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Newman, T. J.; Barker, Brett A.; Wyglinski, Alexander M.; Agah, Arvin; Evans, Joseph; Minden, Gary J.

doi:10.1002/wcm.v7:9

Cited by 86 publications

(1 citation statement)

References 134 publications

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“…With regard to forming clusters in CRN, deciding on the CC within each cluster is the foremost question to answer. Zhao et al, 16 Chen et al, 17 and Baddour et al 18 proposed different clustering schemes and enforced that every cluster possesses at least one CC. The clustering scheme in the work of Wu et al 11 looks for a network partition that improves the accuracy of spectrum sensing without considering robustness.…”

Section: Related Workmentioning

confidence: 99%

Robust clustering for ad hoc cognitive radio network

Fang

Gross

2018

Trans Emerging Tel Tech

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Cluster structure in cognitive radio (CR) networks facilitates cooperative spectrum sensing, routing, and other functionalities. Unlicensed channels, which are temporally available for a group of CR users in one area, consolidate the group into a cluster. More available unlicensed channels in a cluster make the cluster more likely to uphold against the licensed users' influence, making clusters more robust. This paper analyzes the problem of how to form robust clusters in a CR network such that CR systems benefit from collaboration within clusters despite intense primary user activity. We give a formal description of the robust clustering problem, prove it to be NP‐hard, and propose both centralized and distributed solutions. The congestion game model is adopted to analyze the process of cluster formation, which not only contributes to the design of the distributed clustering scheme but also provides a guarantee on the convergence to a Nash equilibrium and the convergence speed. The proposed distributed clustering scheme outperforms state‐of‐the‐art in terms of cluster robustness, convergence speed, and overhead. Extensive simulations are presented supporting the theoretical claims.

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Section: Related Workmentioning

confidence: 99%