An HMM-based spectrum occupancy predictor for energy efficient cognitive radio
Affiliation
University of BedfordshireIssue Date
2013-09Subjects
autoregressive processescognitive radio
Hidden Markov models
Markov processes
predictive models
sensors
channel occupancy prediction
cognitive radio
energy efficiency
spectrum sensing
Metadata
Show full item recordAbstract
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.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.601-605Publisher
IEEEType
Conference papers, meetings and proceedingsLanguage
enISSN
2166-9570ae974a485f413a2113503eed53cd6c53
10.1109/PIMRC.2013.6666207