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.
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Borkowski et al. (2013) studied this question.
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