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 respect to state-of-the-art approaches, reducing the median positioning error by up to 30% in the most sparsely sampled or structurally complex regions. We also observe region-dependent saturation effects, which emerge most rapidly in scenarios with high user density where the information gain from additional synthetic samples quickly diminishes. Overall, the framework offers a practical, low-complexity path to enhance operator positioning services using existing mobile data traces.