A Signal-to-Data Translation Model for Robust Backscatter Communications

Backscatter communication is a promising technology in the hyper-connected era. Because of its ultra-low energy consumption, it can be used in various applications, but there are performance issues due to high uncertainty. We propose a signal-to-data translation model that can transform an entire backscatter signal into the original data. To train the translation model, we developed an automation framework that can efficiently collect datasets. We also proposed a data augmentation technique suitable for backscatter signals. In extensive experiments, our model significantly outperformed a simple rule-based decoding method and a commercial RFID reader. The proposed model showed consistent performance gains across different locations, obstacles, and mobility scenarios indicating a good generalization of learning.