Artificial intelligence based optical performance monitoring

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Palash Rai ◽  
Rahul Kaushik
Keyword(s):  
Communication Networks ◽  
Performance Monitoring ◽  
Signal To Noise Ratio ◽  
Chromatic Dispersion ◽  
Optical Signal ◽  
Optical Performance ◽  
Optical Performance Monitoring ◽  
Eye Diagram ◽  
Artificial Neural Network Ann ◽  
Data Signal

Abstract In this paper, a technique for optical performance monitoring (OPM) using deep learning-based artificial neural network (ANN) is implemented. ANN is trained with parameters derived from eye-diagram for the identification of optical signal to noise ratio (OSNR), chromatic dispersion (CD) and polarisation mode dispersion (PMD) simultaneously and independently in a 10 Gb/s system with non-return-to-zero (NRZ) on-off keying (OOK) data signal. ANN-based OPM confirms that the proposed approach can provide reliable estimated results. The mean squared errors for OSNR, CD and differential group delay (DGD) are found to be 4.6071 dB, 0.0417 ps/nm/km and 0.0016 ps/km, respectively. The proposed technique may be utilized in analyzing the signals of future heterogeneous optical communication networks intelligently.

Optical SNR estimation using machine learning

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Palash Rai ◽  
Rahul Kaushik
Keyword(s):  
Machine Learning ◽  
Signal Analysis ◽  
Nearest Neighbor ◽  
Signal To Noise Ratio ◽  
Optical Signal ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Eye Diagram ◽  
Test Instrument ◽  
Data Signal

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.

scholarly journals Optical performance monitoring using the novel parametric asynchronous eye diagram

2012 ◽  
Vol 20 (9) ◽  
pp. 9851 ◽  
Author(s):  
Vtor Ribeiro ◽  
Liliana Costa ◽  
Mrio Lima ◽  
Antnio L. J. Teixeira
Keyword(s):  
Performance Monitoring ◽  
The Novel ◽  
Optical Performance ◽  
Optical Performance Monitoring ◽  
Eye Diagram

scholarly journals Joint Fiber Nonlinear Noise Estimation, OSNR Estimation and Modulation Format Identification Based on Asynchronous Complex Histograms and Deep Learning for Digital Coherent Receivers

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 380
Author(s):  
Shuailong Yang ◽  
Liu Yang ◽  
Fengguang Luo ◽  
Bin Li ◽  
Xiaobo Wang ◽  
...  
Keyword(s):  
Performance Monitoring ◽  
Signal To Noise Ratio ◽  
Optical Network ◽  
Power Estimation ◽  
Optical Signal ◽  
Training Data ◽  
Noise Power ◽  
Optical Performance Monitoring ◽  
Modulation Format ◽  
Coherent Receivers

In this paper, asynchronous complex histogram (ACH)-based multi-task artificial neural networks (MT-ANNs), are proposed to realize modulation format identification (MFI), optical signal-to-noise ratio (OSNR) estimation and fiber nonlinear (NL) noise power estimation simultaneously for coherent optical communication. Optical performance monitoring (OPM) is demonstrated with polarization mode multiplexing (PDM), 16 quadrature amplitude modulation (QAM), PDM-32QAM, as well as PDM-star 16QAM (S-16QAM) for the first time. The range of launched power is −3 to −2 dBm with a fiber link of 160–1600 km. Then, the accuracy of MFI reaches 100%. The average root mean square error (RMSE) of OSNR estimation can reach 0.37 dB. The average RMSE of NL noise power estimation can reach 0.25 dB. The results show that the monitoring scheme is robust to the increase of fiber length, and the solution can monitor more optical network parameters with better performance and fewer training data, simultaneously. The proposed ACH MT-ANN has certain reference significance for the future long-haul coherent OPM system.

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