PhD defense Joana Girard-Jollet: Subsea Optical Links Characterization Assisted by Machine Learning

Jury

• Polina Bayvel, University College London – UCL (UK), Reviewer
• Vincent Choqueuse, École nationale d’ingénieurs de Brest – ENIB (FR), Reviewer
• Chigo Okonkwo, Eindhoven University of Technology (NL), Examiner
• Yves Jaouën, Télécom Paris (FR), Examiner
• Jérémie Renaudier, Nokia Bell Labs (FR), Guest
• Ghaya Rekaya-Ben Othman, Télécom Paris (FR), Thesis supervisor
• Jean-Christophe Antona, Alcatel Submarine Networks – ASN (FR), Thesis co-supervisor

Abstract

With the growing demand for internet bandwidth, having robust optical communication systems is more crucial than ever. Beyond the advances in individual technologies and components, the performance of the communication system also depends on how intelligently these components are used and combined so that the overall configuration is optimized. Often, network performance is limited by pre-deployment predictions, making accurate performance estimation essential for optimization.

The objective of my thesis is to characterize and monitor key sources of distortion in subsea links, primarily to predict the network’s future performance with minimal margins. We focused on two physical impairments: nonlinear Kerr effects and polarization-dependent loss. The thesis addresses the challenges of performance estimation and network monitoring to introduce novel monitoring tools and provide guidelines for optimizing transmission systems.