# Modulation Format Identification and OSNR Monitoring Based on Multi-Feature Fusion Network

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Proposed Scheme

#### 2.1. Data Pre-Processing

#### 2.2. System Structure

_{Conv}× K

_{Conv}is the output image size of the convolutional layer. F × F is the convolutional kernel size. S is the stride, and P is the padding size. The activation function is the nonlinear rectification unit ReLU.

_{pooling}is the output image size of the pooling layer.

_{out}extracted by a branching network contains more information about the location of constellation points, while the horizontal projection feature J

_{out}extracted by another branching network has more semantic information. Figure 3 presents the feature fusion module that reasonably fuses the feature information of the two branch networks and introduces adaptive feature weights. The model automatically decides its weight parameters based on the feature distribution of the data and fuses the features at the fusion layer. The fused features C

_{f}are calculated using Equation (6).

_{1}and ω

_{2}are obtained from Equation (7).

_{i}is the normalized weight and ∑ ω

_{i}= 1, α

_{i}is the initialized weight parameter. α

_{i}is added to the parameters updated by the optimizer so that α

_{i}is optimized in the direction of minimizing the loss function.

## 3. Simulation Setup

^{17}was mapped into MQAM and MPSK signals, and the electrical signals were generated by an arbitrary waveform generator. A continuous wave (CW) laser with a center frequency of 193.1 THz, a line width of 0.1 MHz, and a power of 10 dBm was utilized to generate the optical carrier signal. It was required by the system to drive the dual Mach-Zehnder Modulator (Dual MZM). The modulated signal was transmitted through a standard single-mode fiber (SSMF) with a length of 80 km, an attenuation of 0.2 dB/km, and a dispersion of 16.75 ps/nm/km. Optical amplifiers (OA) were deployed to make up for the losses generated during transmission. At the optical receiving end, the coherent receiver contained a photodetector. The local oscillator (LO) operated at 1550 nm with a linewidth of 0.1 MHz. It mixed with the optical signal and subsequently converted the optical signal into an electrical signal using the photodetector. The constellation diagrams of the electrical signals were analyzed and collected using a constellation diagram analyzer. The generated constellation images were then sent to the MFF-Net-based digital signal processing module.

## 4. Results and Discussion

#### 4.1. Modulation Format Identification

#### 4.2. OSNR Monitoring

_{Conv}and P

_{fc}represent the number of parameters of the convolutional layer and fully connected layer, respectively. The dimension of the convolution kernel is k × k, where c and n correspond to the number of channels in the input feature map and the output feature map for the layer, respectively.

^{5}, 1.63 × 10

^{8}, 4.27 × 10

^{6}, and 4.76 × 10

^{5}. In addition, we also compared CNN and LSTM, which are shown in the last two columns of Table 1. The OSNR monitoring accuracy is 97.13% and 98.53%, respectively, and the number of parameters is 2.51 × 10

^{6}and 4.80 × 10

^{5}, correspondingly. There is no significant advantage compared with the model in this paper, both in terms of the number of parameters and accuracy. In the MFF-Net model, we replaced the flattening layer with the global average pooling layer, which greatly reduces the number of model parameters. It can be concluded that MFF-Net has the lowest number of parameters and the highest accuracy rate, which is superior compared with other models.

