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The majority of existing spectrum prediction models in Cognitive Radio Networks (CRNs) don’t fully explore the hidden correlation among adjacent observations. In this paper, we first develop a novel prediction approach termed high-order hidden bivariate Markov model (H2BMM) for a stationary CRN. T
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Zhao et al. (2017) studied this question.
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