A Modulation Format Identification Method Based on Information Entropy Analysis of Received Optical Communication Signal

Zhao, Zhao; Yang, Aiying; Guo, Peng

doi:10.1109/access.2019.2907521

Cited by 25 publications

(14 citation statements)

References 25 publications

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“…However, they are sensitive to decision thresholds. In [134], the entropy of the amplitude histogram was used for MFI. The entropy of amplitude histogram with a different number of bins (i.e.…”

Section: A Conventional Opm and Mfi Techniquesmentioning

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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Section: A Conventional Opm and Mfi Techniquesmentioning

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

View full text Add to dashboard Cite

show abstract

“…(1) In this paper, the following high-order cumulants are selected through [17]- [18], which have been proved to have good discrimination performance in modulation classification: intends to extract power spectrum entropy, singular entropy and energy spectrum entropy of signal as features [19] [20]. Assuming that the length of time sequence X is L, the discrete Fourier transform of sequence X is:…”

Section: ) Handcrafted Featuresmentioning

confidence: 99%

Few-Shot Modulation Classification Method Based on Feature Dimension Reduction and Pseudo-Label Training

Shi

Jiang

et al. 2020

IEEE Access

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In modulation classification domain, handcrafted feature based method can fit well from a few labeled samples, while deep learning based method require a large amount of samples to achieve the superior classification performance. In order to improve the modulation classification accuracy under the constraint of limited labeled samples, this paper proposes a few-shot modulation classification method based on feature dimension reduction and pseudo-label training (FDRPLT), which combines handcrafted feature based method with deep learning based method. First, an optimal low-dimensional feature subset is created by the combination of the handcrafted features and autoencoder-extracted features post-processed by a feature selection algorithm. Then, a fully connected network (FCN), trained on a small number of labeled signals, is designed for the automated annotating, where unlabeled samples can be annotated and used for the later convolution neural network (CNN) training. The simulation results show that the classification accuracy of eight kinds of modulation types can reach to 98.3% when the SNR is 20dB.

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“…Alternatively, analytical methods can also be employed to blindly identify the transmitted modulation format. Among them, some works had proposed to employ the received signal entropy [9] or peak-toaverage power ratio (PAPR) [10] to extract data about the transmitted optical constellation.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Techniques Evaluation for Modulation Format Identification in Optical Networks

Souza

Sutili

Júnior

et al. 2021

J. Microw. Optoelectron. Electromagn. Appl.

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A Modulation Format Identification Method Based on Information Entropy Analysis of Received Optical Communication Signal

Cited by 25 publications

References 25 publications

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

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

Few-Shot Modulation Classification Method Based on Feature Dimension Reduction and Pseudo-Label Training

Artificial Intelligence Techniques Evaluation for Modulation Format Identification in Optical Networks

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