PRIMARY USER BEHAVIOR ESTIMATION AND CHANNEL ASSIGNMENT FOR DYNAMIC SPECTRUM ACCESS IN ENERGY-CONSTRAINED COGNITIVE RADIO SENSOR NETWORKS

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy PRIMARY USER BEHAVIOR ESTIMATION AND CHANNEL ASSIGNMENT FOR DYNAMIC SPECTRUM ACCESS IN ENERGY-CONSTRAINED COGNITIVE RADIO SENSOR NETWORKS By Xiaoyuan Li August 2013 Chair: Janise Y. McNair Major: Electrical and Computer Engineering Cognitive radio technology improves spectrum utilization by allowing secondary users (SUs) to access the licensed spectrum bands in an opportunistic manner as long as it does not interfere with the activity of the primary users (PUs). This technology may also be used for wireless sensor networks (WSNs) to solve the problem of spectrum scarcity and bursty traffic. With the knowledge of PU behavior, sensors can transmit packets on the channels which are currently not occupied and vacate the bands by the detection of PU signals. In this dissertation, the spectrum sensing and spectrum access problems are investigated in a cognitive radio sensor network (CRSN), in which a cognitive radio is installed in each sensor and it can be tuned to any available channel. Modeling and estimating the PU behavior is critical to implement dynamic spectrum access. For perfect sensing without sensing errors, we investigate the estimation accuracy of the PU behavior based on the Markov model. The performance of Maximum Likelihood (ML) estimation is evaluated by its distribution. To meet the requirement of estimation accuracy while reducing the unnecessary sensing time, we propose a learning algorithm to dynamically estimate the required length of the sample sequence. For the imperfect sensing with sensing errors, a two-state HMM is employed to model PU behavior with imperfect sensing. Baum-Welch algorithm is used to estimate the

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