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.

翻译：有效评估移动网络覆盖范围并精准识别服务薄弱区域，对于致力于提升用户体验质量（QoE）的网络运营商至关重要。本文提出了一种利用众包QoE数据进行移动网络覆盖与薄弱区域分析的新框架。我们方法的核心在于，基于经验地理定位数据，在单个小区（天线）层面进行覆盖分析，随后聚合至站点层面。本研究的一个关键贡献是应用单类支持向量机（OC-SVM）算法来计算移动网络覆盖。该方法将决策超平面建模为有效覆盖边界，从而实现了对单个小区及整个站点覆盖区域的稳健计算。我们将同一方法扩展应用于分析众包服务中断报告，从而识别并量化地理上局部化的薄弱区域。我们的研究结果证明了该新框架在精确绘制移动网络覆盖图，特别是突出显示信号不足的精细区域（尤其是在复杂的城市环境中）方面的有效性。