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Resource Allocation in Elastic Optical Networks With Physical-Layer Impairments

Xu, Yuxin
Thesis/Dissertation; Online
Xu, Yuxin
Brandt-Pearce, Maite
Elastic optical networks (EONs) have been proposed to meet future communication demands [1]. Planning the resource usage of EONs has been the subject of extensive research. Routing and spectrum assignment (RSA) algorithms are used to minimize the network resources used. Estimation of physical-layer impairments (PLIs) in EONs plays an important role in the network planning stage. The transmission reach (TR) model and the Gaussian noise (GN) model are broadly considered in the estimation of the PLIs. However, due to the nature of these models, their performance remains problematic. Thus, based on the GN model, this thesis proposes a physical layer estimation model, referred to as the conservative linearized Gaussian noise (CLGN) model. In addition, we improve upon the existing TR model with a novel algorithm for obtaining the parameters, leading to a fairer comparison between the TR model and the CLGN model. We then introduce a link-based mixed integer linear programming (MILP) formulation to address the RSA problem to quantify the performance of each PLI model. Suffering from the large computational burden brought by the MILP, we propose a heuristic algorithm, referred to as the sequential allocation (SA) algorithm. The SA algorithm can solve a large number of demands in a large scale network with a reasonable computational burden. Lastly, we show through simulation that network resources such as spectrum and regeneration nodes can be saved by utilizing the CLGN model, compared with the TR model. We also show that the SA algorithm has notably better optimization solutions, compared with a published algorithm, the recursive MILP [2]. Moreover, we also show that our proposed system, which is based on the CLGN model and the SA algorithm, speeds up the optimization process and provides similar resource usage, compared to the published benchmark system in [3]. Refer to the Thesis text for the reference details for this abstract.
University of Virginia, Department of Electrical Engineering, MS (Master of Science), 2017
Published Date
MS (Master of Science)
CC-BY (permitting free use with proper attribution)
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