scholarly journals Joint Fiber Nonlinear Noise Estimation, OSNR Estimation and Modulation Format Identification Based on Asynchronous Complex Histograms and Deep Learning for Digital Coherent Receivers

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 380
Author(s):  
Shuailong Yang ◽  
Liu Yang ◽  
Fengguang Luo ◽  
Bin Li ◽  
Xiaobo Wang ◽  
...  
Keyword(s):  
Performance Monitoring ◽  
Signal To Noise Ratio ◽  
Optical Network ◽  
Power Estimation ◽  
Optical Signal ◽  
Training Data ◽  
Noise Power ◽  
Optical Performance Monitoring ◽  
Modulation Format ◽  
Coherent Receivers

In this paper, asynchronous complex histogram (ACH)-based multi-task artificial neural networks (MT-ANNs), are proposed to realize modulation format identification (MFI), optical signal-to-noise ratio (OSNR) estimation and fiber nonlinear (NL) noise power estimation simultaneously for coherent optical communication. Optical performance monitoring (OPM) is demonstrated with polarization mode multiplexing (PDM), 16 quadrature amplitude modulation (QAM), PDM-32QAM, as well as PDM-star 16QAM (S-16QAM) for the first time. The range of launched power is −3 to −2 dBm with a fiber link of 160–1600 km. Then, the accuracy of MFI reaches 100%. The average root mean square error (RMSE) of OSNR estimation can reach 0.37 dB. The average RMSE of NL noise power estimation can reach 0.25 dB. The results show that the monitoring scheme is robust to the increase of fiber length, and the solution can monitor more optical network parameters with better performance and fewer training data, simultaneously. The proposed ACH MT-ANN has certain reference significance for the future long-haul coherent OPM system.

scholarly journals Going Deeper into OSNR Estimation with CNN

Photonics ◽  
2021 ◽  
Vol 8 (9) ◽  
pp. 402 ◽  
Author(s):  
Fangqi Shen ◽  
Jing Zhou ◽  
Zhiping Huang ◽  
Longqing Li
Keyword(s):  
Performance Monitoring ◽  
Mean Squared Error ◽  
Finite Impulse Response ◽  
Chromatic Dispersion ◽  
Optical Network ◽  
Estimation Method ◽  
Absolute Error ◽  
Optical Signal ◽  
Optical Performance Monitoring ◽  
Modulation Format

As optical performance monitoring (OPM) requires accurate and robust solutions to tackle the increasing dynamic and complicated optical network architectures, we experimentally demonstrate an end-to-end optical signal-to-noise (OSNR) estimation method based on the convolutional neural network (CNN), named OptInception. The design principles of the proposed scheme are specified. The idea behind the combination of the Inception module and finite impulse response (FIR) filter is elaborated as well. We experimentally evaluate the mean absolute error (MAE) and root-mean-squared error (RMSE) of the OSNR monitored in PDM-QPSK and PDM-16QAM signals under various symbol rates. The results suggest that the MAE reaches as low as 0.125 dB and RMSE is 0.246 dB in general. OptInception is also proved to be insensitive to the symbol rate, modulation format, and chromatic dispersion. The investigation of kernels in CNN indicates that the proposed scheme helps convolutional layers learn much more than a lowpass filter or bandpass filter. Finally, a comparison in performance and complexity presents the advantages of OptInception.

Artificial intelligence based optical performance monitoring

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Palash Rai ◽  
Rahul Kaushik
Keyword(s):  
Communication Networks ◽  
Performance Monitoring ◽  
Signal To Noise Ratio ◽  
Chromatic Dispersion ◽  
Optical Signal ◽  
Optical Performance ◽  
Optical Performance Monitoring ◽  
Eye Diagram ◽  
Artificial Neural Network Ann ◽  
Data Signal

