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Unsupervised support vector machines for nonlinear blind equalization in CO-OFDM
Giakoumidis, Ilias; Tsokanos, Athanasios; Ghanbarisabagh, Mohammad; Mhatli, Sofien; Barry, Liam P.
A novel blind nonlinear equalization (BNLE) technique based on the iterative re-weighted least square is experimentally demonstrated for single and multi-channel coherent optical orthogonal frequency-division multiplexing (CO-OFDM). The adopted BNLE combines, for the first time, a support vector machine-learning cost function with the classical Sato or Godard error functions and maximum likelihood recursive least-squares. At optimum launched optical power, BNLE reduces the fiber nonlinearity penalty by ~1 (16-QAM single-channel at 2000 km) and ~1.7 dB (QPSK multi-channel at 3200 km) compared to a Volterra-based NLE. The proposed BNLE is more effective for multi-channel configuration: 1) it outperforms the ‘gold-standard’ digital-back propagation; 2) for a high number of subcarriers the performance is better due to its capability of tackling inter-subcarrier four-wave mixing.
Keyword(s): Optical communication; Machine learning; Signal processing; Telecommunication; OFDM; Optical Fiber Communication; Fiber Nonlinearity Compensation
Publication Date:
Type: Journal article
Peer-Reviewed: Yes
Language(s): English
Institution: Dublin City University
Citation(s): Giakoumidis, Ilias and Tsokanos, Athanasios and Ghanbarisabagh, Mohammad and Mhatli, Sofien and Barry, Liam P. (2018) Unsupervised support vector machines for nonlinear blind equalization in CO-OFDM. Photonics Technology Letters (PTL), 30 (12). pp. 1091-1094. ISSN 1041-1135
Publisher(s): IEEE
File Format(s): application/pdf
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First Indexed: 2018-07-12 06:05:30 Last Updated: 2018-07-14 06:05:23