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

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Abstract

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.

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Acknowledgements

This work was partially supported by National Key Research and Development Program of China (Grant No. 2017YFB0403603) and National Natural Science Foundation of China (Grant No. 61571133).

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Authors and Affiliations

  1. Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai, 200433, China

    Wenqing Niu, Yinaer Ha & Nan Chi

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  1. Wenqing Niu
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  2. Yinaer Ha
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  3. Nan Chi
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Correspondence to Nan Chi.

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Niu, W., Ha, Y. & Chi, N. Support vector machine based machine learning method for GS 8QAM constellation classification in seamless integrated fiber and visible light communication system. Sci. China Inf. Sci. 63, 202306 (2020). https://doi.org/10.1007/s11432-019-2850-3

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  • DOI: https://doi.org/10.1007/s11432-019-2850-3

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