Title :
Spectrum Sensing in Cognitive Radio Using a Markov-Chain Monte-Carlo Scheme
Author :
Wang, Xiao Yu ; Wong, Alexander ; Ho, Pin-Han
Author_Institution :
Univ. of Waterloo, Waterloo, ON, Canada
fDate :
9/1/2010 12:00:00 AM
Abstract :
In this letter, a novel stochastic strategy to spectrum sensing is investigated for the purpose of improving spectrum sensing efficiency of cognitive radio (CR) systems. The problem of selecting the optimal sequence of channels to finely sensing is formulated as an optimization problem to maximize the probability of obtaining available channels, and is then subsequently solved by using a Markov-Chain Monte-Carlo (MCMC) scheme. By employing a nonparametric approach such as the MCMC scheme, the reliance on specific traffic models is alleviated. Experimental results show that the proposed algorithm has the potential to achieve noticeably improved performance in terms of overhead and percentage of missed spectrum opportunities, thus making it well suited for use in CR networks.
Keywords :
Markov processes; Monte Carlo methods; channel allocation; cognitive radio; optimisation; spectral analysis; telecommunication traffic; CR systems; Markov chain Monte Carlo scheme; channel selection; cognitive radio; network traffic; optimization problem; spectrum sensing; Ad hoc networks; Availability; Chromium; Cognitive radio; Degradation; Markov processes; Probability density function; Proposals; Sensors; Signal to noise ratio; Statistics; Stochastic processes; Stochastic systems; Telecommunication traffic; Traffic control; Spectrum sensing; cognitive radio;
Journal_Title :
Communications Letters, IEEE
DOI :
10.1109/LCOMM.2010.080210.100569
