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 Nokia Bell Labs, Team Sander Wahls - Mechanical, Maritime and Materials Engineering
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.

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