Transmission in Elastic Optical Networks

Napoli, Antonio; Rafique, Danish; Bohn, Marc; Nölle, Markus; Fischer, Johannes; Schubert, C.

doi:10.1007/978-3-319-30174-7_5

Cited by 1 publication

(1 citation statement)

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“…For simplicity, it is assumed that SNR (signal to noise ratio) loss in the EON nodes can be compensated such that transmission reach for a given modulation format is a fixed distance. Techniques for compensation of SNR losses due to various components in the nodes have been discussed in [22]. With the above assumption, the transmission reach for QPSK is selected to be 7,200 Km [7].…”

Section: Simulation Modelmentioning

confidence: 99%

Dynamic multi-hop multipath RMSA and traffic grooming in OFDM-based sliceable elastic optical networks

Lin

Tseng

2023

JHS

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In elastic optical networks (EON), routing, modulation selection, and spectrum assignment (RMSA) is crucial in provisioning connection requests. Multipath RMSA offers a number of benefits including provisioning of ultra-high bandwidth demands and better utilization of fragmented spectrum resource. By Combining with traffic grooming, multipath RMSA and traffic grooming is able to provide better utilization of network resource in provisioning connection requests. Adding multi-hop routing mechanism to multipath RMSA and traffic grooming increases the flexibility for selecting paths resulting in higher probability of successfully finding routing paths for connection requests. Dynamic multipath RMSA problem in EON has been investigated extensively in the literature. Dynamic multi-hop multipath RMSA and traffic grooming problem in EON is far from been well studied. This paper proposes an algorithm for the dynamic multi-hop multipath RMSA and traffic grooming problem in OFDM-based elastic optical networks with sliceable bandwidth-variable transponders. Performance of the proposed algorithm is studied via simulation. Our simulation results show that the proposed algorithm yields lower bandwidth blocking ratio than an existing algorithm.

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Section: Simulation Modelmentioning

confidence: 99%