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

Vladislav Neskorniuk*, Fred Buchali, Vinod Bajaj, Sergei K. Turitsyn, Jaroslaw E. Prilepsky, Vahid Aref

*Corresponding author for this work

Research output: Unpublished contribution to conferenceUnpublished Conference Paperpeer-review

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.
Original languageEnglish
Number of pages3
Publication statusPublished - 6 Jun 2021
Event2021 Optical Fiber Communications Conference and Exposition - , United States
Duration: 6 Jun 202110 Jun 2021
https://www.ofcconference.org/en-us/home/

Conference

Conference2021 Optical Fiber Communications Conference and Exposition
Abbreviated titleOFC 2021
CountryUnited States
Period6/06/2110/06/21
Internet address

Bibliographical note

© 2021 The Authors

Fingerprint

Dive into the research topics of 'Neural-Network-Based Nonlinearity Equalizer for 128 GBaud Coherent Transcievers'. Together they form a unique fingerprint.

Cite this