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.
I E E E Photonics Technology Letters, 2013, Vol 25, Issue 21, p. 2129-2132
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)