2017
DOI: 10.26636/jtit.2017.118817
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Reconfigurable Antenna Arrays with Phase-only Control in the Presence of Near-field Nulls

Abstract: Abstract-In this paper an effective iterative method is presented for the power synthesis of reconfigurable antenna arrays. The algorithm is suitable for arrays of arbitrary geometry, including the case where a large number of elements is involved. The reconfigurability is achieved by phase-only control, so that the excitation amplitude of each array element remains constant during the reconfiguration process. Such amplitudes may be different from one array element to the others, and they are not assigned a pr… Show more

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Cited by 3 publications
(2 citation statements)
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“…With the help of a simulated annealing optimization technique, Diaz et al suggested, in paper [5], a technique which enables the effective synthesis of multiple-patterns of linear array antennas characterized by constant amplitude dispersals that differ in phase only. An efficient iterative approach for the power synthesis of programmable array antennas is provided by Buttazzoni and Vescovo in [6]. The approach is applicable to arrays of any shape, covering situations when there are many items present.…”
Section: Introductionmentioning
confidence: 99%
“…With the help of a simulated annealing optimization technique, Diaz et al suggested, in paper [5], a technique which enables the effective synthesis of multiple-patterns of linear array antennas characterized by constant amplitude dispersals that differ in phase only. An efficient iterative approach for the power synthesis of programmable array antennas is provided by Buttazzoni and Vescovo in [6]. The approach is applicable to arrays of any shape, covering situations when there are many items present.…”
Section: Introductionmentioning
confidence: 99%
“…In [8], best common amplitude distribution was searched for several beam shapes (flat-topped, cosecant, and pencil) in reconfigurable arrays with phase-only control using the intersection approach. The same problem was addressed in [9] and [10] using convex optimization and in [11] using vector projection approach.…”
Section: Introductionmentioning
confidence: 99%