Advanced machine learning-powered tunable optical signal processor for precise chromatic dispersion compensation in analog B5G/6G mobile fronthaul networks

Panagiotis Toumasis; George Brestas; Evrydiki Kyriazi; Konstantina Kanta; Giannis Poulopoulos; Giannis Giannoulis; Dimitris Apostolopoulos; Hercules Avramopoulos

doi:10.1117/12.3017029

18 June 2024 Advanced machine learning-powered tunable optical signal processor for precise chromatic dispersion compensation in analog B5G/6G mobile fronthaul networks

Panagiotis Toumasis, George Brestas, Evrydiki Kyriazi, Konstantina Kanta, Giannis Poulopoulos, Giannis Giannoulis, Dimitris Apostolopoulos, Hercules Avramopoulos

Author Affiliations +

Proceedings Volume 13017, Machine Learning in Photonics; 130170Z (2024) https://doi.org/10.1117/12.3017029
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

We introduce an ML-driven optical signal processor for dispersion compensation in B5G RAN. This approach leverages a reconfigurable, energy-efficient MRR structure, effectively mitigating power fading. Our study exploits M-QAM digitally up-converted A-IFoF transmission simulation results to fiber distances up to 25km to prove the capabilities of the designed machine learning-based analog photonic processing unit. Analytical MATLAB calculations show enhanced output power, corroborated by VPI simulations demonstrating improved EVM values, including 16.9% EVM for 1GBd QPSK at 8.5GHz over 25km, meeting the 3GPP standards.

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

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Panagiotis Toumasis, George Brestas, Evrydiki Kyriazi, Konstantina Kanta, Giannis Poulopoulos, Giannis Giannoulis, Dimitris Apostolopoulos, and Hercules Avramopoulos "Advanced machine learning-powered tunable optical signal processor for precise chromatic dispersion compensation in analog B5G/6G mobile fronthaul networks", Proc. SPIE 13017, Machine Learning in Photonics, 130170Z (18 June 2024); https://doi.org/10.1117/12.3017029

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