A Machine Learning Based Signal Demodulator in NOMA-VLC

Bangjiang Lin, Qiwei Lai, Zabih Ghassemlooy, Xuan Tang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Non-orthogonal multiple access (NOMA) is a promising scheme to improve the spectral efficiency, user fairness, and overall throughput in visible light communication (VLC) systems. However, the error propagation (EP) problem together with linear and nonlinear distortions induced by multipath, limited modulation bandwidth and nonlinearity of light emitting diode significantly limit the transmission performance of NOMA-VLC systems. In addition, having accurate channel state information, which is important in the recovery of NOMA signal, in mobile wireless VLC systems is challenging. In this work, we propose a convolutional neural network (CNN) based demodulator for NOMA-VLC, in which signal compensation and recovery are jointly realized. Both simulation and experiment results show that, the proposed CNN based demodulator can effectively compensate for both the linear and nonlinear distortions, thus achieving improved bit error ratio (BER) performances compared with the successive interference cancellation (SIC) and joint detection based receivers. Compared to SIC, the performance gains are 1.9, 2.7 and 2.7 dB for User1 for power allocation ratios (PARs) of 0.16, 0.25 and 0.36, respectively, which are 4, 4 and 2.6 dB for User2 for PARs of 0.16, 0.25 and 0.36, respectively.

Original languageEnglish
Pages (from-to)3081-3087
Number of pages7
JournalJournal of Lightwave Technology
Volume39
Issue number10
Early online date10 Feb 2021
DOIs
Publication statusPublished - 15 May 2021

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