用众包数据精准定位城市信号盲区
Mobile Coverage Analysis using Crowdsourced Data
- 基于单个基站的众包质量数据，用一类支持向量机建模覆盖范围
- 能准确识别出城市中信号薄弱的具体位置，定位精度高
- 适合网络运营商优化覆盖，尤其对复杂城区有实用价值
为提升用户服务质量体验，精确评估移动网络覆盖并识别服务薄弱点至关重要。本文提出一种利用众包服务质量数据进行移动网络覆盖与弱区分析的新框架。方法核心在于以实测地理位置数据为基础，在单个小区（天线）层面进行覆盖分析，并聚合至站点级别。研究关键贡献是应用一类支持向量机（OC-SVM）算法计算网络覆盖范围，将决策超平面建模为有效覆盖轮廓，从而实现对单个小区及整个站点覆盖区域的稳健计算。该方法进一步拓展至分析众包的服务中断报告，实现对地理上局部化弱区的识别与量化。实验结果表明，该框架在精准绘制移动网络覆盖图方面表现优异，尤其在复杂城市环境中能有效凸显信号不足的细粒度区域。
原文摘要 · Abstract (English)
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.
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