Support vector machine based machine learning method for GS 8QAM constellation classification in seamless integrated fiber and visible light communication system

Visible light communication (VLC) network over optical fiber has become a potential candidate in ultra-high speed indoor wireless communication. To mitigate signal distortion accumulated in optical fiber and VLC channel, we present to utilize support vector machine (SVM) for constellation classification in two kinds of geometrically-shaped 8QAM (quadrature amplitude modulation) seamless integrated fiber and VLC system. We introduce 4 sub-bands to simulate multi-user. Experimental results show that system performance can be significantly improved, and transmission at -2.5 dBm input optical power under 7% forward error correction (FEC) threshold can be realized employing Circular (7, 1) geometrically-shaped 8QAM and SVM. At overall capacity of 960 Mbps, Q-factor increases by up to 11.5 dB.