Research On Some Key Technologies Of Cognitive Radio Based On Support Vector Machine

The scarceness of wireless spectrum and the lack of flexibility of allocation methods are two major factors which restrict the development of current wireless communications. As a revolutionary smart spectrum sharing technology, Cognitive Radio (CR) can significantly improve the spectrum utilization and receives more and more interest within these years. It's very important to research on key technologies of cognitive radio for promoting the development and application of cognitive radio. There are many different types of the key technologies in cognition radio at the moment, as well as research methods, which are still not formed the unified fundamental research system. Cognitive radio technology emphasize the intelligence of communications equipment, which make it possible to research the key technologies of cognitive radio based on machine learning theoretical. As a machine learning method, Support Vector Machine (SVM) is very popular in recent years, has a relatively good performance.In this dissertation, some key technologies of cognitive radio are researched based on support vector machine. The research focused on spectrum sensing and dynamic spectrum access (DSA), the dissertation's main contents include:1. In Chapter 2, the key technology of cognitive radio, cognitive radio networks and cognitive networks are redefined, and a systematic summary and detailed information about cognitive radio technology are introduced, especially its key technology research, development and applications. By discussing and analyzing, the problems and further research direction of the key technology in cognitive radio are definable, which fully reflects the necessity and importance of the proposed subject that is research on some key technologies of cognitive radio based on support vector machine.2. In Chapter 4, a novel approach to signal classification is proposed for cognitive radio. Combining the spectral cyclostationary features, embed SVM into the framework of Hidden Markov Mode (HMM) to construct a hybrid HMM/SVM classifier for signal recognition. The simulation results show that the high performance and robustness of the proposed approach, even in low SNR. Compared to the conventional methods, the proposed approach is robust, has a rather higher recognition rate of signals.3. In Chapter 5, a dynamic spectrum access algorithm based on probability density estimation is proposed to estimate the probability density of spectrum idle duration with support vector machines and evaluates the channel states, and cognitive radio users access the channel according to the states. This practicable and flexible algorithm can be adjusted adaptively. Simulation shows that the proposed algorithm significantly reduces disruptions to primary users and improve the throughout as well as the quality of service of cognitive radio users.4. In Chapter 6, a very important and valuable sub-topic is proposed, namely research on fitting probability density of spectrum idle duration in wireless communication networks. And then modeling methods and steps are described in detail.In the simulation environment, the probability density of spectrum idle duration is estimated with conventional parameter estimation and support vector machines. Simulation results show that the proposed modeling methods are effective and robust.