An HMM-based spectrum occupancy predictor for energy efficient cognitive radio

Eleftherios Chatziantoniou, Ben Allen, Vladan Velisavljevic

Research output: Contribution to conferencePaper

23 Citations (Scopus)

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.
Original languageEnglish
DOIs
Publication statusPublished - 2013
EventIEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) - London, United Kingdom
Duration: 08 Sep 201311 Sep 2013

Conference

ConferenceIEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)
CountryUnited Kingdom
CityLondon
Period08/09/201311/09/2013

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