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

Tony Chahoud, Lorenzo Mario Amorosa, Riccardo Marini, Luca De Nardis

TL;DR

A lightweight, modular mobile data augmentation framework designed to enhance multi-cell fingerprinting-based positioning using operator-collected minimization of drive test (MDT) records, which offers a practical, low-complexity path to enhance operator positioning services using existing mobile data traces.

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.

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

TL;DR

A lightweight, modular mobile data augmentation framework designed to enhance multi-cell fingerprinting-based positioning using operator-collected minimization of drive test (MDT) records, which offers a practical, low-complexity path to enhance operator positioning services using existing mobile data traces.

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.

Paper Structure

This paper contains 20 sections, 19 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Contributions and Paper Organization
  3. Background
  4. Fingerprinting in Cellular Networks
  5. Minimization of Drive Tests (MDT)
  6. Related Work
  7. Mobile Data Augmentation
  8. MDT Data
  9. Spatial Features Augmentation via Density Estimation
  10. Radio Features Augmentation via Non-Parametric Regression
  11. Analysis and Benefits
  12. Fingerprinting-based Positioning and Real-World MDT-based Scenarios
  13. Multi-cell Fingerprinting-based Positioning
  14. MDT-based Reference Scenarios
  15. Numerical Results
  16. ...and 5 more sections

Figures (4)

  • Figure 1: Mobile data augmentation architecture, comprising Spatial Features Augmentation and Radio Features Augmentation stages. The augmented dataset is ready to use for fingerprinting-based positioning algorithms.
  • Figure 2: The four MDT-based regions in Bologna representing our reference scenarios.
  • Figure 3: Illustrative comparison of real MDT geo-located samples and synthetic locations generated by KDE, GMM, GAN, and NF for the airport scenario.
  • Figure 4: Statistical comparison of KDE-KNN model variants across four deployment scenarios. Each subfigure corresponds to a cross-model comparison at increasing augmentation rates.