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 duri...
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Veröffentlicht in: | Algorithms 2024-12, Vol.17 (12), p.553 |
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Sprache: | eng |
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Zusammenfassung: | 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. |
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ISSN: | 1999-4893 1999-4893 |
DOI: | 10.3390/a17120553 |