Predicting Spectrum Occupancies Using a Non-Stationary Hidden Markov Model

Xianfu Chen, H Zhang, A B MacKenzie, Marja Matinmikko

    Research output: Contribution to journalArticleScientificpeer-review

    31 Citations (Scopus)


    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.
    Original languageEnglish
    Pages (from-to)333-336
    Number of pages3
    JournalIEEE Wireless Communications Letters
    Issue number4
    Publication statusPublished - 2014
    MoE publication typeA1 Journal article-refereed


    • Cognitive radio
    • spectrum measurement
    • non-stationary hidden Markov model
    • Bayes' rule
    • spectrum occupancy
    • spectrum prediction


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