dc.contributor.author | Giacoumidis, Elias | |
dc.contributor.author | Tsokanos, Athanasios | |
dc.contributor.author | Ghanbarisabagh, M. | |
dc.contributor.author | Mhatli, S. | |
dc.contributor.author | Barry, L. P. | |
dc.date.accessioned | 2018-06-19T17:37:29Z | |
dc.date.available | 2018-06-19T17:37:29Z | |
dc.date.issued | 2018-06-15 | |
dc.identifier.citation | Giacoumidis , E , Tsokanos , A , Ghanbarisabagh , M , Mhatli , S & Barry , L P 2018 , ' Unsupervised Support Vector Machines for Nonlinear Blind Equalization in CO-OFDM ' , IEEE Photonics Technology Letters , vol. 30 , no. 12 , pp. 1091 - 1094 . https://doi.org/10.1109/LPT.2018.2832617 | |
dc.identifier.issn | 1041-1135 | |
dc.identifier.uri | http://hdl.handle.net/2299/20183 | |
dc.description | This document is the Accepted Manuscript of the following article: E. Giacoumidis, et al, 'Unsupervised Support Vector Machines for Nonlinear Blind Equalization in CO-OFDM', Vol. 30 (12): 1091-1094, June 2018. Under embargo until 4 May 2020. The final, published version is available online at doi: https://doi.org/10.1109/LPT.2018.2832617 © 2018 IEEE | |
dc.description.abstract | 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. 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 and 2) for a high number of subcarriers the performance is better due to its capability of tackling inter-subcarrier four-wave mixing. | en |
dc.format.extent | 4 | |
dc.format.extent | 2636942 | |
dc.language.iso | eng | |
dc.relation.ispartof | IEEE Photonics Technology Letters | |
dc.subject | Optical OFDM | |
dc.subject | fiber nonlinearity compensation | |
dc.subject | machine learning | |
dc.subject | optical fiber communication | |
dc.subject | Electronic, Optical and Magnetic Materials | |
dc.subject | Atomic and Molecular Physics, and Optics | |
dc.subject | Electrical and Electronic Engineering | |
dc.title | Unsupervised Support Vector Machines for Nonlinear Blind Equalization in CO-OFDM | en |
dc.contributor.institution | School of Computer Science | |
dc.description.status | Peer reviewed | |
dc.date.embargoedUntil | 2020-05-04 | |
dc.identifier.url | http://www.scopus.com/inward/record.url?scp=85046469780&partnerID=8YFLogxK | |
rioxxterms.versionofrecord | 10.1109/LPT.2018.2832617 | |
rioxxterms.type | Journal Article/Review | |
herts.preservation.rarelyaccessed | true | |