A Modulation Format Identification Method Based on Amplitude Deviation Analysis of Received Optical Communication Signal

Zhao, Zhao; Yang, Aiying; Guo, Peng; Tang, Wenyu

doi:10.1109/jphot.2019.2893658

Cited by 11 publications

(7 citation statements)

References 17 publications

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“…We have adopted the simulation system of a recently proposed MFI scheme [25]. We use the commercial software Virtual Photonics Inc. (VPI) to set up the simulation system.…”

Section: Simulation Resultsmentioning

confidence: 99%

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

2019

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Modulation format identification is an important part of optical performance monitoring in elastic optical networks. We propose a modulation format identification scheme based on the information entropy analysis of received optical communication signal. In our method, the information entropy is calculated based on the amplitude distribution of the received signal and employed to identify four commonly used modulation formats, including QPSK, 16QAM, 32QAM, and 64QAM. The results demonstrate that the modulation format can be identified with the required OSNR not higher than the FEC threshold. The influence of the residual chromatic dispersion on the proposed scheme is also discussed. It is insensitive to phase noise caused by laser linewidth and frequency offset, and can tolerate a range of fiber nonlinearity, which is also an advantage of our method. Compared to other blind modulation formats identification methods, the proposed method needs no additional hardware or training samples, is more simple and cost-effective.INDEX TERMS Optical fiber communication, optical modulation, optical signal processing.

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“…We have adopted the simulation system of a recently proposed MFI scheme [25]. We use the commercial software Virtual Photonics Inc. (VPI) to set up the simulation system.…”

Section: Simulation Resultsmentioning

confidence: 99%

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

2019

Self Cite

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“…In this paper, we propose a decentralized approach where the intelligence is distributed [15]; the Tx decides autonomously the SC configurations, i.e., the SC's MF and SR, and the Rx can detect and recognize such configurations. Previous works in the literature have proposed methods for the blind recognition of the MF based on the received constellation, see, e.g., [16], [17]. Nonetheless, such methods require high signal-to-noise ratio (SNR) for accurate recognition, e.g., SNR > 20 dB and/or they do not consider the SR configuration, which limits its applicability.…”

Section: Autonomous and Energy Efficient Lightpathmentioning

confidence: 99%

Autonomous and Energy Efficient Lightpath Operation Based on Digital Subcarrier Multiplexing

Velasco

Barzegar

Sequeira

et al. 2021

IEEE J. Select. Areas Commun.

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The massive deployment of 5G and beyond will require high capacity and low latency connectivity services, so network operators will have either to overprovision capacity in their transport networks or to upgrade the optical network controllers to make decisions nearly in real time; both solutions entail high capital and operational expenditures. A different approach could be to move the decision making toward the nodes and subsystems, so they can adapt dynamically the capacity to the actual needs and thus reduce operational costs in terms of energy consumption. To achieve this, several technological challenges need to be addressed. In this paper, we focus on the autonomous operation of Digital Subcarrier Multiplexing (DSCM) systems, which enable the transmission of multiple and independent subcarriers (SC). Herein, we present several solutions enabling the autonomous DSCM operation, including: i) SC quality of transmission estimation; ii) autonomous SC operation at the transmitter side and blind SC configuration recognition at the receiver side; and iii) intent-based capacity management implemented through Reinforcement Learning. We provide useful guidelines for the application of autonomous SC management supported by the extensive results presented.

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“…Features extracted from the signal's amplitude deviation were exploited in [132]. The ratio of amplitude deviation for two modulations (i.e.…”

Section: A Conventional Opm and Mfi Techniquesmentioning

confidence: 99%

“…Besides, HOC features and some other features extracted from the signal amplitude and phase that are acquired coherently have been exploited in [132], [133], [135]. In contrast to Stokes space constellation features, these features are insensitive to ASE noise.…”

Section: B Features Utilized 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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A Modulation Format Identification Method Based on Amplitude Deviation Analysis of Received Optical Communication Signal

Cited by 11 publications

References 17 publications

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

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

Autonomous and Energy Efficient Lightpath Operation Based on Digital Subcarrier Multiplexing

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

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