An Improved Clustering Cooperative Spectrum Sensing Algorithm Based on Modified Double-Threshold Energy Detection and Its Optimization in Cognitive Wireless Sensor Networks

Cooperative spectrum sensing (CSS) is a very important technique in cognitive wireless sensor networks, but the channel and multipath affect the sensing performance. For improving the sensing performance, this paper incorporates a modified double-threshold energy detection (MDTED) and the location and channel information to improve the clustering cooperative spectrum sensing (CCSS) algorithm. Within each cluster, the cognitive node with the best channel quality to the fusion center (FC) is chosen as the cluster head (CH), and each node uses the MDTED. The detective information is sent to CH, and CH makes the decision of the cluster. The decision information is sent to FC by each CH, and FC uses the “or” rule to fuse all clusters’ decision information and makes a final decision. Since MDTED needs to transfer large traffic and occupy channel widely, this paper further optimizes the improved algorithm. Ensuring the detection performance, the cognitive nodes participating in the sensing are properly reduced. Simulation results show that the detecting accuracy of the improved algorithm is higher than conventional CSS, and the improved algorithm can also significantly improve collaborative sensing ability. For the optimization of cognitive nodes’ number, the detection probability of the network can be obviously increased.

Abstract licence: CC BY

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