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...
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| Veröffentlicht in: | IEEE wireless communications letters 2014-08, Vol.3 (4), p.333-336 |
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| container_title | IEEE wireless communications letters |
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| creator | Xianfu Chen Honggang Zhang MacKenzie, Allen B. Matinmikko, Marja |
| description | 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. |
| doi_str_mv | 10.1109/LWC.2014.2315040 |
| format | Article |
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(IEEE) 2014</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c325t-607ac8242eb3216a731ea60a01adc12e9d6084a240b19fd2ddcc188881a22a9c3</citedby><cites>FETCH-LOGICAL-c325t-607ac8242eb3216a731ea60a01adc12e9d6084a240b19fd2ddcc188881a22a9c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6782490$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,776,780,792,881,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6782490$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://centralesupelec.hal.science/hal-01073326$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Xianfu Chen</creatorcontrib><creatorcontrib>Honggang Zhang</creatorcontrib><creatorcontrib>MacKenzie, Allen B.</creatorcontrib><creatorcontrib>Matinmikko, Marja</creatorcontrib><title>Predicting Spectrum Occupancies Using a Non-Stationary Hidden Markov Model</title><title>IEEE wireless communications letters</title><addtitle>LWC</addtitle><description>One of the critical challenges for secondary use of licensed spectrum is the accurate modeling of primary users' (PUs') stochastic behavior. 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The results show that the NS-HMM outperforms existing HMM-based approaches.</description><subject>Channel estimation</subject><subject>Computer Science</subject><subject>Electronic mail</subject><subject>Engineering Sciences</subject><subject>Hidden Markov models</subject><subject>Markov processes</subject><subject>Parameter estimation</subject><subject>Prediction algorithms</subject><subject>Signal and Image processing</subject><issn>2162-2337</issn><issn>2162-2345</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE1LAzEQhoMoWLR3wcuCJw9bZ5L9PJaiVtlaoRaPIU1STW03Ndkt-O_NsqVzmWHmmeGdl5AbhBEilA_V52REAZMRZZhCAmdkQDGjMWVJen6qWX5Jht5vIEQGSLEYkNd3p5WRjam_osVey8a1u2guZbsXtTTaR0vfjUT0Zut40YjG2Fq4v2hqlNJ1NBPuxx6imVV6e00u1mLr9fCYr8jy6fFjMo2r-fPLZFzFktG0iTPIhSxoQvWKBWEiZ6hFBgJQKIlUlyqDIhE0gRWWa0WVkhKLECgoFaVkV-S-v_sttnzvzC7o4VYYPh1XvOsBQs4YzQ4Y2Lue3Tv722rf8I1tXR3kcczTMgVgaREo6CnprPdOr09nEXhnMA8G885gfjQ4rNz2K0ZrfcKzPDxWAvsHwb10Jw</recordid><startdate>20140801</startdate><enddate>20140801</enddate><creator>Xianfu Chen</creator><creator>Honggang Zhang</creator><creator>MacKenzie, Allen B.</creator><creator>Matinmikko, Marja</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Channel estimation Computer Science Electronic mail Engineering Sciences Hidden Markov models Markov processes Parameter estimation Prediction algorithms Signal and Image processing |
| title | Predicting Spectrum Occupancies Using a Non-Stationary Hidden Markov Model |
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