Non-intrusive OSNR measurement of polarization-multiplexed signals with spectral shaping and subject to fiber non-linearity with minimum channel spacing of 375GHz

Gariepy, Daniel; Searcy, Steven; He, Gang; Tibuleac, Sorin

doi:10.1364/oe.24.020156

Cited by 21 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where B indicates the reference bandwidth, and the OSNR can be determined through the OSA-based techniques [26], [27] or approximated as [28, Eq. (4-21)]…”

Section: B Reference Value Calculation Of Snr Componentsmentioning

confidence: 99%

See 1 more Smart Citation

Parallel Neural Network Structures for Signal-to-Noise Ratio Estimation in Optical Fiber Communication Systems

Al-Nahhal,

Dobre

et al. 2024

J. Lightwave Technol.

View full text Add to dashboard Cite

This paper proposes two novel neural network (NN) structures to estimate long-term steady linear and nonlinear signal-to-noise ratio (SNR) components in optical fiber communication systems. The first proposed structure is a parallel NNbased (ParNN) estimator, which estimates each SNR component using a different NN structure and input feature set. A combination of gated recurrent unit and dense layers is used to estimate the linear SNR component. On the other hand, the nonlinear SNR component is estimated using a combination of convolutional layer with dense layer. The proposed input features of the ParNN estimator are generated solely from the received signal without knowledge of the transmitted signal. These features are formed of the lower quartile, upper quartile, and entropy, which can accurately characterize the behavior of the SNR components by measuring the received signal spread and uncertainty. For further improvement of the ParNN estimator, an additional stage is added to form the proposed enhanced ParNN (EParNN) estimator. This additional stage consists of two feedforward NNs (FFNNs), each with a single dense layer, where the first FFNN is used to estimate the linear SNR component and the second one estimates the nonlinear SNR component. The input of this additional stage is a combination of the input features and output of the ParNN estimator. The computational complexity is derived for the proposed estimators. The training and testing dataset is built from 16-ary quadrature amplitude modulation of a dualpolarization on a wide range of standard single-mode fiber system configurations, e.g., number of wavelength division multiplexing channels, optical launch power, and number of spans. Numerical results demonstrate that the proposed ParNN estimator achieves better SNR estimation accuracy with comparable computational complexity compared to the most efficient work in the literature. The proposed ParNN estimator can independently estimate each SNR component, in which the complexity per SNR component is reduced. Moreover, the proposed EParNN estimator improves SNR estimation accuracy compared to the ParNN estimator at the expense of a slight increase in the computational complexity.

show abstract

“…where B indicates the reference bandwidth, and the OSNR can be determined through the OSA-based techniques [26], [27] or approximated as [28, Eq. (4-21)]…”

Section: B Reference Value Calculation Of Snr Componentsmentioning

confidence: 99%

“…Here, N i , N h1 , and N o are the number of neurons in the input, hidden recurrent, and output layers, respectively. According to (26), the cost of the RNN structure in [21] is 1053 real multiplications and 1092 real additions.…”

Section: A Complexity Of Literature Estimatorsmentioning

confidence: 99%

Parallel Neural Network Structures for Signal-to-Noise Ratio Estimation in Optical Fiber Communication Systems

Al-Nahhal,

Dobre

et al. 2024

J. Lightwave Technol.

View full text Add to dashboard Cite

show abstract

“…Furthermore, the MT-ANN is efficient only when the tasks share the same features and are of the same type. The OS recognition prefers to use the OS as the feature, while the distortion monitoring prefers to use the OS variations as the feature [1], [2]. In addition, the OS identification tends to be a discrete classification problem, while the distortion estimation tends to be a regression problem.…”

Section: Introductionmentioning

confidence: 99%

Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks

Wang

Cui

et al. 2022

IEEE Photonics J.

View full text Add to dashboard Cite

In the optical communication systems, the optical spectrum (OS) provides useful informations for optical performance monitoring and optical link diagnosis. In this paper, we investigate the neural-network based OS analysis (OSA) techniques able to simultaneously recognize the OS and estimate the optical signal to noise ratio (OSNR), cascaded filtering distortion (CFD) and carrier wavelength drift (CWD). We demonstrate that, compared with the multi-task artificial neural network (MT-ANN) and convolutional neural network (MT-CNN), the proposed multi-task cascaded ANNs (CANN) and cascaded CNNs (CCNN) can greatly improve the OSA performance and accelerate the training process by exploiting specific features and loss functions for different tasks. The CCNN able to deal with the high-resolution OS (HOS) can further improve the OSA performance compared with the CANN. The averaged OS recognition accuracy and OSNR, CFD and CWD estimation errors obtained with the CCNN (CANN) for the 15 kinds of optical signals are 99.32% (97.07%), 0.36 (0.55) dB, 0.24 (0.32) and 0.04 (0.06) GHz, respectively, even when the various OS distortions are present. The proposed CANN and CCNN with a better versatility, higher OSA accuracy and faster convergence speed are promising enabling techniques for the future intelligent OSA systems.

show abstract

“…The noise-loading module composed of an erbium-doped fiber amplifier (EDFA), a 100 GHz rectangular-shaped filter implemented with a wavelength-selective switch (WSS), a booster EDFA, and a variable optical attenuator (VOA), is used to load an appropriate amount of noise power to the pump signal for performance investigation. The in-band OSNR of the pump is calculated using the "signal on/off " method [17]. The polarization of the noise-loaded pump signal is adjusted by PC 1 before it is amplified and filtered by cascaded high-pass and 1 nm bandpass optical filters with a bandwidth of ∼100 GHz.…”

mentioning

confidence: 99%

All-optical digital-to-analog converter based on cross-phase modulation with temporal integration

et al. 2017

View full text Add to dashboard Cite

We propose and experimentally demonstrate an all-optical digital-to-analog converter based on cross-phase modulation with temporal integration. The scheme is robust for driving signal noise due to the low-pass filtering feature of the temporal integrator. The proof-of-concept experiment demonstrates the generation of pulse-amplitude modulation (PAM) sequences up to eight levels. The performance of random PAM 2 and PAM 4 signals with different optical signal-to-noise ratios of the binary driving signal is also investigated. The scheme is scalable for high-speed operation with an appropriate dispersion profile of the nonlinear medium.

show abstract

Non-intrusive OSNR measurement of polarization-multiplexed signals with spectral shaping and subject to fiber non-linearity with minimum channel spacing of 375GHz

Cited by 21 publications

References 5 publications

Parallel Neural Network Structures for Signal-to-Noise Ratio Estimation in Optical Fiber Communication Systems

Parallel Neural Network Structures for Signal-to-Noise Ratio Estimation in Optical Fiber Communication Systems

Multi-Functional Optical Spectrum Analysis Using Multi-Task Cascaded Neural Networks

All-optical digital-to-analog converter based on cross-phase modulation with temporal integration

Contact Info

Product

Resources

About