Optical signal quality monitor built into WDM linear repeaters using semiconductor arrayed waveguide grating filter monolithically integrated with eight photodiodes

Suzuki, H.; Takachio, N.

doi:10.1049/el:19990573

Cited by 44 publications

(9 citation statements)

References 2 publications

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“…The cross validation is performed by testing independent data at different values of k and select the value that provides the best accuracy. The k-NN algorithm is easy to implement and well suited for large datasets and highly nonlinear mapping functions, however, storage requirements and retrieval time can limit its applications in real-time systems [43].…”

Section: A Supervised Learningmentioning

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 Supervised Learningmentioning

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 necessity of a means by which to measure the OSNR has already been set forth in [1]. Previously, the in-band OSNR was determined by using an optical spectrum analyser, measuring the noise level between two channels and performing a linear interpolation in order to obtain a measure of the in-band OSNR [2]. However, the decreasing of inter-channel spacing makes this out-of-band measurement increasingly difficult.…”

Section: Introductionmentioning

confidence: 99%

Dual Polarization Interferometric In-Band OSNR Measurement

Flood

Guo

Smyth

et al. 2012

IEEE Photon. Technol. Lett.

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An in-band optical signal-to-noise ratio (OSNR) monitoring technique based on a Michelson fiber interferometer and a linear polarizer is presented in this letter. OSNR values of up to 25 ± 0.5 dB were measured for 10-G nonreturn-tozero-differential phase-OOK (NRZ-OOK) and NRZ-differential phase-shift keyed (NRZ-DPSK) signals without prior knowledge of the signal coherence properties. Measurements were also carried out with signals having 680 ps/nm of chromatic dispersion.

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“…ASE noise is typically quantified by the optical-signal-to-noise-ratio (OSNR), which is measured within an optical bandwidth of 0.1 nm. Due to its influence on the bit-error-rate of an optical signal, its role in fault diagnosis and as a measure of general network health, it is important to have a reliable means of OSNR monitoring over a large range of signal and noise [1] Current methods involve measuring the noise at some offset from the centre frequency of a channel and performing a linear interpolation in order to make an estimate of the in-band OSNR [2]. This technique becomes unreliable with the use of reconfigurable optical add-drop multiplexers (ROADM).…”

Section: Introductionmentioning

confidence: 99%

In-band OSNR monitoring using a pair of Michelson fiber interferometers

et al. 2010

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Two polarization-independent Michelson fiber interferometers with different optical delays were used to measure the in-band OSNR of an optical signal from 5 to 30dB within an accuracy of 0.5dB. Using an expansion of the amplitude autocorrelation function of the signal around zero delay, it was possible to perform measurements without any prior knowledge of the signal. The system is shown to be immune to the effects of modulation frequency (up to 10G), partially and fully polarized noise, chromatic dispersion and poorly biased modulators.

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Optical signal quality monitor built into WDM linear repeaters using semiconductor arrayed waveguide grating filter monolithically integrated with eight photodiodes

Cited by 44 publications

References 2 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

Dual Polarization Interferometric In-Band OSNR Measurement

In-band OSNR monitoring using a pair of Michelson fiber interferometers

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