Mobile Coverage Analysis using Crowdsourced Geolocation Data
Published in arXiv pre-print (cs.AI), 2025
Recommended citation: Wong, T., Freeman, T., & Feehily, J. (2025). Mobile Coverage Analysis using Crowdsourced Data. arXiv [Cs.AI]. Retrieved from http://arxiv.org/abs/2510.13459 https://arxiv.org/abs/2510.13459
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.