Enhancement of Spectrum Decision through Probabilistic Graphical Models

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The goal of this research is increase the consumer's quality of service in environments where more and more services are competing for use of the radio spectrum. It is widely believed that future regulation of the spectrum will change from the current static environment, where opening new channels for communications takes years, to highly dynamic environment where smart radios will jump from frequency channel to frequency channel. In this dynamic environment, the consumer will share the spectrum with many different types of devices, including radars, other phones, Wi-Fi servers and a host of new smart devices. The new smart devices will constantly change their frequencies, data rates and modulation schemes, thereby creating a dynamic, more uncertain, environment. The scientists will explore how uncertainty influences the characterization of radio spectrum usage. The focus is on the sensing and decision-making aspects of the problem rather than management issues. The scientists will apply their experience with Artificial Intelligence and transfer techniques of dynamic problem solving from other domains. The proposed study has the potential to develop decision support models that will inform new policies for spectrum management in the future. This research project's primary aim is to advance the knowledge and understanding of wireless communication scenarios to enrich the spectrum decision process in cognitive radio by evaluating the impact of uncertainty on the different cognitive cycle changes of adaptive radios. To achieve this goal, they propose to identify, classify, and characterize the random and deterministic variables present in a typical wireless scenario and model their causal relations, using probabilistic graphical models, such as Bayesian networks and influence diagrams.