Intelligent Optical Performance Monitoring Based on Intensity and Differential-Phase Features for Digital Coherent Receivers

Feng, Jiacheng; Jiang, Lin; Yan, Lianshan; Yi, Anlin; Pan, Wei; Luo, Bin

doi:10.1109/jlt.2022.3149412

Cited by 13 publications

(9 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For convenience in data processing and to accelerate convergence, we normalize the amplitude by mean normalization. Equation (1) shows the principle of mean normalization, where yðnÞ is the data after the CMA module, and y 0 ðnÞ is the mean normalized signal:…”

Section: Mfi Schemementioning

confidence: 99%

“…With the rise of high-speed and high-bandwidth services, such as cloud services, augmented reality, virtual reality, and video streaming, elastic optical networks have emerged as one of the candidates for next-generation ultra-high-speed fiber communications. 1,2 Flexible transmitters are deployed in elastic optical networks, which can dynamically adjust the modulation format (MF) to improve transmission and scheduling efficiency. In long-haul transmission, optical signals suffer from various impairments, including chromatic dispersion (CD), nonlinear effect, frequency offset, self-phase modulation effect, polarization-dependent loss effect, polarizationmode dispersion (PMD), and laser phase noise.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cascaded modulation format identification and optical signal-to-noise ratio estimation method for optical communication system

Zhou

Chen

et al. 2022

Opt. Eng.

View full text Add to dashboard Cite

.Modulation format identification (MFI) and optical performance monitoring are important for elastic optical networks. We propose a cascaded module-based MFI and optical signal-to-noise ratio (OSNR) estimation method. Polar coordinates were used as features to distinguish the modulation formats (MFs) and estimate the OSNR. Dilated convolution was adopted to increase the receptive field and reduce computational complexity. Four commonly used MFs were investigated: dual-polarization (DP) quadrature phase shift keying, 8QAM, 16QAM, and 32QAM. A 32G baud DP transmission system is established, and the results are discussed. The results show that MFI accuracy can reach 100% when the OSNR is less than the 7% forward error correction limit, and the accuracy of OSNR monitoring is close to 100%. Two factors, sample length and nonlinearity, were further investigated. The results show that the MFI and OSNR estimators can achieve perfect performance when the sample length is 3000. Simultaneously, the accuracy of the MFI remained at 100%, and the accuracy of the OSNR estimator decreased by 1% to 2% when the launch power changed from 0 to 5 dBm. Furthermore, two modules with the same structure, DC-CNN and CNN, were compared. The results show that the two models can achieve similar accuracies and that the DC-CNN has the least number of parameters. Finally, experimental verification was conducted to ensure the practical applicability of the proposed method.

show abstract

Section: Mfi Schemementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cascaded modulation format identification and optical signal-to-noise ratio estimation method for optical communication system

Zhou

Chen

et al. 2022

Opt. Eng.

View full text Add to dashboard Cite

show abstract

“…Thanks to the excellent feature learning and deduction capabilities of NN technology, it is now able to recognize the minute feature differences between various signals, enhancing the speed and accuracy of performance monitoring. Mainstream NN-based OPM technologies include convolutional neural networks (CNNs) [31][32][33][34] , artificial neural networks (ANNs) [35][36][37][38][39] , deep neural networks (DNNs) [40][41][42][43][44] , and long short-term memory (LSTM) [45,46] . Moreover, based on the structure of the NN, these techniques can be divided into multitask output and single-task cascade output.…”

Section: Introductionmentioning

confidence: 99%

Joint modulation format identification and OSNR monitoring based on Stokes vector distribution features for digital coherent optical receivers

Pu,

Jiang,

Yan

et al. 2024

中国光学快报

Self Cite

View full text Add to dashboard Cite

For joint modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring, a simple and intelligent optical communication performance monitoring method is proposed, and the feasibility is demonstrated by digital coherent optical communication experiments. The experiment results show that for all modulation formats, including 28 GBaud polarization division multiplexing (PDM) QPSK/8-QAM/16-QAM/64-QAM, 100% MFI accuracies are achieved even at OSNR values lower than the corresponding theoretical 20% forward error correction limit, as well as the high accuracies for OSNR monitoring. Furthermore, the proposed scheme has a reasonable monitoring level when chromatic dispersion and fiber nonlinear effects are varied.

show abstract

“…Sun et al introduced a fast and effective deep learning model called the squeeze and excitation WaveNet, which achieved a test accuracy rate of approximately 97.73% [25]. CNN automatically extracts features from large databases, leading to higher accuracy [26][27][28][29]. Chen et al developed a method for enlarging the useful dataset for CNNs using intensity and phase stacked CNN and data augmentation, resulting in a classification accuracy of 88.2% on the DAS dataset with a 1 km sensing length [30].…”

Section: Introductionmentioning

confidence: 99%

A recognition method of less databases by using decoupling parallel CNN based on multiple feature with DAS

Huang

Chai

Jia

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

The integration of artificial intelligence and distributed fiber optic acoustic sensing technology(DAS) has yielded remarkable results in recent years; however, some application scenarios face the challenge of acquiring an adequate amount of data for higher network accuracy. To address this issue, we propose a decoupling parallel convolutional neural network (DPCNN) that relies on multiple feature inputs to achieve higher accuracy while using smaller databases. Our model offers excellent recognition of five events, including background noise, footstep, digging, car passing, and climbing fence, with an accuracy rate of up to 94.9%. The DPCNN is a parallel and lightweight CNN that boasts a short training time of only 3.76s per epoch and a test time of 0.1175s, with superior network convergence. In comparison to a mature single-branch CNN based on mixed images of time-frequency and time-space, the DPCNN accuracy is 6.4% higher. Our model demonstrates excellent performance across various databases and can achieve recognition accuracy of up to 98.7% with larger databases. Finally, we show the broad range of applications available for DPCNN based on multiple feature inputs when using a mature single-branch replacement in each branch of a two-branch network.

show abstract

Intelligent Optical Performance Monitoring Based on Intensity and Differential-Phase Features for Digital Coherent Receivers

Cited by 13 publications

References 41 publications

Cascaded modulation format identification and optical signal-to-noise ratio estimation method for optical communication system

Cascaded modulation format identification and optical signal-to-noise ratio estimation method for optical communication system

Joint modulation format identification and OSNR monitoring based on Stokes vector distribution features for digital coherent optical receivers

A recognition method of less databases by using decoupling parallel CNN based on multiple feature with DAS

Contact Info

Product

Resources

About