Cooperative spectrum estimation over large‐scale cognitive radio networks

Abstract

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.

Transient performance analysis of adaptive multitask network based on correntropy criterion

“…(17) Considering that e k (i) = d k (i) − u k,i w, the optimal solution of Equation 16 can be derived as follows: (17) Considering that e k (i) = d k (i) − u k,i w, the optimal solution of Equation 16 can be derived as follows:…”

“…. (17) Considering that e k (i) = d k (i) − u k,i w, the optimal solution of Equation 16 can be derived as follows:…”

“…Other significant works conducting multitask problems are introduced in previous works. [11][12][13][14][15][16][17][18] The clustering method proposed in the work of Zhao and Sayed 12 and Chen et al 13 is based on solving a hypothesis test for determining the new neighborhood. Another method to exploit similarities between tasks is to solve optimization problems with the regularization technique.…”

“…In our previous works, 17,18 the multitask learning over adaptive networks is derived based on maximum correntropy criterion (MCC). [19][20][21][22][23][24][25][26][27][28][29][30][31] The proposed learning algorithm determines a novel cooperation policy for sensors, in which they can collaborate with neighbors that have similar task and they can reject the collaboration of neighbors that have distinct tasks.…”

“…[19][20][21][22][23][24][25][26][27][28][29][30][31] The proposed learning algorithm determines a novel cooperation policy for sensors, in which they can collaborate with neighbors that have similar task and they can reject the collaboration of neighbors that have distinct tasks. In this paper, we want to analyze the transient performance of our proposed learning algorithm 17,18 and compare the theoretical results with simulation results. This paper is organized as follows.…”