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

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

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Research output: Unpublished contribution to conference › Unpublished Conference Paper › peer-review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 language	English
Number of pages	3
Publication status	Published - 6 Jun 2021
Event	2021 Optical Fiber Communications Conference and Exposition - , United States Duration: 6 Jun 2021 → 10 Jun 2021 https://www.ofcconference.org/en-us/home/

Conference

Conference	2021 Optical Fiber Communications Conference and Exposition
Abbreviated title	OFC 2021
Country/Territory	United States
Period	6/06/21 → 10/06/21
Internet address	https://www.ofcconference.org/en-us/home/

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OFC 2021_Neural_Network_Based_Nonlinearity_Equalizer_for_128_GBaud_Coherent_Transcievers
© 2021 The Authors
Submitted manuscript, 606 KB

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title = "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.",

author = "Vladislav Neskorniuk and Fred Buchali and Vinod Bajaj and Turitsyn, {Sergei K.} and Prilepsky, {Jaroslaw E.} and Vahid Aref",

note = "{\textcopyright} 2021 The Authors; 2021 Optical Fiber Communications Conference and Exposition, OFC 2021 ; Conference date: 06-06-2021 Through 10-06-2021",

year = "2021",

month = jun,

day = "6",

language = "English",

url = "https://www.ofcconference.org/en-us/home/",

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TY - CONF

T1 - Neural-Network-Based Nonlinearity Equalizer for 128 GBaud Coherent Transcievers

AU - Neskorniuk, Vladislav

AU - Buchali, Fred

AU - Bajaj, Vinod

AU - Turitsyn, Sergei K.

AU - Prilepsky, Jaroslaw E.

AU - Aref, Vahid

PY - 2021/6/6

Y1 - 2021/6/6

N2 - 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.

AB - 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.

UR - https://www.ofcconference.org/en-us/home/eposters/poster/?id=3560965

M3 - Unpublished Conference Paper

T2 - 2021 Optical Fiber Communications Conference and Exposition

Y2 - 6 June 2021 through 10 June 2021

ER -

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

Abstract

Conference

Bibliographical note

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Deep Learning Methods for Nonlinearity Mitigation in Coherent Fiber-Optic Communication Links

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

Abstract

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Bibliographical note

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Student theses

Deep Learning Methods for Nonlinearity Mitigation in Coherent Fiber-Optic Communication Links

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