BBAuthors: Ma, S.; Advisor: -; Participants: Dai, J.; Lu, S.; Li, H.; Zhang, H.; Du, C.; Li, S. (2019)
In this paper, we investigate the design and implementation of machine learning (ML)-based demodulation methods in the physical layer of visible light communication (VLC) systems. We build a exible hardware prototype of an end-to-end VLC system, from which the received signals are collected as the real data. The dataset is available online, which contains eight types of modulated signals. Then, we propose three ML demodulators based on convolutional neural network (CNN), the deep belief network (DBN), and adaptive boosting (AdaBoost), respectively. Speci cally, the CNN-based demodulator converts the modulated signals to images and recognizes the signals by the image classi cation. The proposed DBN-based demodulator contains three restricted Boltzmann machines to extract the modulat... 
