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

13 Citations (Scopus)

Abstract

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
Volume3
Issue number4
DOIs
Publication statusPublished - 2014
MoE publication typeA1 Journal article-refereed

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Hidden Markov models
Experiments

Keywords

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

Cite this

Chen, Xianfu ; Zhang, H ; MacKenzie, A B ; Matinmikko, Marja. / Predicting Spectrum Occupancies Using a Non-Stationary Hidden Markov Model. In: IEEE Wireless Communications Letters. 2014 ; Vol. 3, No. 4. pp. 333-336.
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Predicting Spectrum Occupancies Using a Non-Stationary Hidden Markov Model. / Chen, Xianfu; Zhang, H; MacKenzie, A B; Matinmikko, Marja.

In: IEEE Wireless Communications Letters, Vol. 3, No. 4, 2014, p. 333-336.

Research output: Contribution to journalArticleScientificpeer-review

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T1 - Predicting Spectrum Occupancies Using a Non-Stationary Hidden Markov Model

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AU - MacKenzie, A B

AU - Matinmikko, Marja

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PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

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