?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.relation=http%3A%2F%2Fwww.eledia.org%2Fstudents-reports%2F887%2F&rft.title=Enhanced+Linear+Array+Clustering+via+Hilbert+Order&rft.creator=BENONI%2C+Arianna&rft.creator=ROCCA%2C+Paolo&rft.creator=ANSELMI%2C+Nicola&rft.creator=MASSA%2C+Andrea&rft.subject=A+WC+Next+Generation+Wireless+Communications&rft.subject=M+AT+Analytic+Techniques&rft.description=This+study+investigates+the+clustering+of+linear+phased+arrays+(PAs)+featuring+complex+weights.+By+exploiting+the+inherent+locality-preserving+characteristic+of+the+Hilbert+curve%2C+an+initial+reduction+of+problem+dimensionality+is+performed.%0DSubsequently%2C+a+basic+clustering+algorithm+is+employed+to+enhance+the+alignment+of+the+radiated+pattern+with+a+predetermined+reference.%0DWe+systematically+explore+both+contiguous+and+noncontiguous+partitions+of+the+Hilbert-ordered+list+of+complex+excitations+to+comprehensively+sample+the+solution+space.+Representative+results%2C+including+reference+PAs+generating+steered+pencil+and+shaped+beams%2C+are+included+to+highlight+the+superior+performance+of+the+presented+approach+over+conventional+k-means+algorithms.&rft.publisher=ELEDIA+Research+Center+-+University+of+Trento&rft.date=2024-05-31&rft.type=Monograph&rft.type=NonPeerReviewed&rft.format=text&rft.language=en&rft.identifier=http%3A%2F%2Fwww.eledia.org%2Fstudents-reports%2F887%2F1%2FEnhanced_Linear_Array_Clustering_via_Hilbert_Order.pdf&rft.identifier=++BENONI%2C+Arianna+and+ROCCA%2C+Paolo+and+ANSELMI%2C+Nicola+and+MASSA%2C+Andrea++(2024)+Enhanced+Linear+Array+Clustering+via+Hilbert+Order.++Technical+Report.+ELEDIA+Research+Center+-+University+of+Trento.+++++