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dc.contributor.authorUykan, Zekeriya
dc.date.accessioned2021-06-14T20:25:49Z
dc.date.available2021-06-14T20:25:49Z
dc.date.issued2015
dc.identifier.issn1300-0632
dc.identifier.issn1300-0632
dc.identifier.urihttps://app.trdizin.gov.tr/makale/TVRZNE56ZzJOZz09
dc.identifier.urihttps://hdl.handle.net/11376/3848
dc.description.abstractAbstract: The channel allocation problem in cellular radio systems is NP-complete, and thus its general solution is not known for even the 2-channel case. It is well known that the link gain system matrix (or received-signal power system matrix) of the radio network is (and may be highly) asymmetric, and that as the Hopfield neural network is applied to optimization problems, its weight matrix should be symmetric. The main contribution of this paper is as follows: turning the channel allocation problem into a maxCut graph partitioning problem, we propose a simple and effective continuous-time Hopfield neural network-based solution by determining its symmetric weight matrix from the asymmetric received-signal-power-system matrix. Computer simulations confirm the effectiveness and superiority of the proposed solution as compared to standard algorithms for various illustrative cellular radio scenarios for the 2-channel case.en_US
dc.description.abstractAbstract: The channel allocation problem in cellular radio systems is NP-complete, and thus its general solution is not known for even the 2-channel case. It is well known that the link gain system matrix (or received-signal power system matrix) of the radio network is (and may be highly) asymmetric, and that as the Hopfield neural network is applied to optimization problems, its weight matrix should be symmetric. The main contribution of this paper is as follows: turning the channel allocation problem into a maxCut graph partitioning problem, we propose a simple and effective continuous-time Hopfield neural network-based solution by determining its symmetric weight matrix from the asymmetric received-signal-power-system matrix. Computer simulations confirm the effectiveness and superiority of the proposed solution as compared to standard algorithms for various illustrative cellular radio scenarios for the 2-channel case.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMühendislik, Elektrik ve Elektroniken_US
dc.titleContinuous-timeHopfieldneural network-based optimized solution to2-channel allocation problemen_US
dc.typearticleen_US
dc.relation.journalTurkish Journal of Electrical Engineering and Computer Sciencesen_US
dc.department[0-Belirlenecek]en_US
dc.identifier.volume23en_US
dc.identifier.issue2en_US
dc.identifier.startpage480en_US
dc.identifier.endpage490en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanen_US
dc.institutionauthor[0-Belirlenecek]
dc.department-tempDoğuş Üniversitesi, Mühendislik Fakültesi, Otomasyon ve Kontrol Mühendisliği Bölümü, İstanbul, Türkiyeen_US
dc.identifier.trdizinidTVRZNE56ZzJOZz09en_US


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