Abstract
We present a technique for modulation format recognition for heterogeneous reconfigurable optical networks. The method is based on Stokes space signal representation and uses a variational Bayesian expectation maximization machine learning algorithm. Differentiation between diverse common coherent modulation formats is successfully demonstrated numerically and experimentally. The proposed method does not require training or a constellation diagram to operate, is insensitive to polarization mixing or frequency offset and can be implemented in any receiver capable of measuring Stokes parameters.
| Original language | English |
|---|---|
| Journal | I E E E Photonics Technology Letters |
| Volume | 25 |
| Issue number | 21 |
| Pages (from-to) | 2129-2132 |
| ISSN | 1041-1135 |
| DOIs | |
| Publication status | Published - 2013 |
Keywords
- Coherent detection
- Polarization multiplexing
- Modulation format recognition (MFR)
- Modulation format detection (MFD)
- Modulation format identification (MFI)
- Stokes space
- Poincaré sphere
- Variational Bayesian expectation maximization (VBEM)
- Gaussian mixture models (GMM)