Iterative Resource Allocation Algorithm for EONs Based on a Linearized GN Model
Journal article, 2019

Elastic optical networks (EONs) rely on efficient resource planning to meet future communication needs and avoid resource overprovisioning. Estimation of physical-layer impairments (PLIs) in EONs plays an important role in the network planning stage. Traditionally, the transmission reach (TR) and Gaussian noise (GN) models have been broadly employed in the estimation of the PLIs. However, the TR model cannot accurately estimate PLIs, whereas the GN model is incompatible with state of the art linear optimization solvers. In this paper, we propose a physical-layer estimation model based on the GN model, referred to as the conservative linearized Gaussian noise (CLGN) model. To address the routing, spectrum, and regeneration assignment problem accounting for PLIs, we introduce a link-based mixed integer linear programming formulation employing the CLGN, whose heavy computational burden is relieved by a heuristic approach referred to as the sequential iterative optimization algorithm. We show through simulation that network resources such as spectrum and regeneration nodes can be saved utilizing the CLGN model rather than the TR model. Our proposed heuristic algorithm speeds up the optimization process and provides better resource usage compared to state of the art algorithms on benchmark networks.

Regeneration placement

Gaussian noise model

Routing and spectrum allocation

Elastic optical networks

Author

Yuxin Xu

University of Virginia

Li Yan

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Erik Agrell

Chalmers, Electrical Engineering, Communication and Antenna Systems, Communication Systems

Maite Brandt-Pearce

University of Virginia

Journal of Optical Communications and Networking

1943-0620 (ISSN)

Vol. 11 3 39-51

Optical Fiber Interference is Not Noise

Swedish Research Council (VR), 2014-01-01 -- 2017-12-31.

Subject Categories

Telecommunications

Communication Systems

Control Engineering

DOI

10.1364/JOCN.11.000039

More information

Latest update

8/28/2019