Cooperative Spectrum Sensing Schemes Based On Fuzzy Integral And Optimization Theory In Cognitive Radio Networks

With the development of wireless communication technology, the need for bandwidth is increasing continuously, and the growing need makes wireless spectrum resources more and more scarce. Within the current spectrum regulatory framework, however, a large number of spectrum resources are often idle. The conflicts between spectrum scarcity and spectrum under-utilization become more obvious, and they are caused by the mismatch between static spectrum planning system and dynamic use of spectrum. In order to solve the conflicts between spectrum scarcity and spectrum under-utilization, cognitive radio (CR) technology was recently proposed. It can improve the spectrum utilization by allowing secondary networks (users) to borrow unused radio spectrum from primary licensed networks (users) or to share the spectrum with the primary networks (users).Thanks for New Century Excellent Talents in University (NCET) under Grant No and NCET-06-0091 and the National High-tech R&D Program of China (863 program) under Grant No.2009AA011802, these two programs support my research. The content is organized as follows.This paper introduces the background of the research topic and presents some general information of cognitive radio initially. It also gives out the definition and the function of the CR, as well as the key technologies and the current research situations at domestic and abroad in detail, Through detailed background information, the main research areas of this dissertation is determined. Chapter two describes the existing spectrum sensing cognitive radio technology. Spectrum sensing technology can be divided into four categories, and these four technologies are introduced in detail. According to the introduction, the conclusion can be made that:cooperation detection of the radio spectrum has become trend of cognitive radio development. Chapter three focuses on two of the cooperative spectrum sensing methods based on uncertainty integration strategy:they are the evidence theory and fuzzy matrix evaluation. With a detailed description of these two methods and their comparisons with traditional integration strategies, their advantages and disadvantages are summarized. Through chapter four, uncertainty is taken into account into the detection algorithms by the innovative way. The two spectrum sensing algorithms based on fuzzy integral and optimization theory are introduced in detail. Compared to AND, OR, Majority and evidence theory fusion strategies implementing in cooperative spectrum sensing scheme, the proposed algorithms has better performance. In the end, spectrum sensing in CR are summarized and the prospects for the research are speculated.