Design of Time-Domain Learned Volterra Equalisers for WDM Systems

We examine various design aspects of a learned time-domain multiple-input multiple-output (MIMO) Volterra-based equaliser and reveal their impact on the convergence and performance of the model. We show that appropriate parameter initialisation is vital for the model's convergence and scalabili...

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Bibliographic Details
Published in:	2024 International Conference on Optical Network Design and Modeling (ONDM) pp. 1 - 3
Main Authors:	Castro, Nelson, Boscolo, Sonia, Ellis, Andrew D., Sygletos, Stylianos
Format:	Conference Proceeding
Language:	English
Published:	IFIP 06-05-2024
Subjects:	machine learning MIMO communication nonlinearity equalisation Optical fiber networks Optical fibers optical fibre systems Optimization Scalability Time-domain analysis Volterra series Wavelength division multiplexing
Online Access:	Get full text
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Summary:	We examine various design aspects of a learned time-domain multiple-input multiple-output (MIMO) Volterra-based equaliser and reveal their impact on the convergence and performance of the model. We show that appropriate parameter initialisation is vital for the model's convergence and scalability to a higher number of channels. This design optimisation enables the first demonstration of a 7 × 7 operation of the MIMO algorithm at one step per span, achieving 1.5 dB effective signal-to-noise ratio improvement over single-channel nonlinear equalisation, hence significantly enhancing the transmission performance in a wavelength-division multiplexing scenario.
DOI:	10.23919/ONDM61578.2024.10582691