NeTS: Small: Networking over Random Fields: A Statistical Model for Cognitive Radio Networks

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Experiments have demonstrated the temporal and spatial correlations of spectrum availability, which are of key importance in the design and analysis of cognitive radio networks. Motivated by the observation, this research applies the theory of random fields, which describes the behavior of multiple correlated random variables, to model the spectrum availabilities in time and space domains. For global spectrum activity, a homogeneous random field like Ising model is used to model the spatial correlation and analyze the performance. For local spectrum activities, Bayesian networks are used to describe the causality in spectrum and statistically infer the future spectrum situations. Furthermore, the model of controlled random fields is employed to design the networking protocols in cognitive radio networks. A low-cost spectrum sensor is designed to collect the real spectrum measurement in multiple locations simultaneously. The research promotes the understanding of frequency spectrum activities and enhances the design and analysis of the next generation cognitive radio networks. The research involves aspects of wireless communications, networking, artificial intelligence and imaging processing; thus the inter-disciplinary essence of the research also lends itself to cross-disciplinary education. Novel courses will be devised, which involve the topics of cognitive radio networks, machine learning and image processing. This project also expects to attract traditionally underrepresented groups, as well as outreach high school students.