Hidden Markov model based spectrum sensing for cognitive radio




Nguyen, Thao Tran Nhu

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Cognitive radio is an emerging technology for sensing and opportunistic spectrum access in wireless communication networks. It allows a secondary user to detect under-utilized spectrum of a primary user and to dynamically access the spectrum without causing harmful interference to the primary user. A number of spectrum sensing techniques has been proposed in the literature to identify the state of the primary user in the temporal domain. However, most of these techniques make instantaneous decisions based on current measurement received at the cognitive radio, and they do not consider the transmission pattern of the primary user which can be acquired from past measurements. Thus, sensing performance can be improved by incorporating measurement history into the sensing decision. Moreover, using all available data may enable prediction of the primary user activity, which will allow a cognitive radio to better plan for its spectrum usage.



Engineering, Baum algorithm, Bivariate Markov chain, Cognitive radio, Hidden Markov model, Spectrum Sensing