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• W3136753131 abstract "Nonlinear Fourier transform, as a technique that has a great potential to overcome the capacity limit in fibre optical communication system, faces speed and accuracy bottlenecks in practice. Machine learning using convolutional neural networks shows great potential in NFT-based applications. We have developed a convolutional neural network for decoding information in NFT-based communication and numerically demonstrated its performance in comparison to a fast NFT algorithm. The comparison indicates the potential of conventional neural network to replace NFT calculations for decoding of information." @default.
• W3136753131 created "2021-03-29" @default.
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• W3136753131 date "2021-03-30" @default.
• W3136753131 modified "2023-10-12" @default.
• W3136753131 title "Direct decoding of nonlinear OFDM-QAM signals using convolutional neural network" @default.
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• W3136753131 doi "https://doi.org/10.1364/oe.419609" @default.
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