@inproceedings {257,
	title = {Spectral variation constrained power spectral density estimation for wideband spectrum sensing},
	booktitle = {3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL)},
	year = {2010},
	abstract = {<p>In this paper, a novel power spectrum density (PSD) estimation approach is proposed for accurate and efficient wideband spectrum sensing in Cognitive Radio (CR) systems. Based on the observed signal from a wideband receiver, the goal of determining the fluctuation-free signal PSD is formulated as a constrained Bayesian estimation problem, subject to spectral variation constraints between neighboring spectral frequencies. The extracted signal PSD obtained using the proposed approach can then used in the energy detection process to make informed decisions with regards to the identification of free spectrum resources for opportunistic access by the CR. Experimental results using Monte Carlo simulations and real terrestrial digital TV (DTV) signal acquisitions show that the proposed approach allows for accurate PSD computation using wideband receivers under unknown noise and fluctuation conditions. Therefore, there is great potential for integrating the proposed method into existing energy detection methods for more accurate and efficient wideband spectrum sensing in CR systems under unknown noise and channel conditions.</p>
},
	keywords = {Bayes methods, cognitive radio, cognitive radio system, constrained Bayesian estimation problem, CR systems, digital television, energy detection process, fluctuation-free signal PSD, Monte Carlo methods, Monte Carlo simulations, neighboring spectral frequency, power spectrum density estimation approach, radio receivers, real terrestrial digital TV signal acquisitions, signal detection, spectral analysis, spectral variation constraint, wideband receiver, wideband spectrum sensing},
	doi = {http://dx.doi.org/10.1109/ISABEL.2010.5702780},
	author = {X Wang and A Wong and S-Y Lien}
}
@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}
}
