# Monitoring of OSNR Using an Improved Binary Particle Swarm Optimization and Deep Neural Network in Coherent Optical Systems

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## Abstract

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## 1. Introduction

## 2. Principles of the Proposed Method

#### 2.1. OSNR Monitoring Based on the Conventional DNN Trained with AHs

#### 2.2. OSNR Monitoring Based on the IBPSO-Based DNN Trained with AHs

#### 2.2.1. Principle of Particle Swarm Optimization (PSO)

#### 2.2.2. Principle of the IBPSO

## 3. Experimental Setup

## 4. Results and Discussion

## 5. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Cugini, F.; Paolucci, F.; Fresi, F.; Meloni, G.; Sambo, N.; Potí, L.; D’Errico, A.; Castoldi, P. Toward plug-and-play software-defined elastic optical networks. IEEE/OSA J. Lightwave Technol.
**2016**, 34, 1494–1500. [Google Scholar] [CrossRef] - Pan, Z.; Yu, C.; Willner, A.E. Optical performance monitoring for the next generation optical communication network. Opt. Fiber Technol.
**2010**, 16, 20–45. [Google Scholar] [CrossRef] - Velasco, L.; Shariati, B.; Vela, A.P.; Comellas, J.; Ruiz, M. Learning from the optical spectrum: Soft-failure identification and localization. In Proceedings of the Exposition (OFC), San Diego, CA, USA, 11–15 March 2018. [Google Scholar]
- Chan, C.K. Optical Performance Monitoring; Academic Press: Cambridge, MA, USA, 2010. [Google Scholar]
- Dong, Z.; Khan, F.N.; Sui, Q.; Zhong, K.; Lu, C.; Lau, A.P.T. Optical performance monitoring: A review of current and future technologies. IEEE/OSA J. Lightwave Technol.
**2016**, 34, 525–543. [Google Scholar] [CrossRef] - Choi, H.Y.; Lee, J.H.; Jun, S.B.; Chung, Y.H.; Shin, S.K.; Ji, S.K. Improved polarization-nulling technique for monitoring OSNR in WDM network. In Proceedings of the Optical Fiber Communication Conference and the National Fiber Optic Engineers Conference (OFC), Anaheim, CA, USA, 5–10 March 2006. [Google Scholar]
- Lin, X.; Yan, L. Multiple-channel OSNR monitoring using integrated planar lightwave circuit and fast Fourier transform techniques. In Proceedings of the IEEE Lasers and Electro-Optics Society (LEOS), Tucson, AZ, USA, 27–28 October 2003. [Google Scholar]
- Lundberg, L.; Sunnerud, H.; Johannisson, P. In-band OSNR monitoring of PM-QPSK using the Stokes parameters. In Proceedings of the Optical Fiber Communication Conference (OFC), Los Angeles, CA, USA, 22–26 March 2015. [Google Scholar]
- Chitgarha, M.R.; Khaleghi, S.; Daab, W.; Almaiman, A.; Ziyadi, M.; Mohajerin-Ariaei, A.; Rogawski, D.; Tur, M.; Touch, J.D.; Vusirikala, V.; et al. Demonstration of in-service wavelength division multiplexing optical-signal-to-noise ratio performance monitoring and operating guidelines for coherent data channels with different modulation formats and various baud rates. Opt. Lett.
**2014**, 39, 1605–1608. [Google Scholar] [CrossRef] - Chen, M.; Yang, J.; Zhang, N.; You, S. Optical signal-to-noise ratio monitoring based on four-wave mixing. Opt. Eng.
**2015**, 54, 56109. [Google Scholar] [CrossRef] - Wang, Z.; Yang, A.; Guo, P.; Lu, Y.; Qiao, Y. Nonlinearity-tolerant OSNR estimation method based on correlation function and statistical moments. Opt. Fiber Technol.
**2017**, 39, 5–11. [Google Scholar] [CrossRef] - Huang, Z.; Qiu, J.; Kong, D.; Tian, Y.; Li, Y.; Guo, H.; Hong, X.; Wu, J. A novel in-band OSNR measurement method based on normalized autocorrelation function. IEEE Photonics J.
