SVM detection for superposed pulse amplitude modulation in visible light communications

Yuan, Youli, Zhang, Min, Luo, Pengfei, Ghassemlooy, Zabih, Wang, Danshi, Tang, Xiongyan and Han, Dahai (2016) SVM detection for superposed pulse amplitude modulation in visible light communications. In: Proceedings of the 2016 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP). IEEE, Piscataway, pp. 1-5. ISBN 9781509025268

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Official URL: http://dx.doi.org/10.1109/CSNDSP.2016.7573898

Abstract

A support vector machine (SVM)-based data detection for 8-superposed pulse amplitude modulation in visible light communication is proposed and experimentally demonstrated. In this work, the SVM detector contains three binary classifiers with different classification strategies. And the separating hyperplane of each SVM is constructed by training data. The experiment results show that the SVM detection offers 35% higher data rates when compared with the traditional direct decision method.

Item Type: Book Section
Uncontrolled Keywords: direct decision, support vector machine, superposed pulse amplitude modulation, visible light communiction
Subjects: H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Physics and Electrical Engineering
Depositing User: Ay Okpokam
Date Deposited: 12 Dec 2016 16:32
Last Modified: 12 Dec 2016 16:32
URI: http://nrl.northumbria.ac.uk/id/eprint/28878

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