  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 flexible 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), deep belief network (DBN), and adaptive
boosting (AdaBoost), respectively. Specifically, the CNN based demodulator
converts the modulated signals to images and recognizes the signals by the
image classification. The proposed DBN based demodulator contains three
restricted Boltzmann machines (RBMs) to extract the modulation features. The
AdaBoost method includes a strong classifier that is constructed by the weak
classifiers with the k-nearest neighbor (KNN) algorithm. These three
demodulators are trained and tested by our online open dataset. Experimental
results show that the demodulation accuracy of the three data-driven
demodulators drops as the transmission distance increases. A higher modulation
order negatively influences the accuracy for a given transmission distance.
Among the three ML methods, the AdaBoost modulator achieves the best
performance.
