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

Conference paper (2021)

Authors

Vladislav Neskorniuk Aston University, Nokia Bell Labs
Fred Buchali Nokia Bell Labs
V. Bajaj Team Sander Wahls - Mechanical, Maritime and Materials Engineering , Nokia Bell Labs
Sergei K. Turitsyn Aston University
Jaroslaw E. Prilepsky Aston University
Vahid Aref Nokia Bell Labs
Research Group
Team Sander Wahls (Mechanical, Maritime and Materials Engineering) (TU Delft)
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Sander Wahls

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.