4.33
Hdl Handle:
http://hdl.handle.net/10547/333766
Title:
An HMM-based spectrum occupancy predictor for energy efficient cognitive radio
Authors:
Chatziantoniou, Eleftherios; Allen, Ben; Velisavljević, Vladan ( 0000-0001-9980-9368 )
Abstract:
Spectrum sensing is the cornerstone of cognitive radio technology and refers to the process of obtaining awareness of the radio spectrum usage in order to detect the presence of other users. Spectrum sensing algorithms consume considerable energy and time. Prediction methods for inferring the channel occupancy of future time instants have been proposed as a means of improving performance in terms of energy and time consumption. This paper studies the performance of a hidden Markov model (HMM) spectrum occupancy predictor as well as the improvement in sensing energy and time consumption based on real occupancy data obtained in the 2.4GHz ISM band. Experimental results show that the HMM-based occupancy predictor outperforms a kth order Markov and a 1-nearest neighbour (1NN) predictor. Our study also suggests that by employing such a predictive scheme in spectrum sensing, an improvement of up to 66% can be achieved in the required sensing energy and time.
Affiliation:
University of Bedfordshire
Citation:
Chatziantoniou, E., Allen, B., Velisavljevic, V. (2013) 'An HMM-based spectrum occupancy predictor for energy efficient cognitive radio' Personal Indoor and Mobile Radio Communications (PIMRC), IEEE 24th International Symposium on pp.601-605
Publisher:
IEEE
Issue Date:
Sep-2013
URI:
http://hdl.handle.net/10547/333766
DOI:
10.1109/PIMRC.2013.6666207
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6666207
Type:
Conference papers, meetings and proceedings
Language:
en
ISSN:
2166-9570
Appears in Collections:
Centre for Wireless Research (CWR)

Full metadata record

DC FieldValue Language
dc.contributor.authorChatziantoniou, Eleftheriosen
dc.contributor.authorAllen, Benen
dc.contributor.authorVelisavljević, Vladanen
dc.date.accessioned2014-11-06T09:42:37Z-
dc.date.available2014-11-06T09:42:37Z-
dc.date.issued2013-09-
dc.identifier.citationChatziantoniou, E., Allen, B., Velisavljevic, V. (2013) 'An HMM-based spectrum occupancy predictor for energy efficient cognitive radio' Personal Indoor and Mobile Radio Communications (PIMRC), IEEE 24th International Symposium on pp.601-605en
dc.identifier.issn2166-9570-
dc.identifier.doi10.1109/PIMRC.2013.6666207-
dc.identifier.urihttp://hdl.handle.net/10547/333766-
dc.description.abstractSpectrum sensing is the cornerstone of cognitive radio technology and refers to the process of obtaining awareness of the radio spectrum usage in order to detect the presence of other users. Spectrum sensing algorithms consume considerable energy and time. Prediction methods for inferring the channel occupancy of future time instants have been proposed as a means of improving performance in terms of energy and time consumption. This paper studies the performance of a hidden Markov model (HMM) spectrum occupancy predictor as well as the improvement in sensing energy and time consumption based on real occupancy data obtained in the 2.4GHz ISM band. Experimental results show that the HMM-based occupancy predictor outperforms a kth order Markov and a 1-nearest neighbour (1NN) predictor. Our study also suggests that by employing such a predictive scheme in spectrum sensing, an improvement of up to 66% can be achieved in the required sensing energy and time.en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6666207en
dc.subjectautoregressive processesen
dc.subjectcognitive radioen
dc.subjectHidden Markov modelsen
dc.subjectMarkov processesen
dc.subjectpredictive modelsen
dc.subjectsensorsen
dc.subjectchannel occupancy predictionen
dc.subjectcognitive radioen
dc.subjectenergy efficiencyen
dc.subjectspectrum sensingen
dc.titleAn HMM-based spectrum occupancy predictor for energy efficient cognitive radioen
dc.typeConference papers, meetings and proceedingsen
dc.contributor.departmentUniversity of Bedfordshireen
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