Cooperative spectrum estimation over large‐scale cognitive radio networks
Mojtaba Hajiabadi, Hossein Khoshbin, Ghosheh Abed Hodtani · IET Signal Processing · 2017
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 cannot share a common spectrum since the coverage area of primary users is limited. In this study, the authors suggest a diffusion adaptive learning algorithm based on correntropy cooperation policy, which first categorises 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 analysed and supported by simulations. Experimental results show that, in a multitask cognitive network, the proposed algorithm can achieve a better mean‐square deviation learning performance both in transient and steady‐state regimes in comparison with other conventional algorithms.