Generalizability of ML-Based Classification of State of Polarization Signatures Across Different Bands and Links
Övrigt konferensbidrag, 2025
We evaluate the Machine Learning (ML) model generalization for State of Polarization (SOP)-based event classification across spectral bands and links. Results show strong intra-system accuracy of up to 98.6% but limited cross-system generalizability, whereas multi-system training improves performance, highlighting the need for specific system-level knowledge.
Författare
Leyla Sadighi
Trinity College Dublin
Carlos Natalino Da Silva
Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk
Stefan Karlsson
Micropol Fiberoptics AB
Marco Ruffini
Trinity College Dublin
Eoin Kenny
HEAnet
Lena Wosinska
Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk
Marija Furdek Prekratic
Chalmers, Elektroteknik, Kommunikation, Antenner och Optiska Nätverk
51st European Conference on Optical Communication, ECOC
Copenhagen, Denmark,
Copenhagen, Denmark,
Styrkeområden
Informations- och kommunikationsteknik
Ämneskategorier (SSIF 2025)
Telekommunikation