Intelligent Recognition of Optical Modulation Formats in Dynamic Communication Networks

Machine learning-based optical modulation format recognition is essential for dynamic optical networks. Convolutional Neural Networks (CNNs) can analyze signal space diagrams directly from raw data. Specifically, CNNs excel in image classification due to their ability to extract features and learn autonomously. In this study, signal space diagrams for various modulation formats (BPSK, QPSK, 8-QAM, 16-QAM, 32-QAM, and 64-QAM) are generated using an electrical constellation visualizer in Optisystem software, with the Optical Signal-to-Noise Ratio (OSNR) varying from 10 to 40 dB and the noise figure ranging from 5 to 25 dB. The proposed VGG-16 CNN architecture achieves 100% accuracy in modulation format recognition (MFR) for all six formats, utilizing minimal training data and fewer epochs. This approach can significantly enhance next-generation dynamic optical communication networks by enabling automatic detection and recognition of modulation formats.