RESUMO
In this paper, two Stokes space (SS) analysis schemes for modulation format identification (MFI) are proposed. These schemes are based on singular value decomposition (SVD) and Radon transform (RT) for feature extraction. The singular values (SVs) are extracted from the SS projections for different modulation formats to discriminate between them. The SS projections are obtained at different optical signal-to-noise ratios (OSNRs) ranging from 11 to 30 dB for seven dual-polarized modulation formats. The first scheme depends on the SVDs of the SS projections on three planes, while the second scheme depends on the SVDs of the RTs of the SS projections. Different classifiers including support vector machine (SVM), decision tree (DT), and K-nearest neighbor (KNN) for MFI based on the obtained features are used. Both simulation and experimental setups are arranged and tested for proof of concept of the proposed schemes for the MFI task. Complexity reduction is studied for the SVD scheme by applying the decimation of the projections by two and four to achieve an acceptable classification rate, while reducing the computation time. Also, the effect of the variation of phase noise (PN) and state of polarization (SoP) on the accuracy of the MFI is considered at all OSNRs. The two proposed schemes are capable of identifying the polarization multiplexed modulation formats blindly with high accuracy levels up to 98%, even at low OSNR values of 12 dB, high PN levels up to 10 MHz, and SoP up to 45°.
