Adaptive cooperative spectrum sensing based on a novel robust detection algorithm

The optimal data fusion rule for multiple sensor detection systems based on the Bayesian criterion has been derived by Chair and Varshney in 1986. However, most of the following works are focused on how to implement such a fusion rule, since the probability of false alarm and the probability of miss detection are hard to evaluate in practice. Till now, although more and more satisfactory data-fusion implementation schemes are available, most of the cooperative spectrum sensing techniques are based on the simple energy-detection algorithm, which only relies on the energy of the received signal. However, when noise is relatively large or the time-varying characteristics of the signal are conspicuous, the energy-detection spectrum sensing algorithm is more prone to fail. Thus, in this paper, we propose a new adaptive cooperative spectrum sensing scheme, which is based on a novel detection algorithm involving JB (Jarque-Bera) statistic. The ROC (receiver-operating characteristic) curves show that our new cooperative spectrum sensing scheme is more robust than that based on the energy-detection spectrum sensing algorithm. Besides, the performance comparison also implies that the optimal data-fusion rule in our new cooperative spectrum sensing scheme is superior to the commonly adopted “OR” and “AND” rules in the existing literature.