Stokes Space Modulation Format Identification for Optical Signals Using Probabilistic Neural Network - Ming Hao; Lianshan Yan; Anlin Yi; Lin Jiang; Yan Pan; Wei Pan; Bin Luo (IEEE Photonics Journal)
A Stokes space modulation format identification (MFI) method using probabilistic neural network (PNN) is proposed for coherent optical receivers. According to amplitude histograms obtained by the distribution of Stokes vectors on the s1 axis, the incoming signals are first classified into PDM-mPSK, PDM-16QAM, and PDM-64 QAM signals based on PNN. To further identify PDM-mPSK signals, the constellation feature of Stokes vectors on s2–s3 plane is extracted by image processing techniques and then processed by PNN. The high identification accuracy is demonstrated via numerical simulations with 28-Gbaud PDM-QPSK, PDM-8PSK, PDM-16QAM, and PDM-64QAM signals over a wide optical signal-to-noise ratio range. Owing to the characteristic of PNN, the proposed MFI method has simple training process, small training data size and a small number of required symbols. Proof-of-concept experiments have been implemented to verify the effectiveness of the proposed MFI method among 28-Gbaud PDM-QPSK, PDM-8PSK, and PDM-16QAM signals.