Improving Outdoor Multi-cell Fingerprinting-based Positioning via Mobile Data Augmentation

Abstract--Accurate outdoor positioning in cellular networks is hindered by sparse, heterogeneous measurement collections and the high cost of exhaustive site surveys. This paper introduces a lightweight, modular mobile data augmentation framework designed to enhance multi-cell fingerprinting-based positioning using operator-collected minimization of drive test (MDT) records. The proposed approach decouples spatial and radio-feature synthesis: kernel density estimation (KDE) models the empirical spatial distribution to generate geographically coherent synthetic locations, while a k-nearest-neighbor (KNN)-based block produces augmented per-cell radio fingerprints. The architecture is intentionally training-free, interpretable, and suitable for distributed or on-premise operator deployments, supporting privacy-aware workflows. We both validate each augmentation module independently and assess its end-to-end impact on fingerprinting-based positioning using a real-world MDT dataset provided by an Italian mobile network operator across diverse urban and peri-urban scenarios. Results show that the proposed KDE-KNN augmentation consistently improves positioning performance, with the largest benefits in sparsely sampled or structurally complex regions; we also observe region-dependent saturation effects as augmentation increases. The framework offers a practical, low-complexity path to enhance operator positioning services using existing mobile data traces. ECENT years have seen a growing demand for accurate and reliable positioning services in dense urban areas, indoor environments, and under adverse weather conditions, such as overcast skies, where satellite-based systems like the global positioning system (GPS) often suffer from severe multipath propagation, signal blockage, urban canyon effects, and are known to be power-intensive, making it unsuitable for energy-constrained devices commonly used in mobile applications [1]. In these scenarios, multicell fingerprint-based positioning has emerged as a promising approach due to its robustness in non-line-of-sight conditions and the ability to leverage existing cellular infrastructure [2, 3].