Research on SCMA-VLC Receiver based on Convolutional Neural Network

ZOU Shao-qin; LAI Qi-wei; LIN Bang-jiang

doi:10.13756/j.gtxyj.2022.01.006

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Research on SCMA-VLC Receiver based on Convolutional Neural Network

更新时间：2022-06-15

- Research on SCMA-VLC Receiver based on Convolutional Neural Network
- Study on Optical Communications Issue 1, Pages: 27-30,62(2022)
- 作者机构：
  
  1.厦门南洋职业学院，福建　厦门　361102
  2.中国科学院福建物质结构研究所　泉州装备制造研究所，福建　泉州　362000
- 作者简介：
- 基金信息：
- DOI：10.13756/j.gtxyj.2022.01.006
  CLC： TN256
- Published：10 February 2022，
  
  Received：28 January 2021，
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ZOU Shao-qin, LAI Qi-wei, LIN Bang-jiang. Research on SCMA-VLC Receiver based on Convolutional Neural Network. [J]. Study on Optical Communications (1):27-30,62(2022)
DOI：

ZOU Shao-qin, LAI Qi-wei, LIN Bang-jiang. Research on SCMA-VLC Receiver based on Convolutional Neural Network. [J]. Study on Optical Communications (1):27-30,62(2022) DOI： 10.13756/j.gtxyj.2022.01.006.

摘要

发光二极管(LED)有限的带宽与其非线性电光转换引起的信号失真，是限制可见光通信(VLC)传输性能的重要因素。传统的消息传递算法(MPA)在稀疏码分多址(SCMA)-VLC系统中对抗信号非线性失真的能力有限。针对上述问题，文章提出了一种基于卷积神经网络(CNN)接收机，通过对接收信号进行特征提取、分类和判决，可有效提升SCMA-VLC系统抗非线性失真的能力。实验结果表明，在非线性失真较强的情况下，CNN接收机比MPA接收机表现出了更优的误码率(BER)性能，有效地扩大了偏置电压的动态范围，减少了系统对信噪比(SNR)的要求。

Abstract

The signal distortion caused by the limited bandwidth of Light Emitting Diode (LED) and its nonlinear electro-optic conversion severely limit the transmission performance of Visible Light Communication (VLC). Traditional Message Passing Algorithm (MPA) has limited ability to resist signal nonlinear distortion in Sparse Code Multiple Access (SCMA) -VLC. In order to solve the above problems

the paper proposes a Convolutional Neural Network (CNN) receiver

which can effectively improve the anti-nonlinear distortion ability of SCMA-VLC system by feature extraction

classification and judgment of the received signal. The results show that

in the case of strong nonlinear distortion

CNN receiver has better Bit Error Rate (BER) performance than MPA receiver

which effectively expands the dynamic range of bias voltage and reduces the Signal-to-Noise Ratio (SNR) requirements of the system.

关键词

卷积神经网络消息传递算法稀疏码分多址可见光通信信号失真

Keywords

CNNMPASCMAVLCsignal distortion

references

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许俊翔, 林邦姜, 汤璇, 等. 一种基于LSTM的OFDM-VLC后失真补偿方法[J]. 光通信技术, 2020, 44(1): 54-57.

Lee H, Lee I, Lee S H. Deep Learning based Transceiver Design for Multi-colored VLC Systems[J]. Optics Express, 2018, 26(5): 6222-6238.

Wu N, Wang X, Lin B, et al. A CNN-based End-to-end Learning Framework Towards Intelligent Communication Systems[J]. IEEE Access, 2019, 7: 110197-110204.

Lin B, Yang H, Wang R, et al. Convolutional Neural Network-based Signal Demodulation Method for NOMA-PON[J]. Optics Express, 2020, 28(10): 392535.

Lin B, Tang X, Zhou Z, et al. Experimental Demonstration of SCMA for Visible Light Communications[J]. Optics Communications, 2018, 419(12): 36-40.

陈康. 室内可见光通信自适应均衡技术的研究[D]. 长春: 长春理工大学, 2019.

李静. 基于信息传递的SCMA低复杂度检测算法研究[D]. 北京: 北京邮电大学, 2019.

Lin B, Lai Q, Ghassemlooy Z, et al. A Machine Learning based Signal Demodulator in NOMA-VLC[J]. Journal of Lightwave Technology, 2021, 39: 3081-3087.

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