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

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

Conference Paper (2021)

Author(s)

Vladislav Neskorniuk (Aston University, Nokia Bell Labs)

Fred Buchali (Nokia Bell Labs)

Vinod Bajaj (TU Delft - Team Gabriel Gleizer, Nokia Bell Labs)

Sergei K. Turitsyn (Aston University)

Jaroslaw E. Prilepsky (Aston University)

Vahid Aref (Nokia Bell Labs)

Research Group

Team Gabriel Gleizer

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https://resolver.tudelft.nl/uuid:30f9a6ca-a910-41c8-b70f-532ce5f70433

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Publication Year

2021

Language

English

Research Group

Team Gabriel Gleizer

Article number

9489740

ISBN (electronic)

978-1-943580-86-6

Event

2021 Optical Fiber Communications Conference and Exhibition, OFC 2021 (2021-06-06 - 2021-06-11), San Francisco, United States

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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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