A Sequential Sensing Data Transmission and Fusion Approach for Large Scale Cognitive Radios

Cognitive radios are efficient techniques to improve the utilization of the spectrum. Spectrum sensing is the key functionality to improve the spectrum efficiency and avoid harmful interference to the licensed users. By exploiting the spatial diversity of the different secondary users, cooperative sensing can achieve better sensing performance compared to the local sensing, thereby, receiving much attention recently. However, in large scale cognitive radio networks, such as Wireless Regional Area Network (WRAN) defined by IEEE 802.22, the secondary users may have quite different sensing SNRs due to the different pass loss and other environment parameters. How to efficiently transmit and fuse the sensing data of cooperative sensing in large scale cognitive radios have not been fully studied yet. This paper presents a sequential sensing data transmission and fusion approach for large scale cognitive radios to minimize the average sensing time by dividing the users into different sets according to their SNR parameters. The analytical and simulation results both show that the proposed approach not only decreases the sensing time but also certifies the accuracy of detection.