Selective sensing and transmission for multi-channel cognitive radio networks | Xu et al. EURASIP Journal on Wireless Communications and Networking 2011 2011 7 http content 2011 1 7 o EURASIP Journal on Wireless Communications and Networking a SpringerOpen Journal RESEARCH Open Access Selective sensing and transmission for multi-channel cognitive radio networks You Xu1 Yunzhou Li2 4 Yifei Zhao2 Hongxing Zou1 and Athanasios VVasilakos3 Abstract In this article we consider a continuous time Markov chain CTMC modeled multi-channel CR network where there are multiple independent primary users and one slotted secondary user SU who can access multiple channels simultaneously. To maximize SU s temporal channel utilization while limiting its interference to PUs a selective sensing and selective access SS-SA strategy is proposed. With SS strategy each channel is sensed almost periodically with different periods according to parameter Tc which reflects the maximal period that each channel should be probed. The effect of sensing period is also considered. When the sensing period is suitable the SA strategy can be regarded as greedy access strategy. Numerical simulations illustrate that Tc is a valid measurement to indicate how often each channel should be sensed and with SS-SA strategy SU can effectively utilize the channels and consume less energy and time for sensing than adopting reference strategies. Keywords Cognitive radio selective sensing and access continuous time Markov chain Introduction Recently people have made great progress on cognitive radio CR technology 1 2 . The basic idea of CR is to allow secondary user SU to search and utilize instantaneous spectrum opportunities left by primary user PU while limiting its interference to PU. Therefore SU s sensing and access strategy is very important to its performance especially for multi-channel CR networks. To discover and utilize the spectrum opportunities timely and efficiently SU should first model PU s behavior. There are mainly two models namely discrete-time model
