Distributed Cooperative Spectrum Sensing in Cognitive Radio Networks with Adaptive Detection Threshold

Abstract

This paper proposes a cooperative sensing algorithm based on distributed fusion strategy and maintained probability of false alarm for cognitive radio. It further introduces a reporting strategy that discusses how cooperative sensing in distributed manner can select among possible candidates in order to reduce bandwidth requirement. We adopted a dynamic distributed architecture for cooperative sensing based on the link quality and found condition on the channel quality for cooperation to be beneficial. Using probability of detection, and BER metrics we evaluated the performance improvement of distributed cooperation over direct cooperation and noncooperative sensing. We used analytical formulation with possible candidate selection criteria to investigate and maximize the cooperation gain. By employing such distribution and selection technique, the reporting error due to the fading channel is reduced. Results show that the method effectively improve performance of sensing, it increase the probability of detection up to 0.9 at <0.1 probability of false alarm. Sensitivity requirement is reduced with network scale and the number of nodes participate in decision fusion is reduced about 42% at probability of false alarm 0.1. ROC curve has obvious improvement compared with existing methods.