Research On Spectrum Sensing Strategy In Cognitive Radio

With the fleeting progress of wireless communication technology, the next gen-eration networks are moving towards diversification, heterogeneity and intelligentiza-tion. However, the traditional operation in spectrum management, which conduces to extreme underutilization, will inevitably waste the network resource and cannot meet people’s explosive growth demand for communication. Thus, cognitive radio technol-ogy is naturally pushed onto the forefront of academia and industry’s research.Spectrum is a very valuable resource in wireless communication systems, and it has been a focal point for research and development efforts over the last several decades. Cognitive radio, which is one of the efforts to utilize the available spectrum more efficiently through opportunistic spectrum usage, has become an exciting and promising concept. One of the important elements of cognitive radio is sensing the available spectrum opportunities. This thesis investigates the spectrum sensing issue in cognitive radio networks from different aspects. The results of proposed sensing strategies and policies obviously improve the performance of spectrum sensing and increase the spectrum utilization of secondary user. In this thesis, we not only study the single node spectrum sensing, but also research the cooperative spectrum sensing. The benefits of our achievement are guaranteed by both the theorem-proof format and simulated results.Based on "listen-before-talk"(LBT) policy, Chapter2proposes two novel in-terference control strategies for the current new system metrics which regards the exis-tence of "grey" spectrum space. The dynamical IC strategy1has simpler infrastruc-ture but lower spectrum utilization when compared with IC strategy2. For the tradi-tional system metrics which considers the "spectrum space" only has binary status, an IA strategy with dynamical sensing slot is designed to reduce the averaged overhead exhausted by spectrum sensing. The proposed strategies can improve the efficiency of spectrum utilization while being robust with respect to the uncertainty in the primary user traffic pattern. Furthermore, the IC strategies and IA strategy are programmed by closed-form mathematical expressions with related sensing restrictions. By theoretical study, we show the feasible approaches to obtain the optimal strategy configuration to achieve the maximum spectrum utilization. The simulated results confirm the good match between theory and simulation and show that the proposed strategies can obvi-ously improve spectrum utilization.In Chapter3, the cooperative spectrum sensing (CSS) with test-statistic-fusion- rule is studied thoroughly. For decision fusion rule (DFR) based CSS, the conventional CSS schemes demand for knowing the number of cooperative users and require that the sensing threshold of detector in cooperative users can be adjusted according to the number of cooperative users. Hence, these schemes need to confirm that the adjustment information could be down-link delivered to each cooperative user. Additionally, when regarding the practical CR system is dynamic, the number of cooperative users, to which the synchronized information is generated according, may mismatch the actual number of reporting cooperative user. In this chapter, we consider each cooperative user only spends "1bit" on reporting its own sensing decision to data fusion center as the total cooperation overhead. In order to gain better sensing outcomes with certain objective, we derive theorems to reveal the optimum threshold of general DFR. Then we propose three novel DFRs and related three algorithms to efficiently obtain the optimum decision threshold for different objectives. By simulations, the proposed DFRs indicate evident improvement on CSS performance.For multi-bit fusion rule based CSS, there have not been feasible approaches that can analytically calculate the performance of CSS. In this chapter, we not only show the cooperative false alarm probability and cooperative detection probability impacted by quantization, but also formulate them by two closed form expressions. These two expressions enable the calculation of cooperative false alarm probability and cooper-ative detection probability can be tractable efficiently. Additionally, to facilitate this calculation, we derive Normal approximation for evaluating the sensing performance conveniently. Furthermore, two optimization approaches are proposed to achieve the high sensing performance under quantization.Finally in Chapter4, we show a new issue in spectrum sensing, which is termed as spatial false alarm (SFA) issue and has not been published in other literatures. In cognitive radio, SU is permitted to access the primary channel when the presence of secondary user is not detected inside the secondary sensing range. However, the pres-ence of secondary user outside the sensing range still can be detected by SU, causing the SU misinterprets a secondary user is harmfully interfered and hereby loses medi-um access opportunity in the coexisting primary channel. According to our study, the medium access opportunity of SU is influenced by this issue strictly but it is neglected in current sensing strategies and medium access control schemes. Hence, such neglect conduces to inefficient spectrum utilization at practice. The theoretical reason for the existence of SFA is studied thoroughly to reveal how to reduce the impact of SFA on SU’s access opportunity. Furthermore, with regard to spectrum sensing, a closed-form expression is derived to show the practical medium access probability in CR, so that SU could accurately evaluate the medium access probability. The simulated results not only verify the importance of SFA issue but also show the excellent match to the theoretical results.