Show simple item record

dc.contributor.authorCelik, Abdulkadir
dc.contributor.authorKamal, Ahmed E.
dc.date.accessioned2017-06-14T12:17:34Z
dc.date.available2017-06-14T12:17:34Z
dc.date.issued2016-06-27
dc.identifier.citationCelik A, Kamal AE (2016) Multi-Objective Clustering Optimization for Multi-Channel Cooperative Spectrum Sensing in Heterogeneous Green CRNs. IEEE Transactions on Cognitive Communications and Networking 2: 150–161. Available: http://dx.doi.org/10.1109/tccn.2016.2585130.
dc.identifier.issn2332-7731
dc.identifier.doi10.1109/tccn.2016.2585130
dc.identifier.urihttp://hdl.handle.net/10754/625017
dc.description.abstractIn this paper, we address energy efficient (EE) cooperative spectrum sensing policies for large scale heterogeneous cognitive radio networks (CRNs) which consist of multiple primary channels and large number of secondary users (SUs) with heterogeneous sensing and reporting channel qualities. We approach this issue from macro and micro perspectives. Macro perspective groups SUs into clusters with the objectives: 1) total energy consumption minimization; 2) total throughput maximization; and 3) inter-cluster energy and throughput fairness. We adopt and demonstrate how to solve these using the nondominated sorting genetic algorithm-II. The micro perspective, on the other hand, operates as a sub-procedure on cluster formations decided by the macro perspective. For the micro perspectives, we first propose a procedure to select the cluster head (CH) which yields: 1) the best CH which gives the minimum total multi-hop error rate and 2) the optimal routing paths from SUs to the CH. Exploiting Poisson-Binomial distribution, a novel and generalized K-out-of-N voting rule is developed for heterogeneous CRNs to allow SUs to have different local detection performances. Then, a convex optimization framework is established to minimize the intra-cluster energy cost by jointly obtaining the optimal sensing durations and thresholds of feature detectors for the proposed voting rule. Likewise, instead of a common fixed sample size test, we developed a weighted sample size test for quantized soft decision fusion to obtain a more EE regime under heterogeneity. We have shown that the combination of proposed CH selection and cooperation schemes gives a superior performance in terms of energy efficiency and robustness against reporting error wall.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/document/7500096/
dc.subjectmulti-hop reporting
dc.subjectMulti-objective clustering
dc.subjectcluster head selection
dc.subjectPoisson-Binomial
dc.subjectheterogeneous voting rule
dc.subjectweighted sample size test
dc.subjectenergy efficiency
dc.titleMulti-Objective Clustering Optimization for Multi-Channel Cooperative Spectrum Sensing in Heterogeneous Green CRNs
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalIEEE Transactions on Cognitive Communications and Networking
dc.contributor.institutionDepartment of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011 USA
kaust.personCelik, Abdulkadir
dc.date.published-online2016-06-27
dc.date.published-print2016-06


This item appears in the following Collection(s)

Show simple item record