Joint Modulation Classification and OSNR Estimation Enabled by Support Vector Machine

By adopting the cumulative distribution function of the received signal's amplitude as feature, a support vector machine-based algorithm is proposed to jointly classify the modulation format and estimate the optical signal-to-noise ratio (OSNR) in coherent optical communication systems. Three c...

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Bibliographic Details
Published in:IEEE photonics technology letters 2018-12, Vol.30 (24), p.2127-2130
Main Authors: Lin, Xiang, Dobre, Octavia A., Ngatched, Telex M. N., Eldemerdash, Yahia A., Li, Cheng
Format: Article
Language:English
Subjects:
Algorithms
Amplitudes
Classification
Computer simulation
cumulative distribution function
Distribution functions
Estimation
machine learning
Modulation
modulation classification
Neural networks
Optical communication
Optical communication systems
Optical noise
Optical receivers
optical signal-to-noise ratio
Quadrature amplitude modulation
Signal processing algorithms
Signal to noise ratio
support vector machine
Support vector machines
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Summary:By adopting the cumulative distribution function of the received signal's amplitude as feature, a support vector machine-based algorithm is proposed to jointly classify the modulation format and estimate the optical signal-to-noise ratio (OSNR) in coherent optical communication systems. Three commonly-used quadrature-amplitude modulation (QAM) formats are considered. Numerical simulations have been carried out in the OSNR ranges from 5 to 30 dB, and results show that the proposed algorithm achieves a very good modulation classification (MC) performance, as well as high OSNR estimation accuracy with a maximum estimation error of 0.8 dB. Optical back-to-back experiments are also conducted in OSNR ranges of interest. A 99% average correct MC rate is observed, and mean OSNR estimation errors of 0.38, 0.68, and 0.62 dB are noticed for 4-QAM, 16-QAM, and 64-QAM, respectively. Furthermore, compared with the neural networks-based joint estimation algorithm, the proposed algorithm attains better performance with comparable complexity.
ISSN:1041-1135
1941-0174
DOI:10.1109/LPT.2018.2878530