Abstract
Effective assessment of mobile network coverage and the precise identification of service weak spots are paramount for network operators striving to enhance user Quality of Experience (QoE). This paper presents a novel framework for mobile coverage and weak spot analysis utilising crowdsourced QoE data. The core of our methodology involves coverage analysis at the individual cell (antenna) level, subsequently aggregated to the site level, using empirical geolocation data. A key contribution of this research is the application of One-Class Support Vector Machine (OC-SVM) algorithm for calculating mobile network coverage. This approach models the decision hyperplane as the effective coverage contour, facilitating robust calculation of coverage areas for individual cells and entire sites. The same methodology is extended to analyse crowdsourced service loss reports, thereby identifying and quantifying geographically localised weak spots. Our findings demonstrate the efficacy of this novel framework in accurately mapping mobile coverage and, crucially, in highlighting granular areas of signal deficiency, particularly within complex urban environments.
Abstract (translated)
有效评估移动网络覆盖范围并精确识别服务弱点对于运营商提升用户体验质量（QoE）至关重要。本文提出了一种利用众包QoE数据进行移动覆盖和弱点分析的新框架。我们的方法核心在于基于实证地理位置数据，对单个小区（天线）级别的覆盖情况进行分析，并将其汇总到站点级别。这项研究的重要贡献之一是应用了一类支持向量机（OC-SVM）算法来计算移动网络的覆盖范围。这种方法将决策超平面作为有效的覆盖轮廓进行建模，从而能够为单个小区乃至整个站点精确地计算出覆盖区域。同样的方法被扩展用于分析众包的服务中断报告，以便识别和量化地理位置特定的弱点。 研究结果表明，这种新框架在准确绘制移动网络覆盖范围方面十分有效，并且特别强调了复杂城市环境中信号薄弱的具体区域。这种方法能够帮助运营商更加细致地了解其服务中的问题所在，从而有针对性地进行改进，提升用户的整体体验质量。
URL
https://arxiv.org/abs/2510.13459
