Approach for cluster-based spectrum sensing over band-limited reporting channels

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Approach for cluster-based spectrum sensing over band-limited reporting channels

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In this study, the authors address the problem of bandwidth limitations of the reporting channels in cognitive radio (CR) networks. They propose a cluster-based spectrum-sensing approach that minimizes the bandwidth requirements by reducing the number of terminals reporting to the fusion centre to a minimal reporting set. The approach replaces the secondary base station by a local fusion centre and combats the destructive channel conditions by replacing the global reporting channels with local channels. They also propose a new approach to select the location of the local fusion centre using the general centre scheme in graph theory. The minimal dominating set (MDS) clustering algorithm is used to obtain the minimal set of clusters that keep the network connected. This study investigates how the sensing efficiency, the sensing accuracy, and the per-node throughput are affected by the cluster size, the number of clusters, and the reporting channels error. The results obtained reveal that the cluster-based cooperative sensing system outperforms the conventuional cooperative sensing system in terms of throughout capacity especially when the reporting channels are subjected to a high probability of error. A systematic way to find the optimal number of cooperative clusters that gives a minimum probability of false alarm is presented.

Inspec keywords: cooperative communication; wireless channels; pattern clustering; channel capacity; graph theory; probability; cognitive radio; radio networks

Other keywords: cluster-based spectrum-sensing approach; general centre scheme; channel error; global reporting channel; graph theory; CR network; cognitive radio network; bandlimited reporting channel; secondary base station; channel capacity; minimal dominating set clustering algorithm; cluster-based cooperative sensing system; false alarm probability; bandwidth limitation; MDS clustering algorithm; local fusion centre; cluster size

Subjects: Radio links and equipment; Combinatorial mathematics; Other topics in statistics

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