Abstract
machine learning (ML) methods, in particular, the k-means
clustering and support vector machine (SVM) to improve the
performance of the optical communication system based on the
nonlinear Fourier transform (NFT). The NFT system employs the
continuous NFT spectrum part to carry data up to 1000 km using
the 16-QAM OFDM modulation. We classify the performance of
the system in terms of BER versus signal power dependence.
We show that the NFT system performance can be improved
considerably by means of the ML techniques and that the
more advanced SVM method typically outperforms the k-means
clustering.
| Original language | English |
|---|---|
| Title of host publication | 2018 British and Irish Conference on Optics and Photonics, BICOP 2018 - Proceedings |
| Publisher | IEEE |
| ISBN (Electronic) | 978-153867361-4 |
| ISBN (Print) | 978-1-5386-7362-1 |
| DOIs | |
| Publication status | Published - 4 Mar 2019 |
| Event | 1st IEEE British and Irish Conference on Optics and Photonics (BICOP 2018) - London, United Kingdom Duration: 12 Dec 2018 → 14 Dec 2018 |
Conference
| Conference | 1st IEEE British and Irish Conference on Optics and Photonics (BICOP 2018) |
|---|---|
| Country | United Kingdom |
| City | London |
| Period | 12/12/18 → 14/12/18 |
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Bibliographical note
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Funding: European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreements No.751561 (MP) and No.713694 (OK), EPSRC project TRANSNET (EP/R035342/1) (OK, MK & SKT) and the Leverhulme Trust project (RPG-2018-063) (JEP & SKT).
Keywords
- Machine learning
- k-means clustering
- nonlinear Fourer transform
- optical communications
- support vector machine
Cite this
}
Unsupervised and supervised machine learning for performance improvement of NFT optical transmission. / Kotlyar, Oleksandr; Pankratova, Maryna; Kamalian Kopae, Morteza; Vasylchenkova, Anastasiia; Prilepsky, Jaroslaw E.; Turitsyn, Sergei K.
2018 British and Irish Conference on Optics and Photonics, BICOP 2018 - Proceedings. IEEE, 2019. 8658274.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Unsupervised and supervised machine learning for performance improvement of NFT optical transmission
AU - Kotlyar, Oleksandr
AU - Pankratova, Maryna
AU - Kamalian Kopae, Morteza
AU - Vasylchenkova, Anastasiia
AU - Prilepsky, Jaroslaw E.
AU - Turitsyn, Sergei K.
N1 - © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Funding: European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreements No.751561 (MP) and No.713694 (OK), EPSRC project TRANSNET (EP/R035342/1) (OK, MK & SKT) and the Leverhulme Trust project (RPG-2018-063) (JEP & SKT).
PY - 2019/3/4
Y1 - 2019/3/4
N2 - We apply both the unsupervised and supervisedmachine learning (ML) methods, in particular, the k-meansclustering and support vector machine (SVM) to improve theperformance of the optical communication system based on thenonlinear Fourier transform (NFT). The NFT system employs thecontinuous NFT spectrum part to carry data up to 1000 km usingthe 16-QAM OFDM modulation. We classify the performance ofthe system in terms of BER versus signal power dependence.We show that the NFT system performance can be improvedconsiderably by means of the ML techniques and that themore advanced SVM method typically outperforms the k-meansclustering.
AB - We apply both the unsupervised and supervisedmachine learning (ML) methods, in particular, the k-meansclustering and support vector machine (SVM) to improve theperformance of the optical communication system based on thenonlinear Fourier transform (NFT). The NFT system employs thecontinuous NFT spectrum part to carry data up to 1000 km usingthe 16-QAM OFDM modulation. We classify the performance ofthe system in terms of BER versus signal power dependence.We show that the NFT system performance can be improvedconsiderably by means of the ML techniques and that themore advanced SVM method typically outperforms the k-meansclustering.
KW - Machine learning
KW - k-means clustering
KW - nonlinear Fourer transform
KW - optical communications
KW - support vector machine
UR - https://ieeebicop.com/
UR - https://ieeexplore.ieee.org/document/8658274
UR - http://www.scopus.com/inward/record.url?scp=85063904926&partnerID=8YFLogxK
U2 - 10.1109/BICOP.2018.8658274
DO - 10.1109/BICOP.2018.8658274
M3 - Conference contribution
SN - 978-1-5386-7362-1
BT - 2018 British and Irish Conference on Optics and Photonics, BICOP 2018 - Proceedings
PB - IEEE
ER -