Automated Fiber Type Identification in SDN-Enabled Optical Networks

Seve, E.; Pesic, Jelena; Delezoide, Camille; Giorgetti, A.; Sgambelluri, Andrea; Sambo, N.; Bigo, S.; Pointurier, Yvan

doi:10.1109/jlt.2019.2896041

Cited by 16 publications

(5 citation statements)

References 24 publications

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“…It can be used in applications related to classic network security [32], quantum network security [31], or network quality of transmission (QoT). Regarding a network device census, as highlighted in [40], it is common that network operators may not be fully aware of all the deployed fiber types in a network. This implies problems in the quality of transmission estimation, which increases estimation inaccuracy forcing the operator to assume even higher network margins than expected, with a consequent underestimation of the optical reach and an increase in the costs of regeneration [41].…”

Section: Discussionmentioning

confidence: 99%

Optical Systems Identification through Rayleigh Backscattering

Goki

Mulugeta

Caldelli

et al. 2023

Sensors

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We introduce a technique to generate and read the digital signature of the networks, channels, and optical devices that possess the fiber-optic pigtails to enhance physical layer security (PLS). Attributing a signature to the networks or devices eases the identification and authentication of networks and systems thus reducing their vulnerability to physical and digital attacks. The signatures are generated using an optical physical unclonable function (OPUF). Considering that OPUFs are established as the most potent anti-counterfeiting tool, the created signatures are robust against malicious attacks such as tampering and cyber attacks. We investigate Rayleigh backscattering signal (RBS) as a strong OPUF to generate reliable signatures. Contrary to other OPUFs that must be fabricated, the RBS-based OPUF is an inherent feature of fibers and can be easily obtained using optical frequency domain reflectometry (OFDR). We evaluate the security of the generated signatures in terms of their robustness against prediction and cloning. We demonstrate the robustness of signatures against digital and physical attacks confirming the unpredictability and unclonability features of the generated signatures. We explore signature cyber security by considering the random structure of the produced signatures. To demonstrate signature reproducibility through repeated measurements, we simulate the signature of a system by adding a random Gaussian white noise to the signal. This model is proposed to address services including security, authentication, identification, and monitoring.

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Section: Discussionmentioning

confidence: 99%

Optical Systems Identification through Rayleigh Backscattering

Goki

Mulugeta

Caldelli

et al. 2023

Sensors

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“…Uncertainties in parameter values in this work (see Fig. 1b) are due to 1) unaccounted losses in optical connectors, e.g., coming from dust and dirt [16], 2) non-flatness of EDFA gain profile (i.e., gain ripple), and 3) wrong fiber type specifications, due to, e.g., inventory problems [17]. Uncertain knowledge of PL parameters results in two main shortcomings, i.e., powers launched into the span are set suboptimally, and analytical QoT estimation is inaccurate.…”

Section: Modelling Assumptionsmentioning

confidence: 99%

Probabilistic low-margin optical-network design with multiple physical-layer parameter uncertainties

Karandin¹,

Ferrari²,

Musumeci³

et al. 2023

J. Opt. Commun. Netw.

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Analytical models for quality of transmission (QoT) estimation require safety design margins to account for uncertain knowledge of input parameters. We propose and evaluate a design procedure that gradually decreases these margins in the presence of multiple physical-layer uncertainties (namely, connector loss, erbium-doped fiber amplifier gain ripple, and fiber type) by leveraging monitoring data to build a probabilistic machine-learning-based QoT regressor. We evaluate the savings from margin reduction in terms of occupied spectrum and number of installed transponders in the C and C+L bands and demonstrate that 4%–12% transponder/spectrum savings can be achieved in realistic network instances by simply leveraging the SNR monitored at receivers and paying off a low increment in the lightpath disruption probability (at most 1%–4%).

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“…Similarly, the expected fiber effective area is given as 83 , with no range or tolerance. However, the value of has significant uncertainty for deployed fibers, which may suffer from aging effects, or in some cases, the fiber type may even be unknown (Seve et al, 2019). Moreover, variations during the manufacturing process can modify the value of Thus, we define a broad range for , from 0.9 to 1.2 W km .…”

Section: Experimental Systemmentioning

confidence: 99%

Optical network physical layer parameter optimization for digital backpropagation using Gaussian processes

Nevin¹,

Sillekens²,

Sohanpal³

et al. 2023

DCE

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We present a novel methodology for optimizing fiber optic network performance by determining the ideal values for attenuation, nonlinearity, and dispersion parameters in terms of achieved signal-to-noise ratio (SNR) gain from digital backpropagation (DBP). Our approach uses Gaussian process regression, a probabilistic machine learning technique, to create a computationally efficient model for mapping these parameters to the resulting SNR after applying DBP. We then use simplicial homology global optimization to find the parameter values that yield maximum SNR for the Gaussian process model within a set of a priori bounds. This approach optimizes the parameters in terms of the DBP gain at the receiver. We demonstrate the effectiveness of our method through simulation and experimental testing, achieving optimal estimates of the dispersion, nonlinearity, and attenuation parameters. Our approach also highlights the limitations of traditional one-at-a-time grid search methods and emphasizes the interpretability of the technique. This methodology has broad applications in engineering and can be used to optimize performance in various systems beyond optical networks.

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Automated Fiber Type Identification in SDN-Enabled Optical Networks

Cited by 16 publications

References 24 publications

Optical Systems Identification through Rayleigh Backscattering

Optical Systems Identification through Rayleigh Backscattering

Probabilistic low-margin optical-network design with multiple physical-layer parameter uncertainties

Optical network physical layer parameter optimization for digital backpropagation using Gaussian processes

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