数据驱动的多代细胞网络优化：一种用于战略基础设施管理的性能分类框架
Data-Driven Optimization of Multi-Generational Cellular Networks: A Performance Classification Framework for Strategic Infrastructure Management
AI总结：
本文提出一种性能分类框架，用于数据驱动的多代细胞网络优化，通过分析基站数据识别网络部署模式、利用情况及基础设施缺口，为运营商提供战略升级和资源优化的智能决策支持。
摘要：
移动数据需求的指数增长需要对电信基础设施进行智能管理，以确保服务质量（QoS）和运营效率。本文对一个多代细胞网络数据集进行了全面分析，数据来自OpenCelliD项目，以识别网络部署、利用情况和基础设施缺口的模式。方法涉及对1818个基站条目进行地理、时间和性能分析，主要为长期演进（LTE），覆盖三个国家，其中以巴基斯坦为主要集中区域。关键发现揭示了主要城市中心中遗留的2G/3G基础设施的长期持续存在，大量未充分利用的塔楼代表了节省成本的机会，以及识别了特定的'非4G需求区'，其中活跃用户群由过时技术服务。通过引入信号密度指标，我们区分了绝对过利用和局部拥堵。结果为移动网络运营商（MNOs）提供了可操作的智能，以指导战略LTE升级、优化资源配置，并在欠发达地区弥合数字鸿沟。
英文摘要：
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for cost savings, and the identification of specific "non-4G demand zones" where active user bases are served by outdated technologies.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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
