Optical Frequency Comb Noise Characterization Using Machine Learning
Abstract
A novel tool, based on Bayesian filtering framework and expectation maximization algorithm, is numerically and experimentally demonstrated for accurate frequency comb noise characterization. The tool is statistically optimum in a mean-square-error-sense, works at wide range of SNRs and offers more accurate noise estimation compared to conventional methods.
- Publication:
-
arXiv e-prints
- Pub Date:
- April 2019
- DOI:
- 10.48550/arXiv.1904.11951
- arXiv:
- arXiv:1904.11951
- Bibcode:
- 2019arXiv190411951B
- Keywords:
-
- Electrical Engineering and Systems Science - Signal Processing;
- Physics - Optics;
- Quantum Physics