Neural-Network-Based Nonlinearity Equalizer for 128 GBaud Coherent Transcievers

Abstract

We propose an efficient neural-network-based equalization jointly compensating fiber and transceiver nonlinearities for high-symbol-rate coherent short-reach links. Providing about 0.9 dB extra SNR gain, it allows achieving experimentally the record single-channel 1.48 Tbps net rate over 240 km G.652 fiber.

Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Electrical and Electronic Engineering
College of Engineering & Physical Sciences
Additional Information: © 2021 The Authors
Event Title: 2021 Optical Fiber Communications Conference and Exposition
Event Type: Other
Event Dates: 2021-06-06 - 2021-06-10
PURE Output Type: Paper
Published Date: 2021-06-06
Authors: Neskorniuk, Vladislav
Buchali, Fred
Bajaj, Vinod
Turitsyn, Sergei K. (ORCID Profile 0000-0003-0101-3834)
Prilepsky, Jaroslaw E. (ORCID Profile 0000-0002-3035-4112)
Aref, Vahid

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