Machine learning-enhanced dual-task processor for next generation access networks

George Brestas; Giannis Kanakis; Maria Spyropoulou; Hercules Avramopoulos

doi:10.1117/12.3016286

18 June 2024 Machine learning-enhanced dual-task processor for next generation access networks

George Brestas, Giannis Kanakis, Maria Spyropoulou, Hercules Avramopoulos

Proceedings Volume 13017, Machine Learning in Photonics; 130170W (2024) https://doi.org/10.1117/12.3016286
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

We propose a dual-task, all-optical processor controlled by a Machine Learning-based digital optimizer. This innovative system compensates for signal impairments in Intensity Modulation/Direct Detection (IM/DD) and mitigates bandwidth limitations, reducing the need for power-hungry Digital Signal Processing (DSP). The synergy between the optical processor and digital optimizer, operating with the Tree-structured Parzen Estimator algorithm, forms a photonic Reservoir Computing approach that enhances signal performance and reach. This versatile system can function as an all-optical equalizer and chromatic dispersion compensation filter, outperforming traditional electrical counterparts. The proposed scheme, offers a cost-effective and efficient solution for next-generation access networks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

George Brestas, Giannis Kanakis, Maria Spyropoulou, and Hercules Avramopoulos "Machine learning-enhanced dual-task processor for next generation access networks", Proc. SPIE 13017, Machine Learning in Photonics, 130170W (18 June 2024); https://doi.org/10.1117/12.3016286

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