Signal Demodulation With Machine Learning Methods for Physical Layer Visible Light Communications: Prototype Platform, Open Dataset, and Algorithms

Abstract

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 modulation features. The AdaBoost method includes a strong classi er that is constructed by the weak classi ers with the knearest neighbor algorithm. These three demodulators are trained and tested by our online open dataset. The experimental results show that the demodulation accuracy of the three data-driven demodulators drops as the transmission distance increases. A higher modulation order negatively in uences the accuracy for a given transmission distance. Among the three ML methods, the AdaBoost modulator achieves the best performance.