Modeling and Analysis of Random Periodic Spectrum Sensing for Cognitive Radio Networks
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Abstract
A random periodic spectrum sensing scheme is proposed for cognitive radio networks. The sensing period, the transmission time for primary users and cognitive radios are extended to general forms as random variables. A generalized Markov analytical model for sensing period optimization is presented, and the applications of the proposed analytical model by using examples involving primary user systems with both voice and data traffic are illustrated. The analysis and numerical results show that sensing period does affect the maximum rewards of the channel, and the analytical model is justified by its flexibility since it uses general forms of the sensing period, the transmission time for primary users and cognitive radios. Hence the model can be easily adapted for the analysis of many different applications.
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