用于 NFDM 信号的串行和并行卷积神经网络方案。
Serial and parallel convolutional neural network schemes for NFDM signals.
机构信息
Laser Physics and Photonic Devices Laboratories, STEM, University of South Australia, Adelaide, Australia.
Institute for Telecommunications Research, University of South Australia, Adelaide, Australia.
出版信息
Sci Rep. 2022 May 13;12(1):7962. doi: 10.1038/s41598-022-12141-4.
Two conceptual convolutional neural network (CNN) schemes are proposed, developed and analysed for directly decoding nonlinear frequency division multiplexing (NFDM) signals with hardware implementation taken into consideration. A serial network scheme with a small network size is designed for small user applications, and a parallel network scheme with high speed is designed for places such as data centres. The work aimed at showing the potential of using CNN for practical NFDM-based fibre optic communication. In the numerical demonstrations, the serial network only occupies 0.5 MB of memory space while the parallel network occupies 128 MB of memory but allows parallel computing. Both network schemes were trained with simulated data and reached more than 99.9% accuracy.
提出并分析了两种概念上的卷积神经网络（CNN）方案，用于直接解码具有硬件实现考虑的非线性频分复用（NFDM）信号。设计了一个具有小网络规模的串行网络方案，用于小型用户应用，设计了一个具有高速的并行网络方案，用于数据中心等场所。这项工作旨在展示使用 CNN 进行实用的基于 NFDM 的光纤通信的潜力。在数值演示中，串行网络仅占用 0.5MB 的内存空间，而并行网络占用 128MB 的内存空间，但允许并行计算。这两个网络方案都是使用模拟数据进行训练的，准确率都超过了 99.9%。
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