Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks and genetic algorithms

Zhang, Shuting; Peng, Yingqing; Shu, Qi; Li, Jian Ping; Li, Zhaohui

doi:10.1007/s11107-016-0606-7

Cited by 12 publications

(10 citation statements)

References 18 publications

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“…In [66], [67], a NN is used for hierarchical extraction of the amplitude histograms' features, in order to obtain a compressed representation, aimed at reducing the number of neurons in the hidden layers with respect to the number of features. In [65], a NN is combined with a genetic algorithm to improve the efficiency of the weight selection procedure during the training phase. Both studies provide numerical results over experimentally generated data: the former obtains 0% error rate in discriminating among three modulation formats (PM-QPSK, 16-QAM and 64-QAM), the latter shows the tradeoff between error rate and number of histogram bins considering six different formats (NRZ-OOK, ODB, NRZ-DPSK, RZ-DQPSK, PM-RZ-QPSK and PM-NRZ-16-QAM).…”

Section: Modulation Format Recognitionmentioning

confidence: 99%

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

457

226

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Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management.The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation.In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions.

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Section: Modulation Format Recognitionmentioning

confidence: 99%

An Overview on Application of Machine Learning Techniques in Optical Networks

Musumeci

Rottondi

Nag

et al. 2019

IEEE Commun. Surv. Tutorials

457

226

View full text Add to dashboard Cite

show abstract

“…This modification increased the MFI accuracy by more than 99%. Similarly, AAH has been used in [160], [162] for MFI with ANN classifier optimized by genetic algorithm (GA). The results showed same MFI accuracy as obtained in [159], with few number of neurons and hidden layers.…”

Section: B Ml-based Techniques For Opm and Mfimentioning

confidence: 99%

Machine Learning Techniques for Optical Performance Monitoring and Modulation Format Identification: A Survey

Saif

Esmail

Ragheb

et al. 2020

IEEE Commun. Surv. Tutorials

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The trade-off between more user bandwidth and quality of service requirements introduces unprecedented challenges to the next generation smart optical networks. In this regard, the use of optical performance monitoring (OPM) and modulation format identification (MFI) techniques becomes a common need to enable the development of next-generation autonomous optical networks, with ultra-low latency and selfadaptability. Recently, machine learning (ML)-based techniques have emerged as a vital solution to many challenging aspects of OPM and MFI in terms of reliability, quality, and implementation efficiency. This paper surveys ML-based OPM and MFI techniques proposed in the literature. First, we address the key advantages of employing ML algorithms in optical networks. Then, we review the main optical impairments and modulation formats being monitored and classified, respectively, using ML algorithms. Additionally, we discuss the current status of optical networks in terms of MFI and OPM. This includes standards, monitoring parameters, and the available commercial products with their limitations. Second, we provide a comprehensive review of the available ML-based techniques for MFI, OPM, and joint MFI/OPM, describing their performance, advantages, and limitations. Third, we give an overview of the exiting ML-based OPM and MFI techniques for the emerging optical networks such as the new fiber-based networks that use future space division multiplexing techniques (e.g. few-mode fiber), the hybrid radioover-fiber networks, and the free space optical networks. Finally, we discuss the open issues, potential future research directions, and recommendations for the potential implementation of MLbased OPM and MFI techniques. Some lessons learned are presented after each section throughout the paper to help the reader identifying the gaps, weaknesses, and strengths in this field.

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“…In recent years, researchers have utilized various technologies to achieve MFI such as signal cumulant and signal power distribution-based method [7,8], peak-to-average-power ratio of received data samples-based methods [9,10], Stokes space-based methods [11][12][13][14][15], and machine learning-based methods [16][17][18][19]. Various OSNR estimation techniques for coherent detection systems have been proposed recently including statistical moments [20], error vector magnitude [21], delay-line interferometer [22,23], Stokes parameters [24], Golay sequences [25], offset filtering, and optical power measurement [26], radio frequency (RF) spectrum [27], and amplitude noise correlation [28] based methods.…”

Section: Introductionmentioning

confidence: 99%

A Density Clustering Algorithm for Simultaneous Modulation Format Identification and OSNR Estimation

et al. 2020

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In this work, we propose a combined method to implement both modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation, a method based on density-based spatial clustering of applications with a noise (DBSCAN) algorithm. The proposed method can automatically extract the cluster number and density information of samples. The cluster number can be used for MFI and the density information combined with a fourth-order polynomial fitting can correctly estimate OSNR. We verify the feasibility of the method through simulation and conceptual proof experiments. The results show that the MFI can achieve 100% accuracy when the OSNR values are higher than the 7% forward error correction (FEC) thresholds for five commonly used modulation formats (MFs) like polarization division multiplexing (PDM)-QPSK, PDM-8PSK, PDM-16QAM, PDM-32QAM, and PDM-64QAM. Mean absolute OSNR estimation errors are not higher than 1 dB for different signals. There is no additional hardware required, so the proposed method has the ability to be integrated into existing optical performance monitoring systems without burden. Furthermore, the proposed method has the potential to be used in bit-error ratio (BER) calculation, linear, or nonlinear impairments monitoring. We believe that our multifunctional and simple method would be favorable to a future elastic optical network (EON).

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Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks and genetic algorithms

Cited by 12 publications

References 18 publications

An Overview on Application of Machine Learning Techniques in Optical Networks

An Overview on Application of Machine Learning Techniques in Optical Networks

Machine Learning Techniques for Optical Performance Monitoring and Modulation Format Identification: A Survey

A Density Clustering Algorithm for Simultaneous Modulation Format Identification and OSNR Estimation

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