Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks
AbstractAn explosive growth in vehicular wireless applications gives rise to spectrum resource starvation. Cognitive radio has been used in vehicular networks to mitigate the impending spectrum starvation problem by allowing vehicles to fully exploit spectrum opportunities unoccupied by licensed users. Efficient and effective detection of licensed user is a critical issue to realize cognitive radio applications. However, spectrum sensing in vehicular environments is a very challenging task due to vehicle mobility. For instance, vehicle mobility has a large effect on the wireless channel, thereby impacting the detection performance of spectrum sensing. Thus, gargantuan efforts have been made in order to analyze the fading properties of mobile radio channel in vehicular environments. Indeed, numerous studies have demonstrated that the wireless channel in vehicular environments can be characterized by a temporally correlated Rayleigh fading. In this paper, we focus on energy detection for spectrum sensing and a counting rule for cooperative sensing based on Neyman-Pearson criteria. Further, we go into the effect of the sensing and reporting channel conditions on the sensing performance under the temporally correlated Rayleigh channel. For local and cooperative sensing, we derive some alternative expressions for the average probability of misdetection. The pertinent numerical and simulating results are provided to further validate our theoretical analyses under a variety of scenarios. View Full-Text
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Qian, X.; Hao, L.; Ni, D.; Tran, Q.T. Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks. Sensors 2018, 18, 475.
Qian X, Hao L, Ni D, Tran QT. Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks. Sensors. 2018; 18(2):475.Chicago/Turabian Style
Qian, Xiaomin; Hao, Li; Ni, Dadong; Tran, Quang T. 2018. "Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks." Sensors 18, no. 2: 475.
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