# Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learning

arXiv: https://arxiv.org/abs/1906.08171 · HTML: https://mlsanity.com/paper/1906.08171/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 13 of 13)

- [RF-Diffusion: Radio Signal Generation via Time-Frequency Diffusion](https://mlsanity.com/paper/2404.09140/) (2024)
- [Simple and Effective Augmentation Methods for CSI Based Indoor Localization](https://mlsanity.com/paper/2211.10790/) (2022)
- [DataLoc+: A Data Augmentation Technique for Machine Learning in Room-Level Indoor Localization](https://mlsanity.com/paper/2101.10833/) (2021)
- [Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity Recognition](https://mlsanity.com/paper/2401.00964/) (2024)
- [Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios](https://mlsanity.com/paper/2511.14076/) (2025)
- [Privacy-Preserving by Design: Indoor Positioning System Using Wi-Fi Passive TDOA](https://mlsanity.com/paper/2306.02211/) (2023)
- [DeepCell: A Ubiquitous Accurate Provider-side Cellular-based Localization](https://mlsanity.com/paper/2407.16927/) (2024)
- [Improving Depth Estimation using Location Information](https://mlsanity.com/paper/2112.13925/) (2021)
- [A Novel Framework for Brain Tumor Detection Based on Convolutional Variational Generative Models](https://mlsanity.com/paper/2202.09850/) (2022)
- [On the Multidimensional Augmentation of Fingerprint Data for Indoor Localization in A Large-Scale Building Complex Based on Multi-Output Gaussian Process](https://mlsanity.com/paper/2211.10642/) (2022)
- [Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization](https://mlsanity.com/paper/2408.06452/) (2024)
- [LocaGen: Low-Overhead Indoor Localization Through Spatial Augmentation](https://mlsanity.com/paper/2511.18158/) (2025)
- [Data Augmentation for High-Fidelity Generation of CAR-T/NK Immunological Synapse Images](https://mlsanity.com/paper/2602.00949/) (2026)

## Abstract (arXiv)

Recently, deep learning-based positioning systems have gained attention due to their higher performance relative to traditional methods. However, obtaining the expected performance of deep learning-based systems requires large amounts of data to train model. Obtaining this data is usually a tedious process which hinders the utilization of such deep learning approaches. In this paper, we introduce a number of techniques for addressing the data collection problem for deep learning-based cellular localization systems. The basic idea is to generate synthetic data that reflects the typical pattern of the wireless data as observed from a small collected dataset. Evaluation of the proposed data augmentation techniques using different Android phones in a cellular localization case study shows that we can enhance the performance of the localization systems in both indoor and outdoor scenarios by 157% and 50.5%, respectively. This highlights the promise of the proposed techniques for enabling deep learning-based localization systems.