Research On Spectrum Sensing In Cognitive Vehicular Networks With Dense Traffic

The rapid development of Intelligent Transportation System has put forward higher demands on security services and entertainment services,making vehicular networks facing explosive communication requirements and severe spectrum scarcity. Cognitive Radio (CR) enables Secondary Users (SU) to dynamically access the licensed spectrum based on the guaranteed communication of Primary Users (PU), thus making full use of the spectrum resource and alleviating the spectrum shortage problem.Spectrum sensing is a crucial technique of CR. SUs determine the presence or absence of PUs according to the sensing results, which may significantly affect the performance of cognitive radio networks.Therefore, the spectrum sensing technology in CR has caused widespread concern. Nowadays, traffic flow increases year by year, dense traffic has become a major issue, therefore, it is necessary to research the spectrum sensing in Cognitive Vehicular Networks (CVN) with dense traffic.Based on the National Science Funding project: “Sensing and utilization of polarization resource in dense heterogeneous wireless environment"(Project No. 61571062) and a joint project of Beijing Municipal Commission of Education and central universities"Application and Development Technologies of Bus Internet",the paper studies the spectrum sensing performance in CVN with dense traffic and taken user mobility, user correlation and spatial-temporal spectrum opportunities into consideration. Based on the analysis results, a cooperative spectrum sensing (CSS) algorithm is proposed to alleviate the impact of user correlation on spectrum sensing and reduce system overhead. The main research work of this paper are as follows:1. Summarize and analyze the research background and key technologies of CVN and spectrum sensing. It is pointed out the under dense traffic scenario, user correlation, mobility model of vehicles,spatial-temporal spectrum opportunities and cooperative spectrum sensing algorithm are the key factors affecting the spectrum sensing performance.2. Aiming at the lack of research on the existing CVN under the dense traffic scenario, this paper develops the theoretical derivation and simulation analysis of the spectrum sensing performance of CVN with dense traffic.In this paper, the velocity distribution of vehicles in dense traffic is obtained by statistic analysis of vehicle trajectory data, and the vehicle mobility model under the dense traffic scene is established.Then,the correlation coefficient matrix is introduced to consider the probability of detection and the probability of false alarm in the mobile scene. The impacts of average velocity, traffic density, channel environment, PU activity and other parameters on the spectrum sensing performance are analyzed.The theoretical derivation and simulation results show that user correlation is an important factor that affects spectrum sensing performance underdense traffic scene.3. We proposed a user correlation and double threshold based cooperative spectrum sensing algorithm in CVN.Firstly, an improved double-threshold censoring method is proposed to reduce the system overhead. Then, a cooperative spectrum sensing algorithm based on user correlation is proposed to increase the spatial diversity gain of CSS and improve the performance of the algorithm.The theoretical analysis and simulation results show that the proposed algorithm can effectively reduce the overheads while on the basis of guaranteeing the CSS performance of CVN. In addition, the proposed algorithm is suitable for dense traffic environment since the gain introduced by the algorithm increases compared to traditional ones with the traffic density increasing.