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DBSCAN for nonlinear equalization in high-capacity multi-carrier optical communications

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arxiv 1902.01198 v1 pith:AZ7ZVO2B submitted 2019-01-17 eess.SP

classification eess.SP
keywords opticaldbscancommunicationsclusteringcoherentmodifiedmulti-carriernonlinear
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Coherent optical multi-carrier communications have recently dominated metro-regional and long-haul optical communications. However, the major obstacle of networks involving coherent multi-carrier signals such as coherent optical orthogonal frequency-division multiplexing (CO-OFDM) is the fiber-induced nonlinearity and the parametric noise amplification from cascaded optical amplifiers which results in significant nonlinear distortion among subcarriers. Here, we present the first nonlinear equalizer in optical communications using the traditional Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a novel modified version of DBSCAN which combines K-means clustering on the noisy un-clustered symbols. For a 24.72 Gbit/sec differential quaternary phase-shift keying (DQPSK) CO-OFDM system, the modified DBSCAN can increase the signal quality-factor by up to 2.158 dB compared to linear equalization at 500 km of transmission. The modified DBSCAN slightly outperforms the traditional DBSCAN, fuzzy-logic C-means, hierarchical and conventional K-means clustering at high launched optical powers.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Survey on Machine and Deep Learning for Optical Communications

    eess.SP 2024-12 conditional novelty 4.0 of 10

    A survey that catalogs ML and DL algorithms for optical fiber, network, and wireless systems, with quantitative tables of reported gains and comparisons to conventional methods.

  2. A Survey on Machine Learning for Optical Communication [Machine Learning View]

    eess.SP 2019-08 reject novelty 2.0 of 10

    A survey of machine learning for optical communication that classifies many references by algorithm type, but contains factual inaccuracies and an unsupported first-time claim.

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