Zobrazit minimální záznam

dc.contributor.authorAbbass, Waseem
dc.contributor.authorHussain, Riaz
dc.contributor.authorFrnda, Jaroslav
dc.contributor.authorAbbas, Nasim
dc.contributor.authorJaved, Muhammad Awais
dc.contributor.authorMalik, Shahzad A.
dc.date.accessioned2022-06-17T11:36:33Z
dc.date.available2022-06-17T11:36:33Z
dc.date.issued2022
dc.identifier.citationSensors. 2022, vol. 22, issue 4, art. no. 1318.cs
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10084/146286
dc.description.abstractThe paradigm of dynamic shared access aims to provide flexible spectrum usage. Recently, Federal Communications Commission (FCC) has proposed a new dynamic spectrum management framework for the sharing of a 3.5 GHz (3550-3700 MHz) federal band, called a citizen broadband radio service (CBRS) band, which is governed by spectrum access system (SAS). It is the responsibility of SAS to manage the set of CBRS-SAS users. The set of users are classified in three tiers: incumbent access (IA) users, primary access license (PAL) users and the general authorized access (GAA) users. In this article, dynamic channel assignment algorithm for PAL and GAA users is designed with the goal of maximizing the transmission rate and minimizing the total cost of GAA users accessing PAL reserved channels. We proposed a new mathematical model based on multi-objective optimization for the selection of PAL operators and idle PAL reserved channels allocation to GAA users considering the diversity of PAL reserved channels' attributes and the diversification of GAA users' business needs. The proposed model is estimated and validated on various performance metrics through extensive simulations and compared with existing algorithms such as Hungarian algorithm, auction algorithm and Gale-Shapley algorithm. The proposed model results indicate that overall transmission rate, net cost and data-rate per unit cost remain the same in comparison to the classical Hungarian method and auction algorithm. However, the improved model solves the resource allocation problem approximately up to four times faster with better load management, which validates the efficiency of our model.cs
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofseriesSensorscs
dc.relation.urihttps://doi.org/10.3390/s22041318cs
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subject5Gcs
dc.subjectSAScs
dc.subjectCBRScs
dc.subjectoptimizationcs
dc.subjectchannel assignmentcs
dc.subjectlinear assignment problemscs
dc.subjectmultiobjectivecs
dc.titleResource allocation in spectrum access system using multi-objective optimization methodscs
dc.typearticlecs
dc.identifier.doi10.3390/s22041318
dc.rights.accessopenAccesscs
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume22cs
dc.description.issue4cs
dc.description.firstpageart. no. 1318cs
dc.identifier.wos000773033400001


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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Kromě případů, kde je uvedeno jinak, licence tohoto záznamu je © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.