Efficient Deep Learning of Nonlinear Fiber-Optic Communications Using a Convolutional Recurrent Neural Network

descriptionPublicationkeyboard_double_arrow_right Presentation , Other literature type , Article 01 Jan 2021 English Publisher:ZenodoJournal:(presesntation given at 20th IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) (IEEE ICMLA2021), Pasadena, California, USA, 13-15 Dec 2021)Funded by:EC | FONTE

Authors: Shahkarami, Abtin;

doi: 10.5281/zenodo.5871715 , 10.5281/zenodo.5871714

Efficient Deep Learning of Nonlinear Fiber-Optic Communications Using a Convolutional Recurrent Neural Network

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Abstract

A hybrid architecture comprising a CNN encoder and a many-to-one RNN was proposed. CNN captures short-temporal dependencies, and RNN extracts long-term features. CNN lessens the computational burden on RNN by taking the data into a latent space. Thanks to the shrunk number of time-steps, a many-to-one RNN works sufficiently. In a 100 GBd 16-QAM 20 x 100 km SMF optical transmission system, the CNN+RNN resulted in ~98% FLOPs reduction over Learned DBP, and more than 50% FLOPs reduction over the state-of-the-art CNN+bi-LSTM approach.

Keywords

neural networks, fiber-optic, RNN, performance, CNN, optical communication

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