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Spectrum sensing is an essential functionality of cognitive radio networks. In this paper, a noncooperative game framework is proposed for studying the interactions between multiple secondary strategic users in spectrum sensing. The licensed spectrum of single primary user is divided into K sub-bands, each secondary user operates exclusively in one sub-band. In each time interval, secondary users are optimally selected to perform cooperative sensing. We model this scenario as a noncooperative game and analyze it by exploring the properties of Nash equilibrium point. We further develop a distributed learning algorithm so that the secondary users approach the NE solely based on their own payoff observations. The simulation results show that the proposed scheme can significantly increase the total throughput than having all secondary users sensing in every time slot. Moreover, the average throughput per user in the sensing game is higher than the case where secondary user sense individually without cooperation.