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Real-Time Machine Learning Based Fiber-Induced Nonlinearity Compensation in Energy-Efficient Coherent Optical Networks

Applied Physics 2019-10-11 v1
Authors: Elias Giacoumidis , Yi Lin , Michaela Blott , Liam P. Barry
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Abstract

We experimentally demonstrate the first field-programmable gate-array-based real-time fiber nonlinearity compensator (NLC) using sparse K-means++ machine learning clustering in an energy-efficient 40-Gb/s 16-quadrature amplitude modulated self-coherent optical system. Our real-time NLC shows up to 3 dB improvement in Q-factor compared to linear equalization at 50 km of transmission.

Keywords

real-time machine learning fiber nonlinearity compensation coherent optical networks energy-efficient field-programmable gate array sparse k-means++ 16-qam

Cite

@article{arxiv.1910.04313,
  title  = {Real-Time Machine Learning Based Fiber-Induced Nonlinearity Compensation in Energy-Efficient Coherent Optical Networks},
  author = {Elias Giacoumidis and Yi Lin and Michaela Blott and Liam P. Barry},
  journal= {arXiv preprint arXiv:1910.04313},
  year   = {2019}
}

Comments

Submitted to ECOC post-deadline, Sep. 2019, Dublin, Ireland

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