Abstract In this paper, a technique for optical performance monitoring (OPM) using deep learning-based artificial neural network (ANN) is implemented. ANN is trained with parameters derived from eye-diagram for the identification of optical signal to noise ratio (OSNR), chromatic dispersion (CD) and polarisation mode dispersion (PMD) simultaneously and independently in a 10 Gb/s system with non-return-to-zero (NRZ) on-off keying (OOK) data signal. ANN-based OPM confirms that the proposed approach can provide reliable estimated results. The mean squared errors for OSNR, CD and differential group delay (DGD) are found to be 4.6071 dB, 0.0417 ps/nm/km and 0.0016 ps/km, respectively. The proposed technique may be utilized in analyzing the signals of future heterogeneous optical communication networks intelligently.

scholarly journals Optimization of Q-Values for Coherent Optical Transmission Network

2015 ◽  
Vol 43 ◽  
pp. 63-72
Author(s):  
Abdul Gafur ◽  
M. S. Islam
Keyword(s):  
Optical Transmission ◽  
Signal To Noise Ratio ◽  
Optical Signal ◽  
Noise Power ◽  
Transmission Network ◽  
Signal To Noise ◽  
Baud Rate ◽  
Optical Link ◽  
Modulation Format ◽  
Q Values

In this paper we have analyzed the Gaussian Non-linear Interference (GNLI) spectrum considering non identical channels, non-identical links and Amplified Spontaneous Emission (ASE) noise power spectrum for Coherent Optical Transmission Network (COTN) to calculate the Optical Signal to Noise Ratio (OSNR) and Quality (Q) values. In this study, three different Baud rate values (13.875Gbaud, 27.75Gbaud, and 55.5Gbaud) are considered to compute the Q values and OSNR in the COTN. Consequently, the COTN produces 111 Gb/s, 222 Gb/s and 444 Gb/s line rates for three different Baud rate values (13.875Gbaud, 27.75Gbaud, and 55.5Gbaud) respectively in PM-16QAM modulation format. It is confirmed that the OSNR is always greater than Q values. It is also found that the differences between OSNR and Q are 0.23dB, 1.73dB, and 3.24dB for 111Gb/s, 222Gb/s and 444Gb/s line rates respectively. Here transmission of launch power per span, number of channels, number of spans and the fiber dispersion in the optical link are considered.

40 Gb/s High-speed mode-division multiplexing transmission employing NRZ modulation format

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Rabiu Imam Sabitu ◽  
Nafizah Goriman Khan ◽  
Amin Malekmohammadi
Keyword(s):  
System Performance ◽  
High Speed ◽  
Direct Detection ◽  
Signal To Noise Ratio ◽  
Optical Signal ◽  
Threshold Power ◽  
Modulation Format ◽  
Transmission Distance ◽  
Mode Division Multiplexing ◽  
Speed Mode

AbstractThis report examines the performance of a high-speed MDM transmission system supporting four nondegenerate spatial modes at 10 Gb/s. The analysis adopts the NRZ modulation format to evaluate the system performance in terms of a minimum power required (PN) and the nonlinear threshold power (PTH) at a BER of 10−9. The receiver sensitivity, optical signal-to-noise ratio, and the maximum transmission distance were investigated using the direct detection by employing a multimode erbium-doped amplifier (MM-EDFA). It was found that by properly optimizing the MM-EDFA, the system performance can significantly be improved.

scholarly journals AI-Based Modeling and Monitoring Techniques for Future Intelligent Elastic Optical Networks

2020 ◽  
Vol 10 (1) ◽  
pp. 363 ◽  
Author(s):  
Xiaomin Liu ◽  
Huazhi Lun ◽  
Mengfan Fu ◽  
Yunyun Fan ◽  
Lilin Yi ◽  
...  
Keyword(s):  
Optical Networks ◽  
Performance Monitoring ◽  
Recent Progress ◽  
Optical Network ◽  
Data Driven ◽  
High Definition ◽  
Optical Performance Monitoring ◽  
Monitoring Tools ◽  
Quality Of Transmission

