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Performance-enhanced amplified O-band WDM transmission using machine learning based equalization

Performance-enhanced amplified O-band WDM transmission using machine learning based equalization
Performance-enhanced amplified O-band WDM transmission using machine learning based equalization
We investigate the performance of a machine learning-based equalization in an amplified 4×50-Gb/s O-band WDM system. The results show that the scheme offers significant receiver sensitivity improvements over decision-feedback equalization, especially at more dispersive wavelengths.
IEEE
Hong, Yang
73d5144c-02db-4977-b517-0d2f5a052807
Deligiannidis, Stavros
b20ae1a7-3ffc-4722-916c-bd3b4021e247
Taengnoi, Natsupa
afc5fb3e-224b-43b3-a161-931ed77faec1
Bottrill, Kyle R.H.
8c2e6c2d-9f14-424e-b779-43c23e2f49ac
Thipparapu, Naresh K.
a36a2b4c-b75c-4976-a753-b5fab9e54150
Wang, Yu
87b384ad-fc75-4ec9-a5aa-284452b40156
Sahu, Jayanta K.
009f5fb3-6555-411a-9a0c-9a1b5a29ceb2
Richardson, David J.
ebfe1ff9-d0c2-4e52-b7ae-c1b13bccdef3
Mesaritakis, Charis
3e722138-feb0-45b0-ae46-e29104f270f2
Bogris, Adonis
f1d9d602-369c-4d3a-8eb9-6225d68fb275
Petropoulos, Periklis
522b02cc-9f3f-468e-bca5-e9f58cc9cad7
Hong, Yang
73d5144c-02db-4977-b517-0d2f5a052807
Deligiannidis, Stavros
b20ae1a7-3ffc-4722-916c-bd3b4021e247
Taengnoi, Natsupa
afc5fb3e-224b-43b3-a161-931ed77faec1
Bottrill, Kyle R.H.
8c2e6c2d-9f14-424e-b779-43c23e2f49ac
Thipparapu, Naresh K.
a36a2b4c-b75c-4976-a753-b5fab9e54150
Wang, Yu
87b384ad-fc75-4ec9-a5aa-284452b40156
Sahu, Jayanta K.
009f5fb3-6555-411a-9a0c-9a1b5a29ceb2
Richardson, David J.
ebfe1ff9-d0c2-4e52-b7ae-c1b13bccdef3
Mesaritakis, Charis
3e722138-feb0-45b0-ae46-e29104f270f2
Bogris, Adonis
f1d9d602-369c-4d3a-8eb9-6225d68fb275
Petropoulos, Periklis
522b02cc-9f3f-468e-bca5-e9f58cc9cad7

Hong, Yang, Deligiannidis, Stavros, Taengnoi, Natsupa, Bottrill, Kyle R.H., Thipparapu, Naresh K., Wang, Yu, Sahu, Jayanta K., Richardson, David J., Mesaritakis, Charis, Bogris, Adonis and Petropoulos, Periklis (2021) Performance-enhanced amplified O-band WDM transmission using machine learning based equalization. In 2021 Conference on Lasers and Electro-Optics, CLEO 2021 - Proceedings. IEEE.. (doi:10.1364/cleo_si.2021.sth1f.3).

Record type: Conference or Workshop Item (Paper)

Abstract

We investigate the performance of a machine learning-based equalization in an amplified 4×50-Gb/s O-band WDM system. The results show that the scheme offers significant receiver sensitivity improvements over decision-feedback equalization, especially at more dispersive wavelengths.

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More information

Published date: 1 May 2021
Venue - Dates: CLEO 2021 Virtual Conference, Virtual, United States, 2021-05-09 - 2021-05-14
Related URLs:
Learn more about Zepler Institute for Photonics and Nanoelectronics research Learn more about Institute for Life Sciences research Learn more about Zepler Institute for Photonics and Nanoelectronics research

Identifiers

Local EPrints ID: 470948
URI: http://eprints.soton.ac.uk/id/eprint/470948
DOI: doi:10.1364/cleo_si.2021.sth1f.3
PURE UUID: 57208865-5e1c-48db-9970-52637f3a4de8
ORCID for Kyle R.H. Bottrill: ORCID iD orcid.org/0000-0002-9872-110X
ORCID for Naresh K. Thipparapu: ORCID iD orcid.org/0000-0002-5153-4737
ORCID for Yu Wang: ORCID iD orcid.org/0000-0001-5547-1668
ORCID for Jayanta K. Sahu: ORCID iD orcid.org/0000-0003-3560-6152
ORCID for David J. Richardson: ORCID iD orcid.org/0000-0002-7751-1058
ORCID for Periklis Petropoulos: ORCID iD orcid.org/0000-0002-1576-8034

Catalogue record

Date deposited: 21 Oct 2022 16:34
Last modified: 17 Mar 2024 03:41

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Contributors

Author: Yang Hong
Author: Stavros Deligiannidis
Author: Natsupa Taengnoi
Author: Kyle R.H. Bottrill ORCID iD
Author: Naresh K. Thipparapu ORCID iD
Author: Yu Wang ORCID iD
Author: Jayanta K. Sahu ORCID iD
Author: David J. Richardson ORCID iD
Author: Charis Mesaritakis
Author: Adonis Bogris
Author: Periklis Petropoulos ORCID iD

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