#### 4.3. Model Structure

#### 4.4. Robustness Analysis

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Saif, W.S.; Esmail, M.A.; Ragheb, A.M.; Alshawi, T.A.; Alshebeili, S.A. Machine Learning Techniques for Optical Performance Monitoring and Modulation Format Identification: A Survey. IEEE Commun. Surv. Tutor.
**2020**, 22, 2839–2882. [Google Scholar] [CrossRef] - Zhang, Y.; Zhou, P.; Dong, C.; Lu, Y.; Chuanqi, L. Intelligent equally weighted multi-task learning for joint OSNR monitoring and modulation format identification. Opt. Fiber Technol.
**2022**, 71, 102931. [Google Scholar] [CrossRef] - Saif, W.S.; Ragheb, A.M.; Alshawi, T.A.; Alshebeili, S.A. Optical Performance Monitoring in Mode Division Multiplexed Optical Networks. J. Light. Technol.
**2021**, 39, 491–504. [Google Scholar] [CrossRef] - Pan, Z.; Yu, C.; Willner, A.E. Optical performance monitoring for the next generation optical communication networks. Opt. Fiber Technol.
**2010**, 16, 20–45. [Google Scholar] [CrossRef] - LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature
**2015**, 521, 436–444. [Google Scholar] [CrossRef] [PubMed] - Tan, S.; Mayrovouniotis, M.L. Deep learning in photonics: Introduction. Photonics Res.
**2021**, 9, 2327–9125. [Google Scholar] - Wang, D.; Zhang, M.; Li, J.; Li, Z.; Li, J.; Song, C.; Chen, X. Intelligent constellation diagram analyzer using convolutional neural network-based deep learning. Opt. Express
**2017**, 25, 17150–17166. [Google Scholar] [CrossRef] - Wan, Z.; Yu, Z.; Shu, L.; Zhao, Y.; Zhang, H.; Xu, K. Intelligent optical performance monitor using multi-task learning based artificial neural network. Opt. Express
**2019**, 27, 11281–11291. [Google Scholar] [CrossRef] [Green Version] - Tanimura, T.; Hoshida, T.; Kato, T.; Watanabe, S.; Morikawa, H. Convolutional Neural Network-Based Optical Performance Monitoring for Optical Transport Networks. J. Opt. Commun. Netw.
**2019**, 11, A52–A59. [Google Scholar] [CrossRef] - Wang, D.; Wang, M.; Zhang, M.; Zhang, Z.; Yang, H.; Li, J.; Li, J.; Chen, X. Cost-effective and data size—Adaptive OPM at intermediated node using convolutional neural network-based image processor. Opt. Express
**2019**, 27, 9403–9419. [Google Scholar] [CrossRef] - Yu, Z.; Wan, Z.; Shu, L.; Hu, S.; Zhao, Y.; Zhang, J.; Xu, K. Loss weight adaptive multi-task learning based optical performance monitor for multiple parameters estimation. Opt. Express
**2019**, 27, 37041–37055. [Google Scholar] [CrossRef] [PubMed] - 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 networks. Opt. Express
**2017**, 25, 17767–17776. [Google Scholar] [CrossRef] - Zheng, J.; Lv, Y. Likelihood-Based Automatic Modulation Classification in OFDM with Index Modulation. IEEE Trans. Veh. Technol.
**2018**, 67, 8192–8204. [Google Scholar] [CrossRef] - Lin, X.; Eldemerdash, Y.A.; Dobre, O.A.; Zhang, S.; Li, C. Modulation Classification Using Received Signal’s Amplitude Distribution for Coherent Receivers. IEEE Photonics Technol. Lett.
**2017**, 29, 1872–1875. [Google Scholar] [CrossRef] [Green Version] - Liu, G.; Proietti, R.; Zhang, K.; Lu, H.; Yoo, S.B. Blind modulation format identification using nonlinear power transformation. Opt. Express
**2017**, 25, 30895–30904. [Google Scholar] [CrossRef] [PubMed] - Yi, A.; Liu, H.; Yan, L.; Jiang, L.; Pan, Y.; Luo, B. Amplitude variance and 4th power transformation based modulation format identification for digital coherent receiver. Opt. Commun.
**2019**, 452, 109–115. [Google Scholar] [CrossRef] - Khan, F.N.; Zhou, Y.; Lau, P.T.A.; Lu, C. Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks. Opt. Express
**2012**, 20, 12422–12431. [Google Scholar] [CrossRef] [PubMed] [Green Version] - 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] - Wang, Z.; Yang, A.; Guo, P.; He, P. OSNR and nonlinear noise power estimation for optical fiber communication systems using LSTM based deep learning technique. Opt. Express
**2018**, 26, 21346–21357. [Google Scholar] [CrossRef] - Wang, C.; Fu, S.; Wu, H.; Luo, M.; Li, X.; Tang, M.; Liu, D. Joint OSNR and CD monitoring in digital coherent receiver using long short-term memory neural network. Opt. Express
**2019**, 27, 6936–6945. [Google Scholar] [CrossRef] - Xia, L.; Zhang, J.; Hu, S.; Zhu, M.; Song, Y.; Qiu, K. Transfer learning assisted deep neural network for OSNR estimation. Opt. Express
**2019**, 27, 19398–19406. [Google Scholar] [CrossRef] - Zhang, J.; Gao, M.; Ma, Y.; Zhao, Y.; Chen, W.; Shen, G. Intelligent adaptive coherent optical receiver based on convolutional neural network and clustering algorithm. Opt. Express
**2018**, 26, 18684–18698. [Google Scholar] [CrossRef] [PubMed] - Eltaieb, R.A.; Farghal, A.E.A.; Ahmed, H.E.-D.H.; Saif, W.S.; Ragheb, A.M.; Alshebeili, S.A.; Shalaby, H.M.H.; El-Samie, F.E.A. Efficient Classification of Optical Modulation Formats Based on Singular Value Decomposition and Radon Transformation. J. Light. Technol.
**2019**, 38, 619–631. [Google Scholar] [CrossRef] - Zhang, W.; Zhu, D.; Zhang, N.; Xu, H.; Zhang, X.; Zhang, H.; Li, Y. Identifying Probabilistically Shaped Modulation Formats Through 2D Stokes Planes With Two-Stage Deep Neural Networks. IEEE Access
**2020**, 8, 6742–6750. [Google Scholar] [CrossRef] - Shen, F.; Zhou, J.; Huang, Z.; Li, L. Going Deeper into OSNR Estimation with CNN. Photonics
**2021**, 8, 402. [Google Scholar] [CrossRef] - Yang, L.; Xu, H.; Bai, C.; Yu, X.; You, K.; Sun, W.; Guo, J.; Zhang, X.; Liu, C. Modulation Format Identification Using Graph-Based 2D Stokes Plane Analysis for Elastic Optical Network. IEEE Photonics J.
**2021**, 13, 7901315. [Google Scholar] [CrossRef] - Chen, X.; Li, Y.; Li, Y. Multi-feature fusion point cloud completion network. World Wide Web
**2021**, 25, 1551–1564. [Google Scholar] [CrossRef] - Zhang, Z.; Li, X.; Gan, C. Multimodality Fusion for Node Classification in D2D Communications. IEEE Access
**2018**, 6, 63748–63756. [Google Scholar] [CrossRef] - Zhao, Y.; Yu, Z.; Wan, Z.; Hu, S.; Shu, L.; Zhang, J.; Xu, K. Low Complexity OSNR Monitoring and Modulation Format Identification Based on Binarized Neural Networks. J. Light. Technol.
**2020**, 38, 1314–1322. [Google Scholar] [CrossRef] - Lv, H.; Zhou, X.; Huo, J.; Yuan, J. Joint OSNR monitoring and modulation format identification on signal amplitude histograms using convolutional neural network. Opt. Fiber Technol.
**2021**, 61, 102455. [Google Scholar] [CrossRef] - Xie, Y.; Wang, Y.; Kandeepan, S.; Wang, K. Machine Learning Applications for Short Reach Optical Communication. Photonics
**2022**, 9, 30. [Google Scholar] [CrossRef]

