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

• URL: http://arxiv.org/abs/2509.19405v1
• Date: Tue, 23 Sep 2025 09:09:45 GMT
• Title: Improving Outdoor Multi-cell Fingerprinting-based Positioning via Mobile Data Augmentation
• Authors: Tony Chahoud, Lorenzo Mario Amorosa, Riccardo Marini, Luca De Nardis,
• Abstract summary: This paper introduces a lightweight, modular mobile data augmentation framework designed to enhance multi-cell fingerprinting-based positioning.<n>The framework offers a practical, low-complexity path to enhance operator positioning services using existing mobile data traces.
• Score: 2.0065923589074735
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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