分析多代基站数据，帮运营商优化升级与资源分配。
Data-Driven Optimization of Multi-Generational Cellular Networks: A Performance Classification Framework for Strategic Infrastructure Management
- 基于1818个基站数据，结合地理时间维度分析网络部署
- 发现大量老旧2G/3G基站仍在城市使用，部分塔利用率极低
- 提出信号密度指标，识别非4G需求区，助力精准扩容
移动数据需求激增要求智能管理通信基础设施以保障服务质量与运营效率。本文基于OpenCelliD项目数据，对覆盖三个国家（主要集中在巴基斯坦）的1,818个蜂窝基站（以LTE为主）进行多维度分析，揭示网络部署、使用情况及基础设施缺口规律。研究发现：主要城市长期存在大量遗留的2G/3G设施；大量基站利用率不足，存在成本优化空间；特定区域虽有活跃用户却仍依赖过时技术。通过引入信号密度指标，可区分绝对过载与局部拥堵。研究成果为移动网络运营商（MNOs）提供战略升级、资源调配及缩小数字鸿沟的可操作建议。
原文摘要 · Abstract (English)
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for cost savings, and the identification of specific "non-4G demand zones" where active user bases are served by outdated technologies.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.
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