for cost savings, and the identification of specific "non-4G demand zones" where active user bases are served by outdated technologies.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.

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

TL;DR

An analysis of a multigenerational cellular network dataset sourced from the OpenCelliD project to identify patterns in network deployment, utilization, and infrastructure gaps provides actionable intelligence for Mobile Network Operators to guide strategic LTE upgrades, optimize resource allocation, and bridge the digital divide in underserved regions.

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.
Paper Structure (11 sections, 1 equation, 8 figures, 4 tables, 1 algorithm)

This paper contains 11 sections, 1 equation, 8 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Dataset and Methodology
  3. Geographical and Temporal Analysis Findings
  4. Network Utilization and Performance Analysis
  5. Identification of Infrastructure Gaps and Demand Zones
  6. Tower Performance Classifier
  7. Inputs
  8. Output
  9. Step-by-Step Explanation
  10. Results and Discussion
  11. Conclusion and Recommendations

Figures (8)

  • Figure 1: Tower Performance Classifier
  • Figure 1: Time series of monthly network updates from December 2023 to May 2025, illustrating a pronounced non-stationary pattern with a significant step-change in activity.
  • Figure 2: Comparative box plots of (a) coverage range (meters) and (b) sample count per tower, disaggregated by GSM, UMTS, and LTE technologies.
  • Figure 3: Scatter plot of samples versus signal density (samples/meter). The over-utilized clusters are clearly separated in the high-sample, high-density quadrant.
  • Figure 4: Spatial distribution of tower clusters across geographical coordinates, showing the physical layout and density patterns of different utilization categories.
  • ...and 3 more figures