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arXiv · 1906.08171

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

Hamada Rizk·Ahmed Shokry·Moustafa Youssef
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Abstract

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.

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Hamada Rizk, Ahmed Shokry, Moustafa Youssef. 2019-06-11. Effectiveness of Data Augmentation in Cellular-based Localization Using Deep Learning. https://arxiv.org/abs/1906.08171

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