Multi-Objective Clustering Optimization for Multi-Channel Cooperative Spectrum Sensing in Heterogeneous Green CRNs

Handle URI:
http://hdl.handle.net/10754/625017
Title:
Multi-Objective Clustering Optimization for Multi-Channel Cooperative Spectrum Sensing in Heterogeneous Green CRNs
Authors:
Celik, Abdulkadir ( 0000-0001-9007-9979 ) ; Kamal, Ahmed E.
Abstract:
In 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.
KAUST Department:
King Abdullah University of Science and Technology
Citation:
Celik 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.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Cognitive Communications and Networking
Issue Date:
27-Jun-2016
DOI:
10.1109/tccn.2016.2585130
Type:
Article
ISSN:
2332-7731
Additional Links:
http://ieeexplore.ieee.org/document/7500096/
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorCelik, Abdulkadiren
dc.contributor.authorKamal, Ahmed E.en
dc.date.accessioned2017-06-14T12:17:34Z-
dc.date.available2017-06-14T12:17:34Z-
dc.date.issued2016-06-27en
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.en
dc.identifier.issn2332-7731en
dc.identifier.doi10.1109/tccn.2016.2585130en
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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/document/7500096/en
dc.subjectmulti-hop reportingen
dc.subjectMulti-objective clusteringen
dc.subjectcluster head selectionen
dc.subjectPoisson-Binomialen
dc.subjectheterogeneous voting ruleen
dc.subjectweighted sample size testen
dc.subjectenergy efficiencyen
dc.titleMulti-Objective Clustering Optimization for Multi-Channel Cooperative Spectrum Sensing in Heterogeneous Green CRNsen
dc.typeArticleen
dc.contributor.departmentKing Abdullah University of Science and Technologyen
dc.identifier.journalIEEE Transactions on Cognitive Communications and Networkingen
dc.contributor.institutionDepartment of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011 USAen
kaust.authorCelik, Abdulkadiren
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