**2018**, 10, 7903208. [Google Scholar] [CrossRef] - Schmogrow, R.; Nebendahl, B.; Winter, M.; Josten, A.; Hillerkuss, D.; Koenig, S.; Meyer, J.; Dreschmann, M.; Huebner, M.; Koos, C.; et al. Error vector magnitude as a performance measure for advanced modulation formats. IEEE Photonics Technol. Lett.
**2012**, 24, 61–63. [Google Scholar] [CrossRef] - Khan, F.N.; Teow, C.H.; Kiu, S.G.; Tan, M.C.; Zhou, Y.; Al-Arashi, W.H.; Lau, A.P.T.; Lu, C. Automatic modulation format/bit-rate classification and signal-to-noise ratio estimation using asynchronous delay-tap sampling. Comput. Electr. Eng.
**2015**, 47, 126–133. [Google Scholar] [CrossRef] - Do, C.C.; Zhu, C.; Tran, A.V. Data-aided OSNR estimation using low-bandwidth coherent receivers. IEEE Photonics Technol. Lett.
**2014**, 26, 1291–1294. [Google Scholar] [CrossRef] - Lecun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature
**2015**, 521, 436–444. [Google Scholar] [CrossRef] [PubMed] - Nguyen, G.; Dlugolinsky, S.; Bobák, M.; Tran, V.; García, Á.L.; Heredia, I.; Malík, P.; Hluchý, L. Machine learning and deep learning frameworks and libraries for large-scale data mining: A survey. Artif. Intell. Rev.
**2019**, 52, 77–124. [Google Scholar] [CrossRef] - Sun, R.; Wang, X.; Yan, X. Robust visual tracking based on convolutional neural network with extreme learning machine. Multimed. Tools Appl.
**2019**, 78, 7543–7562. [Google Scholar] [CrossRef] - Zhang, Z.; Geiger, J.; Pohjalainen, J.; Mousa, A.E.; Jin, W.; Schuller, B. Deep learning for environmentally robust speech recognition: An overview of recent developments. ACM Trans. Intel. Syst. Tec.
**2017**, 9, 49. [Google Scholar] [CrossRef] - Housseini, A.E.; Toumi, A.; Khenchaf, A. Deep learning for target recognition from SAR images. In Proceedings of the Detection Systems Architectures and Technologies (DAT), Algiers, Algeria, 20–22 February 2017. [Google Scholar]
- Salani, M.; Rottondi, C.; Tornatore, M. Routing and spectrum assignment integrating machine-learning-based QoT estimation in elastic optical networks. In Proceedings of the IEEE Conference on Computer Communications (INFOCOM), Paris, France, 29 April–2 May 2019. [Google Scholar]
- Nie, L.; Wang, M.; Zhang, L.; Yan, S.; Zhang, B.; Chua, T.S. Disease inference from health-related questions via sparse deep learning. IEEE Trans. Knowl. Data Eng.
**2015**, 27, 2107–2119. [Google Scholar] [CrossRef] - Zibar, D.; Schäffer, C. Machine Learning Concepts in Coherent Optical Communication Systems. In Proceedings of the Signal Processing in Photonic Communications (SSPCom), San Diego, CA, USA, 13–16 July 2014. [Google Scholar]
- Lin, X.; Dobre, O.A.; Ngatched, T.M.N.; Eldemerdash, Y.A.; Li, C. Joint modulation classification and OSNR estimation enabled by support vector machine. IEEE Photonics Technol. Lett.
**2018**, 30, 2127–2130. [Google Scholar] [CrossRef] - Cui, S.; He, S.; Shang, J.; Ke, C.; Fu, S.; Liu, D. Method to improve the performance of the optical modulation format identification system based on asynchronous amplitude histogram. Opt. Fiber Technol.
**2015**, 23, 13–17. [Google Scholar] [CrossRef] - Zhang, S.; Wang, M. Chromatic dispersion and OSNR monitoring based on generalized regression neural network. Elector-Opt. Technol. Appl.
**2018**, 33, 30–36. [Google Scholar] - Wang, D.; Zhang, M.; Li, Z.; Li, J.; Fu, M.; Cui, Y.; Chen, X. Modulation format recognition and OSNR estimation using CNN-based deep learning. IEEE Photonics Technol. Lett.
**2017**, 29, 1667–1670. [Google Scholar] [CrossRef] - Khan, F.N.; Zhong, K.; Zhou, X.; Al-Arashi, W.H.; Yu, C.; Lu, C.; Lau, A.P.T. Joint OSNR monitoring and modulation format identification in digital coherent receivers using deep neural network. Opt. Express
**2017**, 25, 17767–17776. [Google Scholar] [CrossRef] - Kennedy, J.; Eberhart, R. Particle swarm optimization. In Proceedings of the International Conference on Neural Networks (ICNN), Perth, Australia, 27 November–1 December 1995. [Google Scholar]
- Kiran, M.S. Particle swarm optimization with a new update mechanism. Appl. Soft Comput.
