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Open AccessArticle

Tunable Optoelectronic Chromatic Dispersion Compensation Based on Machine Learning for Short-Reach Transmission

1
DTU Fotonik, Technical University of Denmark, DK-2800 Lyngby, Denmark
2
Nokia Bell Labs, Lorenzstr. 10, 70435 Stuttgart, Germany
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(20), 4332; https://doi.org/10.3390/app9204332
Received: 11 September 2019 / Revised: 2 October 2019 / Accepted: 8 October 2019 / Published: 15 October 2019
(This article belongs to the Special Issue Optics for AI and AI for Optics)
In this paper, a machine learning-based tunable optical-digital signal processor is demonstrated for a short-reach optical communication system. The effect of fiber chromatic dispersion after square-law detection is mitigated using a hybrid structure, which shares the complexity between the optical and the digital domain. The optical part mitigates the chromatic dispersion by slicing the signal into small sub-bands and delaying them accordingly, before regrouping the signal again. The optimal delay is calculated in each scenario to minimize the bit error rate. The digital part is a nonlinear equalizer based on a neural network. The results are analyzed in terms of signal-to-noise penalty at the KP4 forward error correction threshold. The penalty is calculated with respect to a back-to-back transmission without equalization. Considering 32 GBd transmission and 0 dB penalty, the proposed hybrid solution shows chromatic dispersion mitigation up to 200 ps/nm (12 km of equivalent standard single-mode fiber length) for stage 1 of the hybrid module and roughly double for the second stage. A simplified version of the optical module is demonstrated with an approximated 1.5 dB penalty compared to the complete two-stage hybrid module. Chromatic dispersion tolerance for a fixed optical structure and a simpler configuration of the nonlinear equalizer is also investigated. View Full-Text
Keywords: chromatic dispersion; short-reach communication; neural network; hybrid signal processing chromatic dispersion; short-reach communication; neural network; hybrid signal processing
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Ranzini, S.M.; Da Ros, F.; Bülow, H.; Zibar, D. Tunable Optoelectronic Chromatic Dispersion Compensation Based on Machine Learning for Short-Reach Transmission. Appl. Sci. 2019, 9, 4332.

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