Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices
Paper in proceeding, 2024
We consider the problem of recovering spatially resolved polarization information from receiver Jones matrices. We introduce a physics-based learning approach, improving noise resilience compared to previous inverse scattering methods, while highlighting challenges related to model overparameterization.
Machine Learning
fiber-optic communications
Fiber Sensing
Author
Mohammad Farsi
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Christian Häger
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
Magnus Karlsson
Chalmers, Microtechnology and Nanoscience (MC2), Photonics
Erik Agrell
Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks
2024 Optical Fiber Communication Conference and Exhibition, OFC 2024 - Proceeding
21622701 (eISSN)
9781957171326 (ISBN)
Optical Fiber Communication Conference
San Diego California, ,
San Diego California, ,
Unlocking the Full-dimensional Fiber Capacity
Knut and Alice Wallenberg Foundation (KAW 2018.0090), 2019-07-01 -- 2024-06-30.
Areas of Advance
Information and Communication Technology
Roots
Basic sciences
Subject Categories
Communication Systems
DOI
10.48550/arXiv.2401.09917
ISBN
9781957171326