@article {219,
	title = {Extended knowledge-based reasoning approach to spectrum sensing for cognitive radio},
	journal = {IEEE Transactions on Mobile Computing},
	volume = {9},
	year = {2010},
	pages = {465 - 478},
	abstract = {<p>In this paper, a novel scheme for cognitive radio (CR) spectrum sensing in medium access control (MAC) layer, called as extended knowledge-based reasoning (EKBR), is proposed. The target of EKBR is to improve the fine sensing efficiency by jointly considering a number of network states and environmental statistics, including fast sensing results, short-term statistical information, channel quality, data transmission rate, and channel contention characteristics. This is for a better estimation on the optimal range of spectrum for fine sensing so as to adaptively reduce the overall channel sensing time. Performance analysis is conducted on the proposed EKBR scheme using a multidimensional absorbing Markov chain to evaluate various performance metrics of interest, such as average sensing delay (or referred to as sensing overhead in the study), average data transmission rate, and percentage of missed spectrum opportunities. Numerical results show that the proposed EKBR scheme achieves better performance than that by the state-or-the-art techniques while yielding less computation complexity and sensing overhead.</p>
},
	keywords = {access protocols, channel quality, cognitive radio, computation complexity, computational complexity, data transmission rate, environmental statistics, extended knowledge based reasoning, MAC layer, Markov chain, Markov processes, medium access control layer, network states, sensing overhead, short term statistical information, spectrum sensing, spread spectrum communication},
	issn = {1536-1233},
	doi = {http://dx.doi.org/10.1109/TMC.2009.148},
	author = {X Wang and A Wong and P-H. Ho}
}
@article {228,
	title = {Spectrum sensing in cognitive radio using a Markov-Chain Monte-Carlo scheme},
	journal = {IEEE Communications Letters},
	volume = {14},
	year = {2010},
	pages = {830-832},
	abstract = {<p>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.</p>
},
	keywords = {channel allocation, channel selection, cognitive radio, CR systems, Markov chain Monte Carlo scheme, Markov processes, Monte Carlo methods, network traffic, optimisation, optimization problem, spectral analysis, spectrum sensing, telecommunication traffic},
	issn = {1089-7798},
	doi = {http://dx.doi.org/10.1109/LCOMM.2010.080210.100569},
	author = {X Wang and A Wong and P-H Ho}
}
