Demo: A Machine Learning based M-ary Amplitude Modulated Visible Light Communication System

Abstract

Visible Light Communication (VLC), which operates in the Terahertz band, is theoretically capable of terabits/second data speeds. However, traditional state-of-the-art VLC has largely been limited in the data rates and communication range. A common approach to modulation in VLC is the use of ON-OFF Keying (OOK), where a binary bit 1 is mapped to a high or ON state of the light emitter and bit 0 to a low or OFF state. While there have been approaches to improve the spectral efficiency through other modulation schemes such as color-shift-keying (CSK), frequency-shift-keying (FSK) and orthogonal frequency division multiplexing (OFDM), the fundamental mapping of intensity of light emitters to high and low states is still required. In this work, we explore a novel approach for using multi-level or M-ary amplitude modulation to encode symbols in VLC. We demonstrate a technique that uses machine learning of the VLC channel state to improve demodulation of M-ary signals in a LED-Photodiode VLC system.