**2017**, 60, 670–678. [Google Scholar] [CrossRef] - Jiang, F.; Xia, H.; Tran, Q.A.; Ha, Q.M.; Tran, N.Q.; Hu, J. A new binary hybrid particle swarm optimization with wavelet mutation. Knowl. Based Syst.
**2017**, 130, 90–101. [Google Scholar] [CrossRef] - Balaji, S.; Revathi, N. A new approach for solving set covering problem using jumping particle swarm optimization method. Nat. Comput.
**2016**, 15, 503–517. [Google Scholar] [CrossRef] - Karami, H.; Karimi, S.; Bonakdari, H.; Shamshirband, S. Predicting discharge coefficient of triangular labyrinth weir using extreme learning machine, artificial neural network and genetic programming. Neural Comput. Appl.
**2018**, 29, 983–989. [Google Scholar] [CrossRef] - Arefi-Oskoui, S.; Khataee, A.; Vatanpour, V. Modeling and optimization of NLDH/PVDF ultrafiltration nanocomposite membrane using artificial neural network-genetic algorithm hybrid. ACS Comb. Sci.
**2017**, 19, 464–477. [Google Scholar] [CrossRef] - Saidi-Mehrabad, M.; Dehnavi-Arani, S.; Evazabadian, F.; Mahmoodian, V. An ant colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Comput. Ind. Eng.
**2015**, 86, 2–13. [Google Scholar] [CrossRef] - Tran, D.C.; Wu, Z.J.; Wang, Z.L.; Deng, C.S. A novel hybrid data clustering algorithm based on artificial bee colony algorithm and K-means. Chin. J. Electron.
**2015**, 24, 694–701. [Google Scholar] [CrossRef] - Pavao, L.V.; Borba, C.B.; Ravagnani, M. Heat exchanger network synthesis without stream splits using parallelized and simplified simulated annealing and particle swarm optimization. Chem. Eng. Sci.
**2017**, 158, 96–107. [Google Scholar] [CrossRef] - Sharmila, T.; Leo, L.M. Image up-scaling based convolutional neural network for better reconstruction quality. In Proceedings of the International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India, 6–8 April 2016. [Google Scholar]
- Lei, Z.; Yang, K. Sound sources localization using compressive beamforming with a spiral array. In Proceedings of the International Conference on Information and Communication Technologies (ICT), Xi’an, China, 24 April 2015. [Google Scholar]
- Kyriakides, I. Target tracking using adaptive compressive sensing and processing. Signal Process.
**2016**, 127, 44–55. [Google Scholar] [CrossRef] - Savory, S.J. Digital coherent optical receivers: Algorithms and subsystems. IEEE J. Sel. Top. Quantum Electron.
**2010**, 16, 1164–1178. [Google Scholar] [CrossRef] - Ashkzari, A.; Azizi, A. Introducing genetic algorithm as an intelligent optimization technique. Appl. Mech. Mater.
**2014**, 568, 793–797. [Google Scholar] [CrossRef] - Dorigo, M.; Blum, C. Ant colony optimization theory: A survey. Theor. Comput. Sci.
**2005**, 344, 243–278. [Google Scholar] [CrossRef] - Codetta-Raiteri, D.; Luigi, P. Dynamic Bayesian networks for fault detection, identification, and recovery in autonomous spacecraft. IEEE Trans. Syst. Man Cybern.
**2015**, 45, 13–24. [Google Scholar] [CrossRef] - Chuang, L.Y.; Yang, C.H.; Li, J.C. Chaotic maps based on binary particle swarm optimization for feature selection. Appl. Soft Comput.
**2011**, 11, 239–248. [Google Scholar] [CrossRef]

