Multicast Routing Algorithms Based on $Q$-Factor Physical-Layer Constraints in Metro Networks

Ellinas, Georgios; Antoniades, N.; Panayiotou, Tania; Hadjiantonis, A.; Levine, A. M.

doi:10.1109/lpt.2008.2011750

Cited by 32 publications

(9 citation statements)

References 10 publications

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“…The same procedure, as the one described in Section V, was followed for generating the dataset for Network B. The Q-threshold was again set at 9dB corresponding to a BER of 10 −15 and the multicast capable architecture with fixed TXs/RXs described in [2], [9] was again utilized.…”

Section: B Results For Network Bmentioning

confidence: 99%

“…The existing QoT model for multicast connections [2], [9], is merely a function of the physical layer impairments and is based on the Q-factor modeling approach presented in [3]. The Q-factor model was evaluated with time-consuming Monte Carlo simulations that are based on stochastic numerical sampling from distributions, and each PLI (e.g., distortion-induced penalty due to filter concatenation, crosstalk, ASE noise, polarization mode dispersion, etc.)…”

Section: Problem Statement and Approach Overviewmentioning

confidence: 99%

“…8 Table I. Further, the Q-factor formulation [2] in combination with the network architecture/engineering assuming multicast-capable nodes with fixed TXs/RXs were assumed. Note that for a detailed analysis of the Q-modeling, node architecture, and node engineering considered, the reader is referred to previous work in [2], [9].…”

Section: Training and Qot Decision Algorithmmentioning

confidence: 99%

“…However, if such applications are offered in transparent optical networks, the physical layer impairments must be taken into consideration to ensure that the signal can be correctly detected at the receiver [1]. Several works exist in the literature that use different representations for modeling the most important physical layer effects that can accumulate in a transparent optical network, such as ASE noise, crosstalk, optical filter concatenation, and polarization mode dispersion (PMD) amongst others [1], [2]. One approach for the modeling of the physical layer is based on the physical path Q-factor that is subsequently used to calculate the Bit Error Rate (BER) of the system, a parameter that is difficult to evaluate upfront.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, a feed-forward neural network from the context of pattern recognition is used that is reported to attain the above objectives [4]. The neural network is trained on a QoT dataset generated from the Q-factor formula described in detail in [2] and based on the analytical Q-budgeting approach proposed in [3]. The Q-factor model utilized for generating the data was developed for a multicast-capable metro network that considers multicast-capable node architecture/engineering for a network that spans a metropolitan area (i.e., utilizing an average distance of 80km between network nodes).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A data-driven QoT decision approach for multicast connections in metro optical networks

Panayiotou

Ellinas

Chatzis

2016

2016 International Conference on Optical Network Design and Modeling (ONDM)

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A data-driven technique for analyzing Quality-of-Transmission (QoT) data of previously established connections is proposed for accurately deciding the QoT of the newly arriving multicast requests in metro optical networks. The proposed approach is self-adaptive, it is a function of data that are independent from the physical layer impairment (PLIs) and thus does not require specific measurement equipment, and it does not assume the existence of a system with extensive processing and storage capabilities. It is also fast in processing new data, and fast in finding a near-accurate QoT model provided that such a model exists. The proposed technique can replace the existing Q-factor models that are not self-adaptive, they are a function of the PLIs, and their evaluation requires timeconsuming simulations, lab experiments, specific measurement equipment, and considerable human effort. The proposed datadriven QoT approach is based on the utilization of a feed-forward neural network that is trained on a dataset previously generated from a known Q-factor model. The dataset fed to the neural network is represented in a way that specifically describes the QoT of the multicast connections requesting to be established in the network but it is independent from the PLIs. The validity of the proposed approach is examined for two distinct networks, exhibiting a high accuracy when compared to the results of the Q-factor model utilized for generating the QoT data.

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Section: B Results For Network Bmentioning

confidence: 99%