An Adaptive Cooperative Spectrum Sensing Scheme Based on the Optimal Data Fusion Rule

Spectrum sensing is a key technology in cognitive radio (CR). It provides the CR system with available vacant channels list by sensing the spectrum environment over a wide frequency band. However, local sensing by single node will be compromised if the sensing node experiences shadowing or fading effect. Cooperative spectrum sensing is derived to solve this problem. In this paper, we propose an adaptive cooperative spectrum sensing scheme by using the optimal data fusion rule. To implement such a rule, the probabilities of detection and false alarm of each sensing node must be known, but these probabilities are not readily available in practice. So we develop an iterative algorithm to estimate the probabilities of detection and false alarm. Two different methods are also developed to enhance the adaptability of the proposed estimating algorithm in time-varying environment. Simulation results show that the proposed scheme can approach the ideal optimal performance but do not need any prior information of the primary users´ (PU) signal.