Spectrum sensing is a significant issue in cognitive radio networks which enables estimation of the frequency spectrum and hence provides frequency reuse. In the large-scale cognitive radio networks, secondary users can not share a common spectrum since the coverage area of primary users is limited. In this paper, we suggest a diffusion adaptive learning algorithm based on correntropy cooperation policy, which firstly categorizes received data of secondary users into several groups, and then learns a common spectrum inside each group. The mean square performance of proposed algorithm is analyzed and supported by simulations. Experimental results show that, in a multitask cognitive network, the proposed algorithm can achieve a better mean square deviation (MSD) learning performance both in transient and steady-state regimes in comparison with other conventional algorithms.

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