Mobile Coverage Analysis using Crowdsourced Data

TL;DR

This work addresses the challenge of mapping mobile network coverage and identifying geographically localized weak spots using crowdsourced QoE data. It introduces a per-cell coverage modeling framework based on a kernelized $OC$-SVM, learning a boundary function $f(x)$ that delineates coverage and can be aggregated to site level. The approach yields non-convex, data-driven coverage boundaries and outperforms a convex-hull baseline, validated across 4,000+ cell towers and synthetic NetPerform UK data. The study enables granular, scalable coverage assessment with practical implications for network planning, QoE improvement, and regulatory monitoring, and outlines extensions to 3D, temporal dynamics, and multi-technology analyses.

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.

Figures (3)

• Figure S1: Mobile signal coverage area of a single cell tower. On the left is the convex hull approach (a), which overgeneralises and includes large areas of no coverage. On the right is the OC-SVM approach, which captures concavities and internal holes to better represent the true coverage area. The right figure (b) is a smoothed, non-convex boundary that tightly wraps around the empirical coverage points, calculated using OC-SVM with RBF kernel.
• Figure S2: Effect of varying hyparameters $(\nu, \gamma)$ on the shape and fidelity of the OC-SVM boundary. Note the trade-off between smoothness and fragmentation.
• Figure S3: F1 Scores of OC-SVM vs Convex Hull