No abstract

This paper presents a routing, modulation, spectrum, and core allocation (RMSCA) algorithm for space-division multiplexing-based elastic optical networks (SDM-EONs). A network state-dependent route and core selection method is proposed using a multi-attribute decision-making method based on the analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluations (PROMETHEE) methods. This systematic resource allocation allows the network designer to choose which resources are most valuable. It is followed by a spectrum allocation algorithm using a weighted score function to rate and select the best spectrum blocks. Physical layer impairments, including inter-core cross talk, amplified spontaneous emission, and Kerr fiber nonlinearities, are considered alongside fragmentation and energy consumption. The proposed RMSCA approach is compared with published benchmarks incorporating quality of transmission constraints and evaluated on two network topologies, NSFNET (7- and 12-core multicore fiber links) and COST. It is shown to be superior in terms of blocking probability, bandwidth blocking probability, network fragmentation, and energy consumption compared to standard and published benchmarks.

No abstract

The aim of this paper is to display the efficacy of three newly proposed optimization algorithms named as Rao-1, Rao-2, and Rao-3 in synthesizing antenna arrays. The algorithms are applied to three different antenna array configurations. Thinned arrays with isotropic radiators are considered and the main objective is to find the optimal configuration of ON/OFF elements that produce low side lobe levels. The results of Rao-1, Rao-2, and Rao-3 algorithms are compared with those of improved genetic algorithm (IGA), hybrid Taguchi binary particle swarm optimization (HTBPSO), teaching-learning-based optimization (TLBO), the firefly algorithm (FA), and biogeography-based optimization (BBO). The Rao-1, Rao-2, and Rao-3 algorithms were able to realize antenna arrays having lower side lobe levels (SLL) when compared to the other optimization algorithms.

No abstract