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

arXiv: https://arxiv.org/abs/2509.19405 · HTML: https://mlsanity.com/paper/2509.19405/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 23 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling](https://mlsanity.com/paper/2303.11103/) (2023)
- [CSI-Based Multi-Antenna and Multi-Point Indoor Positioning Using Probability Fusion](https://mlsanity.com/paper/2009.02798/) (2020)
- [Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home](https://mlsanity.com/paper/2201.07387/) (2022)
- [Machine Learning in NextG Networks via Generative Adversarial Networks](https://mlsanity.com/paper/2203.04453/) (2022)
- [Positioning Using Wireless Networks: Applications, Recent Progress and Future Challenges](https://mlsanity.com/paper/2403.11417/) (2024)
- [Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning](https://mlsanity.com/paper/2202.10968/) (2022)
- [Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework](https://mlsanity.com/paper/2304.12480/) (2023)
- [Characterizing Power Consumption of Dual-Frequency GNSS of a Smartphone](https://mlsanity.com/paper/1910.13041/) (2019)

## Abstract (arXiv)

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.