SVM-based detection in visible light communications

Yuan, Youli, Zhang, Min, Luo, Pengfei, Ghassemlooy, Zabih, Lang, Lei, Wang, Danshi, Zhang, Bo and Han, Dahai (2017) SVM-based detection in visible light communications. Optik - International Journal for Light and Electron Optics, 151. pp. 55-64. ISSN 0030-4026

Full text not available from this repository. (Request a copy)
Official URL: https://doi.org/10.1016/j.ijleo.2017.08.089

Abstract

A support vector machine (SVM)-based data detection for 8-superposed pulse amplitude modulation and direct-current-biased optical orthogonal frequency division multiplexing in visible light communication is proposed and experimentally demonstrated. In this work, the SVM detector contains multiple binary classifiers with different classification strategies. The separating hyperplane of each SVM is constructed by means of the training data. The experiment results presented that the SVM detection offers improved bit error rate performance compared with the traditional direct decision method.

Item Type: Article
Uncontrolled Keywords: Support vector machine, Superposed pulse amplitude modulation, Orthogonal frequency division multiplexing, Visible light communication, Direct decision
Subjects: H600 Electronic and Electrical Engineering
P900 Others in Mass Communications and Documentation
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Becky Skoyles
Date Deposited: 03 Oct 2017 14:45
Last Modified: 11 Apr 2018 08:27
URI: http://nrl.northumbria.ac.uk/id/eprint/32228

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics


Policies: NRL Policies | NRL University Deposit Policy | NRL Deposit Licence