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In Cognitive Radio Network (CRN), the secondary user (SU) opportunisticallyaccess the wireless channels whenever they are free from the licensed / PrimaryUser (PU). Even after occupying the channel, the SU has to sense the channelintermittently to detect reappearance of PU, so that it can stop itstransmission and avoid interference to PU. Frequent channel sensing results inthe degradation of SU's throughput whereas sparse sensing increases theinterference experienced by the PU. Thus, optimal sensing interval policy playsa vital role in CRN. In the literature, optimal channel sensing strategy hasbeen analysed for the case when the ON-OFF time distributions of PU areexponential. However, the analysis of recent spectrum measurement tracesreveals that PU exhibits heavy-tailed idle times which can be approximated wellwith Hyper-exponential distribution (HED). In our work, we deduce the structureof optimal sensing interval policy for channels with HED OFF times throughMarkov Decision Process (MDP). We then use dynamic programming framework toderive sub-optimal sensing interval policies. A new Multishot sensing intervalpolicy is proposed and it is compared with existing policies for itsperformance in terms of number of channel sensing and interference to PU.

Optimal Channel Sensing Strategy for Cognitive Radio Networks with Heavy-Tailed Idle Times