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并精确识别服务薄弱区域，对于网络运营商提升用户体验质量至关重要。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利用经验地理定位数据，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算法进行移动网络覆盖计算。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，进而识别并量化地理局部化的薄弱区域。研究结果表明，该新框架能有效绘制精确的移动网络覆盖图，并尤为关键的是，能在复杂城市环境中清晰揭示信号缺失的精细区域。