A CONCEPTUAL STUDY ON ADVANCED MODULATION TECHNIQUES IN OPTICAL COMMUNICATION
Keywords:
Optical Communication, Advanced Modulation Formats, Signal-to-Noise Ratio Estimation, Machine Learning, Modulation Format IdentificationAbstract
This study explores the integration of advanced modulation techniques with machine learning-based signal processing to enhance signal-to-noise ratio (SNR) estimation and modulation format identification in modern optical communication systems. By analyzing modulation formats such as QPSK, 16-QAM, and 64-QAM, the research highlights the trade-offs between spectral efficiency and robustness under varying channel conditions. The application of machine learning models, including convolutional neural networks and multi-task learning frameworks, enables real-time monitoring of optical signal quality and adaptive modulation classification. Additionally, the study investigates reconfigurable modulation schemes like ASK and FSK, which support flexible all-optical format conversions, crucial for next-generation high-capacity networks. The findings aim to contribute towards designing scalable, intelligent optical networks that meet future demands for efficiency, flexibility, and high data throughput.
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