Predicting Spectrum Occupancies Using a Non-Stationary Hidden Markov Model

One of the critical challenges for secondary use of licensed spectrum is the accurate modeling of primary users´ (PUs´) stochastic behavior. However, the conventional hidden Markov models (HMMs) assume stationary state transition probability and fail to adequately describe PUs´ dwell time distributions. In this letter, we propose a non-stationary hidden Markov model (NS-HMM), in which the time-varying property of PU behavior is realized. A variant of the Baum-Welch algorithm is developed to estimate the parameters of an NS-HMM. Finally, the performance of the proposed model is evaluated through experiments using real spectrum measurement data. The results show that the NS-HMM outperforms existing HMM-based approaches.