Modulation format/bit rate recognition based on principal component analysis (PCA) and artificial neural networks (ANNs)

In this paper, we propose a novel approach to recognize modulation formats/bit rates. Firstly, the polarization multiplexed (pol-mux) signal is splitted by a polarization beam splitter (PBS) to generate asynchronous delay tap plots (ADTPs). The ADTP patterns are analyzed by principal component analysis (PCA), and the principal eigenvalues and the principal eigenvectors are evaluated. Afterwards, the ADTPs are converted into the weight vectors by projecting them onto the principal eigenvector directions. The weight vectors from the training signals are sent to train the artificial neural network (ANN) and the trained ANN can distinguish the modulation formats/bit rates of the detected signals by taking their weight vectors as the input. The proposed PCA + ANN method allows modulation format/bit rate recognition for 16 different types of data streams. 14 traditional modulate formats achieve 99% recognition accuracy with 20 dB OSNR, which is a significant improvement in comparison with the existing methods. 2 OFDM signals can also be distinguished with about 85% accuracy.