# Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks  
[PERSON]  
1 Key Lab of Information Coding and Transmission, Southwest Jiaotong University, Chengdu 610031, China; [EMAIL_ADDRESS] (X.Q. ); [EMAIL_ADDRESS] (D.N. ); [EMAIL_ADDRESS] (Q.T.T.) 1 Key Lab of Information Coding and Transmission, Southwest Jiaotong University, Chengdu 610031, China; [EMAIL_ADDRESS] (X.Q. ); [EMAIL_ADDRESS] (D.N. ); [EMAIL_ADDRESS] (Q.  
deficiency. In view of the stringent QoS requirements on DSRC spectrum, it is not sufficient for all applications to depend only on the 5.9-GHz DSRC spectrum. There is a dramatic increase in the demand for frequency resources to satisfy their communication requirements. To solve the aforementioned problems, cognitive radio (CR) [1; 2] has increasingly been presented as a potential technique being capable of accessing licensed but unoccupied frequency bands only without causing any unacceptable interference to licensed users (or primary users, PUs). Cognitive radio has become one of the most breathtaking technologies to improve the spectrum efficiency effectively for the past couple of decades. In addition to a better remedy for frequency scarcity issue, cognitive radio is appropriate for vehicular environments, since their unique characteristics make it much better to achieve the spatial and temporal reusage of the empty frequency bands of PUs compared to other traditional cognitive networks [3; 4]. To efficiently reutilize the spectrum holes with minimum interference to PUs, the CR-enabled vehicles, which can be called secondary vehicular users (SVUs), need to reliably make a decision inferring the presence or absence of the PU. Consequently, spectrum sensing constitutes the key component of cognitive vehicular networks (CVNs) [5]. Spectrum sensing performed in temporal domain [6] and spatial domain [7; 8] is a wealthily investigated subject. Among them, cooperative sensing [9] has already attracted strong research interest as it is such an effective way to improve sensing accuracy and efficiency by exploiting multi-user cooperative spatial diversity. However, these existing sensing techniques are largely concentrated in the traditional CR networks. They assume that all users just stand in one place. As a result, they cannot be directly applicable to vehicular networks. In CVNs, we must give an account of the idiosyncrasies of vehicular networks such as high mobility while designing sensing schemes [3; 10]. For instance, rapid movement of vehicles makes the availability of spectrum holes dynamically change since a vehicle may enter or leave a region interfered by a particular PU at different locations along the road. In this regard, it is considerable for SVUs to detect PU activities in the fastest possible way. Additionally, the vehicles' motion are restricted and predictable due to the fixed road topology. In consequence, each vehicle may be glad to know in advance the spectrum opportunities to better utilize them for transmission on its track. Further, high speeds and the environmental clutter can affect the received signal due to the Doppler effect, fading and shadowing. These factors will have immediate impacts on spectrum sensing of CVNs. Spectrum sensing and sharing in dynamic environments have been researched in some preliminary works [4; 11; 12; 13; 14; 15; 16; 17; 18]. The authors in [4] proposed a novel adaptive sensing coordinated mechanism, in which the central nodes merely assist and coordinate the SVUs to better acquire the availability of spectrum holes instead of completely controlling the sensing and access. The authors in [11] studied the detection performance of spectrum sensing under the shadowing and multi-path composite fading channel in vehicular environments. The authors in [12] considered a cognitive inter-vehicular cooperative network where all channels are modelled by the double Rayleigh fading distribution.