Complex-Valued Neural Network Design for Mitigation of Signal Distortions in Optical Links

Abstract

Nonlinearity compensation is considered as a key enabler to increase channel transmission rates in the installed optical communication systems. Recently, data-driven approaches - motivated by modern machine learning techniques - have been proposed for optical communications in place of traditional model-based counterparts. In particular, the application of neural networks (NN) allows improving the performance of complex modern fiber-optic systems without relying on any a priori knowledge of their specific parameters. In this work, we introduce a novel design of complex-valued NN for optical systems and examine its performance in standard single mode fiber (SSMF) and large effective-area fiber (LEAF) links operating in relatively high nonlinear regime. First, we present a methodology to design a new type of NN based on the assumption that the channel model is more accurate in the nonlinear regime. Second, we implement a Bayesian optimizer to jointly adapt the size of the NN and its number of input taps depending on the different fiber properties and total length. Finally, the proposed NN is numerically and experimentally validated showing an improvement of 1.7 dB in the linear regime, 2.04 dB at the optimal optical power and 2.61 at the max available power on Q-factor when transmitting a WDM 30 × 200G DP-16QAM signal over a 612 km SSMF legacy link. The results highlight that the NN is able to mitigate not only part of the nonlinear impairments caused by optical fiber propagation but also imperfections resulting from using low-cost legacy transceiver components, such as digital-to-analog converter (DAC) and Mach-Zehnder modulator.

Publication DOI: https://doi.org/10.1109/JLT.2020.3042414
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Electrical and Electronic Engineering
College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Funding: This paper was supported by the EU Horizon 2020 program under the Marie Skodowska-Curie grant agreements No.766115 (FONTE) and 813144 (REAL-NET).
Uncontrolled Keywords: Bayesian optimizer,Neural network,channel model,coherent detection,metropolitan links,nonlinear equalizer,Atomic and Molecular Physics, and Optics
Full Text Link:
Related URLs: https://ieeexpl ... cument/9280329/ (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2021-03-15
Published Online Date: 2020-12-03
Accepted Date: 2020-12-01
Authors: Freire, Pedro J.
Neskorniuk, Vladislav
Napoli, Antonio
Spinnler, Bernhard
Costa, Nelson
Khanna, Ginni
Riccardi, Emilio
Prilepsky, Jaroslaw E. (ORCID Profile 0000-0002-3035-4112)
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)

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