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A Machine Learning Based Signal Demodulator in NOMA-VLC

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 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 an accurate channel state information, which is important in the recovery of NOMA signal, in mobile wireless VLC 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 linear and nonlinear distortions, thus achieving improved bit error ratio performance compared with the successive interference cancellation and joint detection based receivers. Compared to SIC, the performance gains are 1.9, 2.7, and 2.7 dB for User1 for the 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.

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Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.39 No.10 May 15 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 3081 - 3087

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

VISIBLE LIGHT COMMUNICATIONS (VLC)
NON-ORTHOGONAL MULTIPLE ACCESS (NOMA)
CONVOLUTIONAL NEURAL NETWORK (CNN)

Info Detail Spesifik

DOI: 10.1109/JLT.2021.3058591

Pernyataan Tanggungjawab

Bangjiang Lin, Qiwei Lai, Zabih Ghassemlooy, Xuan Tang

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