Linear antenna arrays consisting of dipole antennas find wide applications in wireless communication systems. Typically, arrangements with uniform spacing between the array elements are applied to gain highly directive radiation patterns while controlling the radiation intensity in the side lobes. To describe such arrangements, the so-called pattern multiplication (PM) technique is frequently applied, which inherently neglects the inter-element coupling. In this paper, an antenna array arrangement with non-uniformly spacing between the array elements is synthesized in the multi-objective sense. Therefore, the PM technique is deficient. Hence, numerical techniques have to be applied to compute the electromagnetic field during the optimization process. Due to its simplicity and the consequently reduced computational costs, the partial element equivalent circuit method is applied within the field computation process. The optimization relies on a stochastic particle swarm optimization stra...

PEEC-based multi-objective synthesis of non-uniformly spaced linear antenna arrays

Alotto, Piergiorgio;Torchio, Riccardo
2017

Abstract

Linear antenna arrays consisting of dipole antennas find wide applications in wireless communication systems. Typically, arrangements with uniform spacing between the array elements are applied to gain highly directive radiation patterns while controlling the radiation intensity in the side lobes. To describe such arrangements, the so-called pattern multiplication (PM) technique is frequently applied, which inherently neglects the inter-element coupling. In this paper, an antenna array arrangement with non-uniformly spacing between the array elements is synthesized in the multi-objective sense. Therefore, the PM technique is deficient. Hence, numerical techniques have to be applied to compute the electromagnetic field during the optimization process. Due to its simplicity and the consequently reduced computational costs, the partial element equivalent circuit method is applied within the field computation process. The optimization relies on a stochastic particle swarm optimization stra...
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3249877
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