Simultaneous baud rate/modulation format identification and multi-parameter optical performance monitoring using multi-task learning with enhanced picture of Radon transform

Zhou, Tanglei; Xu, Hengying; Chen, Bai; Yang, Lishan; Sun, Weibin; Yu, Xinkuo; Ge, Peiyun; Li, Baokun; Zhao, Ruqing; Tang, Xue Qin; Wang, Zhiguo; Qin, Peng

doi:10.1016/j.yofte.2022.102873

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…Neural network technology utilizes deep neural networks with multiple nonlinear layers to learn and extract feature information from signals, without relying on manual experience to extract signal features. MFI methods based on neural network technology have the advantages of requiring less prior knowledge, strong feature extraction capabilities, and high recognition accuracy, which have received widespread attention and research from scientific researchers [17][18][19][20] .…”

Section: Telecommunications Operators In Various Countriesmentioning

confidence: 99%

Advanced modulation formats for 4 × 100Gbps computing power optical networks and AI-based format recognition

He,

Huang,

et al. 2024

Preprint

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In the era of the digital economy, enabling users to utilize computing power as conveniently as water and electricity is inseparable from the development of computing power networks. The carrier network of future 6G also needs to achieve deep collaboration between computing and networking. In high-speed computing power optical networks above 400G, nonlinear effects became one of the main factors limiting system transmission performance. Traditional coherent optical communication solutions are complex to implement, require high coherence and stability of light sources, which is difficult, and costly. This paper proposes a low-cost and low-complexity non-coherent solution based on the advanced modulation format Apol-CRZ-FSK to achieve 4 x 100Gbps computing power optical networks. Simulation results show that it exhibits better resistance to non-linear effects compared to traditional modulation formats such as CRZ-FSK and DQPSK, enabling longer single-span and multi-span transmission distances and superior transmission performance. Furthermore, in response to the transmission requirements and signal perception and recognition requirements in future computing optical networks, this study identifies three different modulation formats through the Inception-ResNet-v2 convolutional neural network model. When compared with six deep learning methods including AlexNet, ResNet50, GoogleNet, SqueezeNet, Inception-v4, and Xception, the Inception-ResNet-v2 model achieved the highest accuracy rate of 99.51%, which is a 1.66% improvement over the ResNet50 model. It can provide an effective solution for low-cost, low-complexity, and high-performance signal transmission and signal recognition in the 6G era of high-speed computing power optical networks.

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Section: Telecommunications Operators In Various Countriesmentioning

confidence: 99%

Advanced modulation formats for 4 × 100Gbps computing power optical networks and AI-based format recognition

He,

Huang,

et al. 2024

Preprint

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show abstract

“…5 The MF information can be tracked through the modulation format identification (MFI) modules, and the corresponding DSP algorithm can be reconfigured to accurately recover the signal. 6 In long-haul transmission, the loss of optical power in the fiber and optical nodes can be compensated by an erbium-doped fiber amplifier (EDFA). 7 However, with the gain of the signal, amplifier spontaneous emission noise can be introduced by each EDFA, which accumulates in the transmission link and results in a decline in the optical signal-to-noise ratio (OSNR).…”

Section: Introductionmentioning

confidence: 99%

“…The other is MF-dependent algorithms (the main reason is that different MFs contain different amplitude and phase features), such as the multimodulus algorithm (MMA), frequency offset compensation, carrier phase recovery, and decoder 5 . The MF information can be tracked through the modulation format identification (MFI) modules, and the corresponding DSP algorithm can be reconfigured to accurately recover the signal 6 …”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Chen

et al. 2022

Opt. Eng.

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.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.

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Combined image Hough transform based simultaneous multi-parameter optical performance monitoring for intelligent optical networks

Kulandaivel,

Jeyachitra

2023

Optical Fiber Technology

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Simultaneous baud rate/modulation format identification and multi-parameter optical performance monitoring using multi-task learning with enhanced picture of Radon transform

Cited by 7 publications

References 21 publications

Advanced modulation formats for 4 × 100Gbps computing power optical networks and AI-based format recognition

Advanced modulation formats for 4 × 100Gbps computing power optical networks and AI-based format recognition

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

Combined image Hough transform based simultaneous multi-parameter optical performance monitoring for intelligent optical networks

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