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 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.
2019 IEEE Wireless Communications and Networking Conference (WCNC)