Weak signal detection for visible light communication in the pulse and transition regimes of an operational PMT detector via an SVM-based learning method

Abstract

Photo-multiplier tube can be adopted for optical signal detection under weak signal and ambient light intensity, where the signals can be classified into three regimes, discrete-pulse regime, continuous waveform regime and the transition regime between the discrete-photon and continuous waveform regimes. While Poisson and Gaussian distributions can well characterize the discrete-photon and continuous waveform regimes, respectively, a statistical characterization and the related signal detection in the transition regime are difficult. In this work, we resort to a learning approach for the signal characterization and detection under pulse and transition regimes. We propose a support vector machine (SVM)-based approach for signal detection, which extracts eight key features on the received signal. We optimize the hyper-parameters to improve the SVM detection performance. The proposed SVM-based approach is experimentally evaluated under different symbol and sampling rates, and outperforms that of various statistics-based comparison benchmarks.