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arXiv · 2206.12180

Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systems

Pedro J. Freire·Michael Anderson·Bernhard Spinnler·Thomas Bex·Jaroslaw E. Prilepsky·Tobias A. Eriksson·Nelson Costa·Wolfgang Schairer·Michaela Blott·Antonio Napoli·Sergei K. Turitsyn
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Abstract

For the first time, recurrent and feedforward neural network-based equalizers for nonlinearity compensation are implemented in an FPGA, with a level of complexity comparable to that of a dispersion equalizer. We demonstrate that the NN-based equalizers can outperform a 1 step-per-span DBP.

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Pedro J. Freire, Michael Anderson, Bernhard Spinnler, Thomas Bex, Jaroslaw E. Prilepsky, Tobias A. Eriksson, Nelson Costa, Wolfgang Schairer, Michaela Blott, Antonio Napoli, Sergei K. Turitsyn. 2022-06-24. Towards FPGA Implementation of Neural Network-Based Nonlinearity Mitigation Equalizers in Coherent Optical Transmission Systems. https://arxiv.org/abs/2206.12180

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