m-QAM Receiver Based on Data Stream Spectral Clustering for Optical Channels Dominated by Nonlinear Phase Noise

Optical communication systems face challenges like nonlinear noises, particularly Kerr-induced phase noise, which worsens with higher-order m-QAM formats due to their dense data-symbol sets. Advanced signal processing, including machine learning, is increasingly used to enhance signal integrity during demodulation. This paper explores the application of a spectral clustering algorithm adapted to deal with data streaming to mitigate nonlinear noise in long-haul optical channels dominated by nonlinear phase noise, offering a promising solution to a pressing issue. The spectral clustering algorithm was adapted to handle data streams, enabling potential real-time applications. Additionally, it was combined with a demapping process for m-QAM to resolve labeling inconsistencies when processing windowed data. We demonstrate that the spectral clustering algorithm outperforms the k-means algorithm in the face of nonlinear phase noise in −90, −100, and −110 dBc/Hz scenarios at 1 MHz in a simulated 10 GHz symbol rate channel.