With the development of 5G technology, high definition video and internet of things, the capacity demand for optical networks has been increasing dramatically. To fulfill the capacity demand, low-margin optical network is attracting attentions. Therefore, planning tools with higher accuracy are needed and accurate models for quality of transmission (QoT) and impairments are the key elements to achieve this. Moreover, since the margin is low, maintaining the reliability of the optical network is also essential and optical performance monitoring (OPM) is desired. With OPM, controllers can adapt the configuration of the physical layer and detect anomalies. However, considering the heterogeneity of the modern optical network, it is difficult to build such accurate modeling and monitoring tools using traditional analytical methods. Fortunately, data-driven artificial intelligence (AI) provides a promising path. In this paper, we firstly discuss the requirements for adopting AI approaches in optical networks. Then, we review various recent progress of AI-based QoT/impairments modeling and monitoring schemes. We categorize these proposed methods by their functions and summarize advantages and challenges of adopting AI methods for these tasks. We discuss the problems remained for deploying AI-based methods to a practical system and present some possible directions for future investigation.

scholarly journals Optical performance monitoring using digital coherent receivers and convolutional neural networks

2020 ◽  
Vol 28 (21) ◽  
pp. 32087
Author(s):  
Hyung Joon Cho ◽  
Siddharth Varughese ◽  
Daniel Lippiatt ◽  
Richard Desalvo ◽  
Sorin Tibuleac ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Performance Monitoring ◽  
Optical Performance ◽  
Optical Performance Monitoring ◽  
Coherent Receivers

Applying Smart Security Model to Protect Client Services From the Threats of the Optical Network

2021 ◽  
pp. 80-113
Author(s):  
Kamel H. Rahouma ◽  
Ayman A. Ali
Keyword(s):  
Signal To Noise Ratio ◽  
Optical Network ◽  
Optical Signal ◽  
Software Defined Network ◽  
Security Model ◽  
Signal To Noise ◽  
New Model ◽  
Intelligent Software ◽  
Optical Transport Network ◽  
The Right

The chapter discusses the security of the client signals over the optical network from any wiretapping or loosing. The physical layer of the optical transport network (OTN) is the weakest layer in the network; anyone can access the optical cables from any location and states his attack. A security layer is proposed to be added in the mapping of OTN frames. The detection of any intrusion is done by monitoring the variations in the optical signal to noise ratio (OSNR) by using intelligent software defined network. The signal cryptographic is done at the source and the destination only. The chapter shows how the multi-failure restorations in the multi-domains could be done. A new model is introduced by slicing the multi-domains to three layers to fit the needs of 5G. The results show that the multi-failure restoration improved from 25% to 100%, the revenue from some OTN domains increased by 50%, the switching time enhanced by 50%, the latency reduced from 27 msec to 742 usec, and it will take many years to figure out the right keys to perform the decryption process.

scholarly journals Coherent Power Measurements with a Compact Airborne Ka-Band Precipitation Radar

2018 ◽  
Vol 35 (1) ◽  
pp. 3-20 ◽  
Author(s):  
Andrew L. Pazmany ◽  
Samuel J. Haimov
Keyword(s):  
Signal To Noise Ratio ◽  
Power Estimation ◽  
Noise Power ◽  
Signal Power ◽  
Signal To Noise ◽  
Precipitation Radar ◽  
Noise Component ◽  
Ka Band ◽  
Noise Ratio ◽  
Conventional Signal

AbstractCoherent power is an alternative to the conventional noise-subtracted power technique for measuring weather radar signal power. The inherent noise-canceling feature of coherent power eliminates the need for estimating and subtracting the noise component, which is required when performing conventional signal power estimation at low signal-to-noise ratio. The coherent power technique is particularly useful when averaging a high number of samples to improve sensitivity to weak signals. In such cases, the signal power is small compared to the noise power and the required accuracy of the estimated noise power may be difficult to achieve. This paper compares conventional signal power estimation with the coherent power measurement technique by investigating bias, standard deviation, and probability of false alarm and detection rates as a function of signal-to-noise ratio and threshold level. This comparison is performed using analytical expressions, numerical simulations, and analysis of cloud and precipitation data collected with the airborne solid-state Ka-band precipitation radar (KPR) operated by the University of Wyoming.

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