?url_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rft.relation=http%3A%2F%2Fwww.eledia.org%2Fstudents-reports%2F888%2F&rft.title=Hilbert-Based+Clustering+Approach+for+Linear+Arrays&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+research+examines+the+clustering+of+linear+phased+arrays+(PAs)%0Dincorporating+complex+weights.+Through+exploitation+of+the+intrinsic%0Dlocality-preservation+property+associated+with+the+Hilbert+curve%2C+the+problem's%0Ddimensionality+is+decreased.+Subsequently%2C+a+basic+clustering+algorithm%0Dis+employed+to+optimize+the+alignment+of+the+radiated+pattern+with+a%0Dpredetermined+reference.+We+systematically+assess+both+contiguous+and%0Dnoncontiguous+partitions+of+the+Hilbert-ordered+list+of+complex+excitations%0Dto+comprehensively+sample+the+solution+space.+Representative+outcomes%2C%0Dencompassing+reference+PAs+generating+steered+pencil+and+shaped+beams%2C+are%0Dprovided+for+validation+and+to+underscore+the+efficacy+of+our+methodology%0Drelative+to+state-of-the-art+k-means+algorithms.&rft.publisher=ELEDIA+Research+Center+-+University+of+Trento&rft.date=2024-06-07&rft.type=Monograph&rft.type=NonPeerReviewed&rft.format=text&rft.language=en&rft.identifier=http%3A%2F%2Fwww.eledia.org%2Fstudents-reports%2F888%2F1%2FHilbert-Based_Clustering_Approach_for_Linear_Arrays.pdf&rft.identifier=++BENONI%2C+Arianna+and+ROCCA%2C+Paolo+and+ANSELMI%2C+Nicola+and+MASSA%2C+Andrea++(2024)+Hilbert-Based+Clustering+Approach+for+Linear+Arrays.++Technical+Report.+ELEDIA+Research+Center+-+University+of+Trento.+++++