One of the critical challenges for secondary use of licensed spectrum is the accurate modeling of primary users' (PUs') stochastic behaviors. 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 PUs' behaviors is realized. A variant of the Baum-Welch algorithm is developed to estimate the parameters of a 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.
- Cognitive radio
- spectrum measurement
- non-stationary hidden Markov model
- Bayes' rule
- spectrum occupancy
- spectrum prediction
Chen, X., Zhang, H., MacKenzie, A. B., & Matinmikko, M. (2014). Predicting Spectrum Occupancies Using a Non-Stationary Hidden Markov Model. IEEE Wireless Communications Letters, 3(4), 333-336. https://doi.org/10.1109/LWC.2014.2315040