**Figure 1.**Schematic of the coherent optical receiver and digital signal processing (DSP) for optical signal-to-noise ratio (OSNR) monitoring (LO: local oscillator, PBS: polarization beam splitter, and ADC: analog-to-digital converter).

**Figure 2.**Constellation diagrams and amplitude histograms with Fourier fitting at three different OSNRs for PM-RZ-QPSK signals.

**Figure 3.**Constellation diagrams and amplitude histograms with Fourier fitting at three different OSNRs for PM-NRZ-16QAM signals.

**Figure 8.**Averaged error of estimated OSNR for (

**a**) PM-RZ-QPSK and (

**b**) PM-NRZ-16QAM signals versus AHs bin number processed by the particle swarm optimization optimized deep neural network (PSO-DNN) and DNN.

**Figure 9.**The sparse amplitude histogram of (

**a**) PM-RZ-QPSK signal and (

**b**) PM-NRZ-16QAM signal obtained by IBPSO.

**Figure 10.**True versus estimated OSNRs and errors for PM-RZ-QPSK signal with (

**a**) 100 bin numbers and (

**b**) 55 bin numbers using PSO-DNN in different OSNRs.

**Figure 11.**True versus estimated OSNRs and errors for PM-NRZ-16QAM signal with (

**a**) 100 bin numbers and (

**b**) 65 bin numbers using PSO-DNN in different OSNRs.

**Figure 12.**Average estimated errors for (

**a**) PM-RZ-QPSK signal and (

**b**) PM-NRZ-16QAM signal processed by artificial neural network (ANN), improved binary particle swarm optimization and deep neural network (IBPSO-DNN), and support vector machine (SVM).

**Table 1.**The average and maximum estimated errors of PM-RZ-QPSK and PM-NRZ-16QAM signals using ANN, IBPSO-DNN, and SVM.

Signal | ANN | IBPSO-DNN | SVM |
---|---|---|---|

Average/Maximum Error (dB) | Average/Maximum Error (dB) | Average/Maximum Error (dB) | |

PM-RZ-QPSK | 0.33/0.54 | 0.29/0.37 | 0.34/0.61 |

PM-NRZ-16QAM | 0.48/0.65 | 0.37/0.48 | 0.47/0.72 |

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**MDPI and ACS Style**

Sun, X.; Su, S.; Wei, J.; Guo, X.; Tan, X.
Monitoring of OSNR Using an Improved Binary Particle Swarm Optimization and Deep Neural Network in Coherent Optical Systems. *Photonics* **2019**, *6*, 111.
https://doi.org/10.3390/photonics6040111

**AMA Style**

Sun X, Su S, Wei J, Guo X, Tan X.
Monitoring of OSNR Using an Improved Binary Particle Swarm Optimization and Deep Neural Network in Coherent Optical Systems. *Photonics*. 2019; 6(4):111.
https://doi.org/10.3390/photonics6040111

**Chicago/Turabian Style**

Sun, Xiaoyong, Shaojing Su, Junyu Wei, Xiaojun Guo, and Xiaopeng Tan.
2019. "Monitoring of OSNR Using an Improved Binary Particle Swarm Optimization and Deep Neural Network in Coherent Optical Systems" *Photonics* 6, no. 4: 111.
https://doi.org/10.3390/photonics6040111