Study on soft combination for spectrum sensing in cognitive vehicular ad hoc networks
Tianyu Yuan, Ying Wang, Zhonyu Miao, Zixuan Fei · 2017
The increasing demand of vehicular network oriented applications (both safety and non-safety related services) would undoubtedly lead to shortage of spectral resource which is a challenge for vehicle-to-vehicle (V2V) communication networks. As a promising solution to meet the increasing demand of spectrum resource, cognitive radio (CR) technology has attracted much attention. Compared with general communication environment, vehicle environment has its own features such as movement of second users (SU) that can be negative to the performance of spectrum sensing. In this paper, we introduce a cooperative spectrum sensing (CSS) scheme in vehicle environment where we reduce the spatial correlation between samples and improve detection probability in region with low signal-to-noise (SNR) and a soft combination rule is proposed where different ways are used to aggregate samples. Simulation results show that our scheme can significantly increase the detection probability in low SNR region with a slight loss in region with high SNR.