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Experimental Evaluation of Computational Complexity for Different Neural Network Equalizers in Optical Communications

Addressing the neural network-based optical channel equalizers, we quantify the trade-off between their performance and complexity by carrying out the comparative analysis of several neural network architectures, presenting the results for TWC and SSMF set-ups.

eess.SPcs.LGEngineeringPhysicsComputer Science
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References (10)

01Performance Versus Complexity Study of Neural Network Equalizers in Coherent Optical SystemsPedro J. Freire, Yevhenii Osadchuk, B. Spinnler et al.2021 · Journal of Lightwave Technology · 147 citations In Library
02Complex-Valued Neural Network Design for Mitigation of Signal Distortions in Optical LinksPedro J. Freire, Vladislav Neskornuik, A. Napoli et al.2020 · Journal of Lightwave Technology · 59 citations
03Advancing theoretical understanding and practical performance of signal processing for nonlinear optical communications through machine learningQirui Fan, Gai Zhou, T. Gui et al.2020 · Nature Communications · 150 citations
04Memory-controlled deep LSTM neural network post-equalizer used in high-speed PAM VLC system.Xingyu Lu, Chao Lu, Weixiang Yu et al.2019 · Optics Express · 83 citations
05Lower Bounds for Smooth Nonconvex Finite-Sum OptimizationDongruo Zhou, Quanquan Gu2019 · International Conference on Machine Learning · 49 citations In Library
06Equalization performance and complexity analysis of dynamic deep neural networks in long haul transmission systems.O. Sidelnikov, A. Redyuk, S. Sygletos2018 · Optics Express · 76 citations
07Equalizer Design and Complexity for Digital Coherent ReceiversB. Spinnler2010 · IEEE Journal of Selected Topics in Quantum Electronics · 172 citations
08Table of Parameters for the paper (Experimental Evaluation of Computational Complexity for Different Neural Network Equalizers in Optical Communications)2021 · Zenodo
09A survey of modelcompression and acceleration for deep neural networks2017
10Deep LearningXingbang Hao, Guigang Zhang, Shang Ma2016 · International Journal of Semantic Computing · 79k citations

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