Work

Learned Volterra models for nonlinearity equalization in wavelength-division multiplexed systems

Year: 2025

Type: article

Abstract: Multichannel digital equalization has proved capable of mitigating fiber-induced inter-channel impairments which constitute a main limitation in wavelength-division multiplexed systems. In this paper, we present three multiple-input-multiple-output learned equalization architectures based on the inverse Volterra series transfer function (IVSTF): a fully parallel frequency-domain approach (L-IVSTF), a field-enhanced version with improved adaptability (FE L-IVSTF), and a time-domain implementation (L-simIVSTF). We demonstrate that machine-learning optimization enables efficient multichannel equalization for all the structures, with the 9 × 9 L-simIVSTF and FE L-IVSTF equalizers achieving an average signal-to-noise ratio improvement of ∼2.2 dB over chromatic dispersion compensation. The three models are thoroughly characterized and compared in terms of performance and computational cost, pinpointing the FE L-IVSTF as the model with the best trade-off.

Source: Optics Express

DOI: 10.1364/oe.554077

Authors: Nelson Castro, Sonia Boscolo, Andrew D. Ellis, Stylianos Sygletos

Language: English

Topic: Optical Network Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,270

Funders: North Atlantic Treaty Organization, Engineering and Physical Sciences Research Council

Awards: G3903522246, Transforming networks - building an intelligent optical infrastructure (TRANSNET), Superchannel Transponders for the Big Data Era