Optical SNR estimation using machine learning
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Palash Rai
and
Rahul Kaushik
Abstract
A technique for the estimation of an optical signal-to-noise ratio (OSNR) using machine learning algorithms has been proposed. The algorithms are trained with parameters derived from eye-diagram via simulation in 10 Gb/s On-Off Keying (OOK) nonreturn-to-zero (NRZ) data signal. The performance of different machine learning (ML) techniques namely, multiple linear regression, random forest, and K-nearest neighbor (K-NN) for OSNR estimation in terms of mean square error and R-squared value has been compared. The proposed methods may be useful for intelligent signal analysis in a test instrument and to monitor optical performance.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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© 2021 Walter de Gruyter GmbH, Berlin/Boston
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- Performance signature of transceiver communication system based on the cascade uniform fiber Bragg grating devices
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- All-optical simultaneous XOR-AND operation using 1-D periodic nonlinear material
- Implementation of frequency encoded all optical reversible logic
- All-optical frequency-encoded Toffoli gate
- Performance analysis of all optical 2 × 1 multiplexer in 2D photonic crystal structure
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