Quick Parallel Cooperative Spectrum Sensing in Cognitive Wireless Sensor Networks

In order to meet the increasing frequency demand of sensors and their related applications, cognitive wireless sensor networks (CWSNs) have emerged, which enable multiple sensors to detect the primary user (PU) signal through cooperative spectrum sensing (CSS) framework, access the channels that are not used by the PU, and improve the detection accuracy of the PU signal. However, traditional serial CSS needs more reporting time for sensors to submit their sensing samples to achieve collaborative gain, which undoubtedly reduces the efficiency of CSS and the throughput of CWSNs. Therefore, we propose a quick parallel CSS in this letter. First, we propose a parallel CSS model in a centralized CWSN, which enhances the performance of local spectrum sensing for sensors. Subsequently, we utilize the advantages of sequential approach (SA) to check the consistency of sensing samples through the data delivery state at the data transmission stage, and design a weight of likelihood ratio for sensing samples, implementing a weighted sequential approach (WSA). Finally, simulation results show that in contrast to classic SA, the proposed WSA requires less sensing time to achieve better CSS performance and achievable throughput of CWSNs.