ONG members win JLT Best Paper Award (again)

Congratulations to Head of the ONG Polina Bayvel and former members Boris Karanov and Domanic Lavery on their winning publication ‘End-to-End Deep Learning of Optical Fiber Communications’ which reconsiders how optical fibre communication systems are designed using artificial neural networks.

Boris Karanov, first author on the paper said:

“To receive the Best Paper Award from the flagship journal in our community is fantastic recognition for our work – it’s a great honour! I believe this work to be an important contribution to the development of AI in optical networks. The outcome is efficient, reconfigurable and versatile transceivers which exploit the potential for high-speed data transmission over optical fibres to a greater extent.”

Read more in this departmental news post. 

As well as the 2021 JLT Best Paper Award, ONG members also received the accolade last year and in 2018 for ‘SSBI Mitigation and the Kramers–Kronig Scheme in Single-Sideband Direct-Detection Transmission with Receiver-Based Electronic Dispersion Compensation’ and ‘Replacing the Soft-Decision FEC Limit Paradigm in the Design of Optical Communication Systems’, respectively.

JLT Best Papers

End-to-End Deep Learning of Optical Fiber Communications
Boris Karanov, Mathieu Chagnon, Felix Thouin, Tobias A. Eriksson, Henning Bulow, Domanic Lavery, Polina Bayvel, and Laurent Schmalen
doi.org/10.1109/JLT.2018.2865109

SSBI Mitigation and the Kramers–Kronig Scheme in Single-Sideband Direct-Detection Transmission with Receiver-Based Electronic Dispersion Compensation
Zhe Li, M. Sezer Erkilinc, Kai Shi, Eric Sillekens, Lidia Galdino, Benn C. Thomsen, Polina Bayvel and Robert I. Killey
doi.org/10.1109/JLT.2017.2684298

Replacing the Soft-Decision FEC Limit Paradigm in the Design of Optical Communication Systems
Alex Alvarado, Erik Agrell, Domanic Lavery, Robert Maher and Polina Bayvel
doi.org/10.1109/JLT.2015.2450537