**Figure 1.**Samples of the constellation and horizontal projection for all modulation formats at OSNR = 25 dB. (

**a**) QPSK, (

**b**) 8PSK, (

**c**) 16QAM, (

**d**) 32QAM, and (

**e**) 64QAM.

**Figure 4.**Simulation setup of the coherent optical communication system. PRBS, pseudo-random binary sequence; CW laser, continuous wave laser; Dual MZM, dual Mach–Zehnder modulator; OA, optical amplifier; LO, local oscillator.

**Figure 6.**The MFI accuracies at different epochs for different sample numbers. The sample numbers of each format are 550, 1100, and 2200.

**Figure 12.**Accuracies for MFF-Net trained on data from three different transmission distances (80 km, 160 km, and 240 km).

**Figure 13.**Accuracies for MFF-Net trained on data from two different bitrates (56 Gbit/s, 112 Gbit/s).

Model Type | MFF-Net | B-CNN | VGG-Net | EW-MTL | CNN | LSTM |
---|---|---|---|---|---|---|

OSNR Accuracy | 98.82% | 97.38% | 98.16% | 98.41% | 97.13% | 95.53% |

Total Parameters | 2.64 × 10^{5} | 1.63 × 10^{8} | 4.27 × 10^{6} | 4.76 × 10^{5} | 2.51 × 10^{6} | 4.80 × 10^{5} |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Li, J.; Ma, J.; Liu, J.; Lu, J.; Zeng, X.; Luo, M.
Modulation Format Identification and OSNR Monitoring Based on Multi-Feature Fusion Network. *Photonics* **2023**, *10*, 373.
https://doi.org/10.3390/photonics10040373

**AMA Style**

Li J, Ma J, Liu J, Lu J, Zeng X, Luo M.
Modulation Format Identification and OSNR Monitoring Based on Multi-Feature Fusion Network. *Photonics*. 2023; 10(4):373.
https://doi.org/10.3390/photonics10040373

**Chicago/Turabian Style**

Li, Jingjing, Jie Ma, Jianfei Liu, Jia Lu, Xiangye Zeng, and Mingming Luo.
2023. "Modulation Format Identification and OSNR Monitoring Based on Multi-Feature Fusion Network" *Photonics* 10, no. 4: 373.
https://doi.org/10.3390/photonics10040373