Multi-objective clustering optimization for multi-channel cooperative spectrum sensing in heterogeneous green CRNs
Multi-objective clustering optimization for multi-channel cooperative spectrum sensing in heterogeneous green CRNs
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 non-dominated 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.
cluster head selection, energy efficiency, heterogeneous voting rule, Multi-Hop reporting, Multi-objective clustering, Poisson-Binomial, weighted sample size test
150-161
Celik, Abdulkadir
f8e72266-763c-4849-b38e-2ea2f50a69d0
Kamal, Ahmed E.
b7e85bb0-fbc5-4dcd-80d6-011c900201dc
June 2016
Celik, Abdulkadir
f8e72266-763c-4849-b38e-2ea2f50a69d0
Kamal, Ahmed E.
b7e85bb0-fbc5-4dcd-80d6-011c900201dc
Celik, Abdulkadir and Kamal, Ahmed E.
(2016)
Multi-objective clustering optimization for multi-channel cooperative spectrum sensing in heterogeneous green CRNs.
IEEE Transactions on Cognitive Communications and Networking, 2 (2), , [7500096].
(doi:10.1109/TCCN.2016.2585130).
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 non-dominated 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.
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Published date: June 2016
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© 2015 IEEE.
Keywords:
cluster head selection, energy efficiency, heterogeneous voting rule, Multi-Hop reporting, Multi-objective clustering, Poisson-Binomial, weighted sample size test
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Local EPrints ID: 504465
URI: http://eprints.soton.ac.uk/id/eprint/504465
ISSN: 2332-7731
PURE UUID: c682e230-b4af-4922-9d25-162f52c0b982
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Date deposited: 09 Sep 2025 19:55
Last modified: 13 Sep 2025 02:40
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Author:
Abdulkadir Celik
Author:
Ahmed E. Kamal
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