Abstract-We introduce an algorithm for optimizing sensing parameters including the number of sensing samples and the number of reporting bits of a quantization-based cooperative spectrum sensing scheme in cognitive radio networks. This is obtained by maximizing the network throughput subject to a target detection probability. With Rayleigh fading and energy detector, the proposed algorithm simultaneously optimizes the number of sensing samples at a local node, the number of bits for quantizing local sensing data and the global threshold at a fusion center.
Developing an effective cooperative spectrum sensing (CSS) scheme in cognitive radio (CR), which is considered as promising system for enhancing spectrum utilization, is necessary. In this paper, a cluster-based optimal selective CSS scheme is proposed for reducing reporting time and bandwidth while maintaining a certain level of sensing performance. Clusters are organized based on the identification of primary signal signal-to-noise ratio value, and the cluster head in each cluster is dynamically chosen according to the sensing data qualities of CR users. The cluster sensing decision is made based on an optimal threshold for selective CSS which minimizes the probability of sensing error. A parallel reporting mechanism based on frequency division is proposed to considerably reduce the time for reporting decision to fusion center of clusters. In the fusion center, the optimal Chair-Vashney rule is utilized to obtain a high sensing performance based on the available cluster's information.
We investigate the primary user emulation (PUE) attack which is a serious security problem in cognitive radio (CR) networks. There exist three types of PUE attackers: i) selfish one which aims at maximizing its selfish usage of channel resource, ii) malicious one which points for obstructing the operation of CR network, and iii) mixed between selfish and malicious PUE attacker. For combating a selfish PUE attacker, a channel surveillance process has to be implemented in order to determine active user's identification and so selfish PUE attacker. An extra-sensing process has to be implemented for observing new opportunities to access the channel and so for mitigating the malicious PUE attacker's effect. Relevant strategies for deploying the above processes are obtained through a game theory-based analysis and the exhibition of Nash equilibrium (NE). We show the NE strongly depend on the network demand, the availability of the spectrum resource, and the type of the attacker. We also show the proposed defensive strategies (surveillance and extra sensing) are efficient for combating the effects of PUE attackers.
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