Abstract
Spectrum starvation is a key challenge for wireless services and applications in vehicular networks with serious negative implications for traffic safety and transportation efficiency. As a promising solution, cognitive radio (CR) allows CR-enabled vehicles to access the spectrum holes of primary users (PUs) in an opportunistic manner, but requires a robust sensing mechanism to provide adequate protection for PUs. However, highly dynamic environments make spectrum sensing an increasingly difficult problem. For example, vehicular communications are subject to multipath fading due to vehicle mobility. In this paper, the wireless channels in vehicular environments are considered to be subject to time-correlated Rayleigh fading. To facilitate the analysis, temporal correlation could be classified into complete correlation, partial correlation and complete independence according to the degree of correlation. The performance of cooperative sensing scheme is investigated for soft fusion (SF) and hard fusion (HF) approaches. The simulation results are presented to verify our theoretical analysis for varying conditions and scenarios. The results indicate that the detecting performance could be importantly influenced by channel correlation, which can be improved by vehicles’ cooperation.
Highlights
The concept of vehicular communication networking has fascinated researchers working in various fields because it has the ability and the potential to enhance vehicular safety and transportation efficiency
Throughout this paper on the performance, we will declare the local probabilities of false alarm, miss detection, and detection as Pf, Pm, and Pd, respectively, whereas their global probabilities at the FC will be represented by PF, PM, and PD for soft fusion and Q F, Q M, and Q D for hard fusion
This paper contains a theoretical and simulated discussion of the performance of spectrum sensing in time-correlated Rayleigh vehicular environments
Summary
The concept of vehicular communication networking has fascinated researchers working in various fields because it has the ability and the potential to enhance vehicular safety and transportation efficiency. Spectrum deficiency has become so strong pressure and a tremendous challenge in vehicular networks. In light of the challenge, cognitive radio (CR) [2] incorporated into vehicular networks has been proposed to alleviate the pressure from the increasing demand for the radio spectrum. We call such a vehicular networks with CR capability as a cognitive vehicular network (CVN), as well as vehicles with CR capability as secondary vehicular users (SVUs) [3]. It follows that spectrum sensing is a key design issue of CVNs
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