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- W4385800893 abstract "In this paper, we compare the performance of two data-driven fiber channel modeling methods based on Bi-LSTM and CGAN waveform modeling schemes. The results show that the two methods can learn the accurate transfer function of the fiber channel, with only 0.6% average MSE compared with SSFM. The running times are less than 0.2 seconds and 0.25 seconds for 200-km transmission, versus 400 seconds using the SSFM under the same hardware conditions, which shows the reduction in complexity of the fiber channel modeling." @default.
- W4385800893 created "2023-08-15" @default.
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- W4385800893 date "2023-07-02" @default.
- W4385800893 modified "2023-10-16" @default.
- W4385800893 title "Accurate Data-driven Fiber Channel Modeling Based on BiLSTM and Conditional GAN" @default.
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- W4385800893 doi "https://doi.org/10.1109/oecc56963.2023.10209793" @default.
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