Feature Selection Based Spectrum Decision Making For Cognitive Radio Networks
Abstract
The growing success of new wireless applications and services has led to overcrowded licensed bands. Cognitive radio networks have been proposed as a solution to both spectrum inefficiency and spectrum scarcity problems by improving the utilization of limited radio resources. Cognitive radio networks are intelligent networks that can automatically sense the environment and adapt the communication parameters accordingly. These types of networks have applications in dynamic spectrum access, co- existence of different wireless networks, interference management etc. A spectrum decision framework is proposed to determine a set of spectrum bands by considering the application requirements as well as the dynamic nature of the spectrum bands. The use of cyclic feature based methods for distributed signal detection and classification is discussed and recent results are presented. Moreover, a dynamic resource management scheme is developed to coordinate the spectrum decision adaptively dependent on the time- varying cognitive radio network capacity. Simulation results can be obtained by using NS2 which shows that the proposed method provide efficient bandwidth utilization while satisfying service requirements.
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