Interferometer based in-band OSNR monitoring of single and dual polarisation QPSK signals

Flood, E.; Guo, Weihua; Reid, Douglas A.; Lynch, M.; Bradley, A. Louise; Barry, Liam P.; Donegan, John F.

doi:10.1109/ecoc.2010.5621549

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Despite the possibility of directly measuring the OSNR without calibration, there are drawbacks for both proposed methods. For the case in [41], even when increasing the system performance to get a better approximation of the signal coherence level, signals with high bit rates and high OSNR values yield very inaccurate OSNR estimations. Furthermore, the number of filters traversed along the optical path influences the OSNR estimation, since cascaded filters narrow a signal's bandwidth, which changes its coherent properties.…”

Section: Osnr Monitorsmentioning

confidence: 97%

See 1 more Smart Citation

Toward Terabit Autonomic Optical Networks Based on a Software Defined Adaptive/Cognitive Approach [Invited]

Oliveira¹,

Oliveira²,

Magalhães³

et al. 2015

J. Opt. Commun. Netw.

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Terabit elastic optical networking (EON) is foreseen as a viable solution to extend the lifetime of a network exploiting the available bandwidth in previously deployed optical fibers. EON is based on bandwidth-variable transponders capable of supporting multiple bit rates and/ or modulation formats according to traffic requirements and node architectures that route arbitrary channel bandwidths. Thus, EON increases the heterogeneity of the network, which may create the need for autonomic adaptive and/or cognitive techniques. In this context, the software-defined networking (SDN) paradigm emerges as an opportunity to enable such techniques thanks to the centralized view of the network by decoupling the control plane and the data plane. This paper surveys different activities carried out at the Optical Technologies Division in Centro de Pesquisa e Desenvolvimento em Telecomunicações, Brazil. We review an optical transport SDN controller for virtual optical networks that supports two adaptive algorithms. First, the autonomic flexible transponder reconfigures the transmission modulation format according to a threshold level. Second, the adaptive global spectrum equalization reconfigures the wavelengths' attenuation profiles applied at the optical nodes to improve the signals' optical signal-to-noise ratio (OSNR) at reception. Finally, we report experimental results of an in-band OSNR monitor for advanced modulation formats.

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Section: Osnr Monitorsmentioning

confidence: 97%

“…In order to make the interferometric technique independent of calibration processes, [41] proposes a method to measure the OSNR directly, i.e., without calibration. This approach is possible because a set of different interferometers are used to obtain an approximation of the input signal coherence level.…”

Section: Osnr Monitorsmentioning

confidence: 99%

Toward Terabit Autonomic Optical Networks Based on a Software Defined Adaptive/Cognitive Approach [Invited]

Oliveira¹,

Oliveira²,

Magalhães³

et al. 2015

J. Opt. Commun. Netw.

View full text Add to dashboard Cite

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Non-intrusive measurement of In-Band OSNR of high bitrate polarization-multiplexed signals

Gariepy

Schinn

2011

Optical Fiber Technology

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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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Interferometer based in-band OSNR monitoring of single and dual polarisation QPSK signals

Abstract: The in-band OSNR of SP and DP QPSK signals were measured over a range of 10 to 21dB ±1dB using a pair of polarisation independent Michelson fibre interferometers without the requirement for prior knowledge of the signal's coherence properties.

Cited by 5 publications

References 10 publications

Toward Terabit Autonomic Optical Networks Based on a Software Defined Adaptive/Cognitive Approach [Invited]

Toward Terabit Autonomic Optical Networks Based on a Software Defined Adaptive/Cognitive Approach [Invited]

Non-intrusive measurement of In-Band OSNR of high bitrate polarization-multiplexed signals

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

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