Demodulation Framework Based on Machine Learning for Unrepeated Transmission Systems
Author:
Affiliation:
1. Kyoto University
2. Nagoya University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Software
Link
https://www.jstage.jst.go.jp/article/transcom/E107.B/1/E107.B_2023PNP0003/_pdf
Reference52 articles.
1. [1] G.P. Agrawal, Nonlinear Fiber Optics, Sixth Ed., Academic Press, 2019. 10.1016/C2018-0-01168-8
2. [2] P.J. Winzer and R.-J. Essiambre, “Advanced optical modulation formats,” Proc. IEEE, vol.94, no.5, pp.952-985, 2006. 10.1109/jproc.2006.873438
3. [3] J. Renner, T. Fehenberger, M.P. Yankov, F.D. Ros, S. Forchhammer, G. Böcherer, and N. Hanik, “Experimental comparison of probabilistic shaping methods for unrepeated fiber transmission,” J. Lightwave Technol., vol.35, no.22, pp.4871-4879, 2017. 10.1109/jlt.2017.2752243
4. [4] J. Cho and P.J. Winzer, “Probabilistic constellation shaping for optical fiber communications,” J. Lightwave Technol., vol.37, no.6, pp.1590-1607, 2019. 10.1109/jlt.2019.2898855
5. [5] G. Böcherer, P. Schulte, and F. Steiner, “Probabilistic shaping and forward error correction for fiber-optic communication systems,” J. Lightwave Technol., vol.37, no.2, pp.230-244, 2019. 10.1109/jlt.2019.2895770
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