QoT-Aware Optical Amplifier Placement in Filterless Metro Networks

Ibrahimi, Memedhe; Ayoub, Omran; Karandin, Oleg; Musumeci, Francesco; Castoldi, A.; Pastorelli, R.; Tornatore, Massimo

doi:10.1109/lcomm.2020.3034736

Cited by 18 publications

(15 citation statements)

References 8 publications

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“…A flow diagram describing the behaviour of the Genetic Algorithm (GA-FF) is illustrated in Fig. 3 [40], [41]. The parameters of GA-FF have been finetuned in order to get the best trade-off between computing times and solution quality.…”

Section: Illustrative Numerical Resultsmentioning

confidence: 99%

On Deep Reinforcement Learning for Static Routing and Wavelength Assignment

Cicco

Mercan²,

Karandin³

et al. 2022

IEEE J. Select. Topics Quantum Electron.

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Deep Reinforcement Learning (DRL) is rising as a promising tool for solving optimization problems in optical networks. Though studies employing DRL for solving static optimization problems in optical networks are appearing, assessing strengths and weaknesses of DRL with respect to state-of-theart solution methods is still an open research question. In this work, we focus on Routing and Wavelength Assignment (RWA), a well-studied problem for which fast and scalable algorithms leading to better optimality gaps are always sought for. We develop two different DRL-based methods to assess the impact of different design choices on DRL performance. In addition, we propose a Multi-Start approach that can improve the average DRL performance, and we engineer a shaped reward that allows efficient learning in networks with high link capacities. With Multi-Start, DRL gets competitive results with respect to a state-of-the-art Genetic Algorithm with significant savings in computational times. Moreover, we assess the generalization capabilities of DRL to traffic matrices unseen during training, in terms of total connection requests and traffic distribution, showing that DRL can generalize on small to moderate deviations with respect to the training traffic matrices. Finally, we assess DRL scalability with respect to topology size and link capacity.

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Section: Illustrative Numerical Resultsmentioning

confidence: 99%

On Deep Reinforcement Learning for Static Routing and Wavelength Assignment

Cicco

Mercan²,

Karandin³

et al. 2022

IEEE J. Select. Topics Quantum Electron.

Self Cite

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“…It includes three subroutines: fiber tree establishment, OA placement and transponder selection. First two subroutines use the Genetic Algorithm (GA) introduced in [4]. Note that the subroutines can be carried out independently (e.g., TE can be performed to minimize the longest lightpath, without optimizing OA placement) to separately estimate the effect of the minimization of the number of each device (WSS, OA and transponder).…”

Section: A Tree Establishment and Optical Amplifier Placementmentioning

confidence: 99%

“…To estimate SNR of the lightpaths, we use the LOGONmodel [5], adapted to FON in [4]. It assumes full spectrum occupation, hence OA placement and TE result to be independent of traffic scenarios.…”

Section: B Physical Modelmentioning

confidence: 99%

“…Characteristics of EDFAs are taken from [4], and their gains are set to compensate propagation losses as in [7]. Received optical power (computed as in [7]) must be above -18 dBm, putting a constraint on OA placement.…”

Section: B Physical Modelmentioning

confidence: 99%

“…Ref. [4] shows that we can remove up to 55% of OAs in FON networks without deteriorating Signal-to-Noise Ratio (SNR).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optical Metro Network Design with Low Cost of Equipment

Karandin

Ayoub

Ibrahimi

et al. 2021

2021 International Conference on Optical Network Design and Modeling (ONDM)

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New arising 5G services will pressure optical metro networks with unprecedented requirements. As operators' revenues are not scaling accordingly, several technical directions to devise low-cost architectures for optical metro networks are being investigated. In this study we observe that operators can jointly i) enforce Filterless Optical Network (FON) architecture, hence removing costly Wavelength Selective Switches, ii) leverage relatively short links in metro networks to reduce the number of optical amplifiers (OAs), iii) place amplifiers intelligently to maximize signal quality (SNR), hence employing higher-order modulation formats (MF) and reducing the number of transponders. To quantify the achievable cost-reduction we develop a Quality-of-Transmission (QoT) aware planning tool, based on Genetic Algorithm, for joint optimization of fiber tree establishment (inherent to FON), OA placement and MF assignment. Results obtained over a realistic topology show that the proposed "lowest-cost" architecture achieves overall 4-8% equipment cost savings compared to baseline optical network deployment, without affecting QoT.

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Innovative Cross-Layer Optimization Techniques for the Design of Optical Networks

Ibrahimi

2024

SpringerBriefs in Applied Sciences and Technology

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Optical networks have become indispensable in the era of 5G-and-beyond communications, supporting applications that require unprecedented capacity, reliability, and high Quality-of-Transmission (QoT) of lightpaths. To meet these requirements, network operators strive to provide innovative solutions while managing network costs effectively. This work summarizes the main findings of my Ph.D. thesis Innovative Cross-Layer Optimization Techniques for the Design of Filterless and Wavelength-Switched Optical Networks, that has been conducted in partnership with an industrial partner, SM-Optics. The main objective of the Ph.D. thesis is to investigate solutions to reduce network costs while enabling network expandability through novel network architectures. To ensure cost savings and scalability, (1) we optimize the deployment of Optical Amplifiers (OA) while accurately modeling physical layer impairments in filterless networks, (2) we propose a modular node architecture relying on pluggable devices and a scalable add/drop section at the node level for traffic grooming and capacity increase, and (3) we investigate the application of Machine Learning (ML)—regression approaches to estimate lightpaths’ QoT as they allow to make informed decisions about how conservative or aggressive a network operator can be when taking network planning choices, i.e., deploying a new lightpath. Numerical evaluations show that our proposed approaches achieve significant cost savings compared to benchmark approaches: (1) $${\sim }50\%$$ ∼ 50 % savings in OA cost, (2) $${\sim }50\%$$ ∼ 50 % savings in node architecture equipment cost, (3) $${\sim }70\%$$ ∼ 70 % in penalty costs for deploying wrong lightpath configurations.

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QoT-Aware Optical Amplifier Placement in Filterless Metro Networks

Cited by 18 publications

References 8 publications

On Deep Reinforcement Learning for Static Routing and Wavelength Assignment

On Deep Reinforcement Learning for Static Routing and Wavelength Assignment

Optical Metro Network Design with Low Cost of Equipment

Innovative Cross-Layer Optimization Techniques for the Design of Optical Networks

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