relation: http://www.eledia.org/students-reports/888/
title: Hilbert-Based Clustering Approach for Linear Arrays
creator: BENONI, Arianna
creator: ROCCA, Paolo
creator: ANSELMI, Nicola
creator: MASSA, Andrea
subject: A WC Next Generation Wireless Communications
subject: M AT Analytic Techniques
description: This research examines the clustering of linear phased arrays (PAs)  incorporating complex weights. Through exploitation of the intrinsic  locality-preservation property associated with the Hilbert curve, the problem's  dimensionality is decreased. Subsequently, a basic clustering algorithm  is employed to optimize the alignment of the radiated pattern with a  predetermined reference. We systematically assess both contiguous and  noncontiguous partitions of the Hilbert-ordered list of complex excitations  to comprehensively sample the solution space. Representative outcomes,  encompassing reference PAs generating steered pencil and shaped beams, are  provided for validation and to underscore the efficacy of our methodology  relative to state-of-the-art k-means algorithms.
publisher: ELEDIA Research Center - University of Trento
date: 2024-06-07
type: Monograph
type: NonPeerReviewed
format: text
language: en
identifier: http://www.eledia.org/students-reports/888/1/Hilbert-Based_Clustering_Approach_for_Linear_Arrays.pdf
identifier:   BENONI, Arianna and ROCCA, Paolo and ANSELMI, Nicola and MASSA, Andrea  (2024) Hilbert-Based Clustering Approach for Linear Arrays.  Technical Report. ELEDIA Research Center - University of Trento.