scholarly journals Joint Estimation of Symbol Rate and Chromatic Dispersion Using Delayed Multiplier for Optical Performance Monitoring

Photonics ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 58
Author(s):  
Junhao Ba ◽  
Zhiping Huang ◽  
Fangqi Shen ◽  
Junyu Wei
Keyword(s):  
Spectral Line ◽  
Pulse Shape ◽  
Performance Monitoring ◽  
Mean Squared Error ◽  
Chromatic Dispersion ◽  
Joint Estimation ◽  
Optical Performance ◽  
Optical Performance Monitoring ◽  
Squared Error ◽  
Symbol Rate

Symbol rate and chromatic dispersion (CD) are very important for optical performance monitoring. The CD, however, hinders the symbol rate detection. In this paper, we proposed a joint estimation of symbol rate and chromatic dispersion. We show that, when the signal conjugates and multiplies with the delayed replica, the spectral line can be restored. The proposed method provides a fast and simple solution for joint estimation as traditional tentative CD scanning is time consuming. The simulation shows that the root-mean-squared error (RMSE) for CD was 39.5 ps/nm and the symbol rate was 2.4 MHz. For the squared-root-raised-cosine (SRRC) pulse shape with a roll-off factor of 0.1, the experimental results show that 25,000 input samples were needed for an error-free estimation. The RMSE is 105.6 ps/nm and 63.5 kHz for CD and symbol rate, respectively.

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.

scholarly journals Going Deeper into OSNR Estimation with CNN

Photonics ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 402 ◽  
Author(s):  
Fangqi Shen ◽  
Jing Zhou ◽  
Zhiping Huang ◽  
Longqing Li
Keyword(s):  
Performance Monitoring ◽  
Mean Squared Error ◽  
Finite Impulse Response ◽  
Chromatic Dispersion ◽  
Optical Network ◽  
Estimation Method ◽  
Absolute Error ◽  
Optical Signal ◽  
Optical Performance Monitoring ◽  
Modulation Format

As optical performance monitoring (OPM) requires accurate and robust solutions to tackle the increasing dynamic and complicated optical network architectures, we experimentally demonstrate an end-to-end optical signal-to-noise (OSNR) estimation method based on the convolutional neural network (CNN), named OptInception. The design principles of the proposed scheme are specified. The idea behind the combination of the Inception module and finite impulse response (FIR) filter is elaborated as well. We experimentally evaluate the mean absolute error (MAE) and root-mean-squared error (RMSE) of the OSNR monitored in PDM-QPSK and PDM-16QAM signals under various symbol rates. The results suggest that the MAE reaches as low as 0.125 dB and RMSE is 0.246 dB in general. OptInception is also proved to be insensitive to the symbol rate, modulation format, and chromatic dispersion. The investigation of kernels in CNN indicates that the proposed scheme helps convolutional layers learn much more than a lowpass filter or bandpass filter. Finally, a comparison in performance and complexity presents the advantages of OptInception.

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