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• W2893078023 abstract "In this paper, we demonstrate a novel Gaussian kernel-aided deep neural network (GK-DNN) equalizer that can effectively compensate for the high nonlinear distortion of underwater PAM8 visible light communication (VLC) channels. The application of a Gaussian kernel can reduce the necessary training iterations to 47.06%, enabling it to outperform the traditional DNN equalizer. At the same time, a novel design strategy with respect to the structure of the GK-DNN equalizer is proposed, which can effectively save computing resources and reduce the data volume of the necessary training data set. By using the GK-DNN equalizer, a 1.5 Gbps PAM8 VLC system over 1.2-m underwater transmission is successfully demonstrated." @default.
• W2893078023 created "2018-10-05" @default.
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• W2893078023 date "2018-09-27" @default.
• W2893078023 modified "2023-10-17" @default.
• W2893078023 title "Gaussian kernel-aided deep neural network equalizer utilized in underwater PAM8 visible light communication system" @default.
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• W2893078023 doi "https://doi.org/10.1364/oe.26.026700" @default.
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