@article {178, title = {Stochastic medium access for cognitive radio ad hoc networks}, journal = {IEEE Journal on Selected Areas in Communications}, year = {2011}, pages = {770 - 783}, abstract = {

In ad hoc cognitive radio (CR) networks, medium access control (MAC) design has been raised as a major challenge due to its highly dynamic nature and strong user diversity, particularly in situations where a dedicated control channel is not reserved among the distributed CR nodes. In this paper, we propose a novel Stochastic Medium Access (SMA) scheme that takes interference constraints into account to improve spectrum sharing efficiency. Specifically, the proposed SMA scheme is developed to serve in a CR network without dedicated control channels, such that the probability of successful channel accesses can be maximized. The formulated optimization problem is then solved by using a dynamic Markov-Chain Monte-Carlo scheme. Moreover, the paper introduces a suite of mechanisms for implementation of the proposed SMA scheme, including segmentation of long packets and contention resolution, which is working on top of power controlled Request-to-Send (RTS) and Clear-to-Send (CTS) exchanges in a multichannel environment. An analytical model is developed on the proposed SMA scheme using an absorbing Markov chain model to evaluate throughput of the secondary user network. Extensive simulation is conducted to study the impact of some important factors on the proposed SMA scheme, such as channel conditions and secondary traffic loads.

}, keywords = {ad hoc network, ad hoc networks, clear to send, cognitive radio, dedicated control channel, Markov chain model, Markov process, medium access control design, Monte Carlo scheme, request to send, stochastic medium access}, issn = {0733-8716}, doi = {0733-8716}, author = {X Wang and A Wong and P-H Ho} } @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 = {

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.

}, 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} } @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 = {

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.

}, 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 = {

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 = {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} } @inproceedings {299, title = {Prioritized spectrum sensing in cognitive radio based on spatiotemporal statistical fusion}, booktitle = {IEEE Wireless Communications \& Networking Conference}, year = {2009}, abstract = {

In this paper, a novel statistics-driven spectrum sensing algorithm is developed for improving spectrum sensing efficiency in the media access control (MAC) layer of cognitive radio (CR) systems. The proposed algorithm aims to achieve higher spectrum sensing efficiency and spectrum access opportunity by prioritizing channels for fine sensing based on the statistical likelihood of channel availability. The sensing priority is obtained by jointly exploiting the long-term spatiotemporal statistics recorded from the historical result of fine sensing, the short-term statistical information of channel condition obtained from a small-scale observation window, and the instantaneous statistical information obtained from fast sensing. Simulation results show that the proposed prioritization algorithm can achieve improved data transmission rates and reduced missed spectrum access opportunities when compared to the conventional non- prioritization spectrum sensing approach for situations where cooperative spectrum sensing is not suitable.

}, keywords = {access protocols, channel availability, cognitive radio, frequency allocation, MAC, media access control layer, prioritized spectrum sensing, spatiotemporal statistical fusion, statistical analysis, wireless channels}, doi = {http://dx.doi.org/10.1109/WCNC.2009.4917920}, author = {X Wang and A Wong and P-H Ho} } @inproceedings {300, title = {Stochastic channel prioritization for spectrum sensing in cooperative cognitive radio}, booktitle = {Consumer Communications and Networking Conference}, year = {2009}, abstract = {

In this paper, a novel cooperative stochastic channel prioritization algorithm is presented for the purpose of improving spectrum sensing efficiency in cooperative cognitive radio systems. The proposed algorithm achieves the goal by prioritizing the channels for fine sensing based on both local statistics obtained by the cognitive radio as well as long-term spatiotemporal statistics obtained from other cognitive radios. Channel priority is determined in a stochastic manner by performing statistical fusion on the local statistics and statistics from neighboring cognitive radios to obtain a biasing density from which stochastic sampling can be used to identify the likelihood of channel availability. Therefore, the individual cognitive radios collaborate to improve the likelihood of each cognitive radio in obtaining available channels. Simulation results show that the proposed cooperative stochastic channel prioritization algorithm can be used to reduce both sensing overhead and percentage of missed opportunities when implemented in a complimentary manner with existing cooperative cognitive radio systems.

}, keywords = {cognitive radio, cooperative cognitive radio, long-term spatiotemporal statistic, spectrum sensing efficiency, statistical analysis, statistical fusion, stochastic channel prioritization algorithm, stochastic processes, stochastic sampling}, url = {http://dx.doi.org/10.1109/CCNC.2009.4784893}, author = {X Wang and A Wong and P-H Ho} } @inproceedings {341, title = {Towards efficient spectrum sensing for cognitive radio through knowledge-based reasoning}, booktitle = {International Dynamic Spectrum Access Networks (DySPAN) Symposium {\textquoteright}08}, year = {2008}, abstract = {

In this paper, an efficient approach to Medium Access Control layer spectrum sensing algorithm is introduced for the purpose of peer-to-peer communications with cognitive radio. The proposed algorithm is designed specifically for the application scenario of information gathering in a full-loaded legacy voice and data band, where the cognitive radio tries to utilize other available licensed bands through spectrum sensing. Efficiency is achieved in the proposed algorithm through the use of a knowledge-based reasoning approach to fine sensing, where the optimal range of channels to finely sense is determined using proactive fast sensing and channel quality information as priors. Simulation results demonstrate that the proposed spectrum sensing algorithm is capable of achieving improved performance over existing techniques by drawing a good balance between performance and operational complexity.

}, keywords = {access protocols, cognitive radio, knowledge based systems, knowledge-based reasoning, medium access control layer spectrum sensing algorithm, peer-to-peer communications, peer-to-peer computing}, author = {X Wang and P-H Ho and A Wong} }