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

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...

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Bibliographic Details
Main Authors: Rizk, Hamada, Shokry, Ahmed, Youssef, Moustafa
Format: Conference Proceeding
Language:English
Subjects:
Data collection
Data models
Deep learning
Poles and towers
Training
Wireless communication
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Summary: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.
ISSN:1558-2612
DOI:10.1109/WCNC.2019.8886005