Selective weight setting algorithm in cognitive radio network under resource limitation

The ever-increasing demand for higher data rates in wireless communications in the face of inadequate or underutilized spectral resources has motivated the introduction of cognitive radio. In cognitive radio network, unlicensed users continuously scan a vast range of frequencies to detect `white spaces' or `spectrum holes' that are temporarily and spatially not being used by licensed user, process of which is commonly known as spectrum sensing. In order to implement the cooperative spectrum sensing among Cognitive Radio (CR) users, data fusion schemes are superior to that of decision fusion ones in terms of detection performance but suffer from the drawback of huge traffic overhead when restriction of bandwidth of communication channels and energy consumption of the radio network comes into consideration. In this paper, a combination of data and decision fusion approach is implemented to mutually adventure the advantages of both and a selective weight setting algorithm is proposed by utilizing normal and modified deflection coefficients maximization under Neyman-Pearson criterion in order to obtain a final decision. Also, cluster-based Cooperative Spectrum Sensing (CSS) has been proposed in cognitive radio network to improve the energy efficiency. The simulations show promising results as the hybridization process visibly reduces the network traffic overhead while attaining a highly reasonable detection performance.