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Physics-based Distributed Polarization Sensing

This repository hosts code accompanying the paper [1] and the full source code will be released upon completed reviews.

Inverse Scattering Algorithm:

The code is based on PyTorch.

The implementation is based on [2].

  • The algorithm is susceptible to channel estimation noise
  • We have proposed a learning-based approach that has a higher tolerance to estimation noise in [1].
  • The algorithm experiences numerical instability when the number of segments increases (N>10)

Required Packages

  • Unzip the package to your local directory, then
  • Run 'pip install -r requirements.txt' to download the required packages;

Usage Example

main(num_segments = 5)

Additional Information

If you decide to use the source code for your research, please make sure to cite the original paper and ours:

  • [1] M. Farsi, C. Häger, M. Karlsson, E. Agrell, "[Learning to Extract Distributed Polarization Sensing Data from Noisy Jones Matrices]", in proc. Optical Fiber Communication Conference (submitted), 2024.
  • [2] R. Noé et al, "Polarization-Dependent Loss: New Definition and Measurement Techniques", in Journal of Lightwave Technology, vol. 33, no. 10, pp. 2127-2138, 2015.

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The implementation of inverse scattering algorithm (ISA).

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