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

Type
Article

Authors
Celik, Abdulkadir
Kamal, Ahmed E.

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Online Publication Date
2016-06-27

Print Publication Date
2016-06

Date
2016-06-27

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.

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

DOI
10.1109/tccn.2016.2585130

Additional Links
http://ieeexplore.ieee.org/document/7500096/

Permanent link to this record