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Optimal Online Sensing Sequence in Multi-Channel Cognitive Radio                                         NetworksWe addres...
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Optimal online sensing sequence in multi channel cognitive radio networks

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Optimal online sensing sequence in multi channel cognitive radio networks

  1. 1. Optimal Online Sensing Sequence in Multi-Channel Cognitive Radio NetworksWe address the problem of rapidly discovering spectrum opportunities for seamless serviceprovisioning in cognitive radio networks (CRNs). In particular, we focus on multi-channelcommunications via channel-bonding with heterogeneous channel characteristics of ON/OFFpatterns, sensing-time, and channel capacity. Using dynamic programming (DP), we derive anoptimal online sensing-sequence incurring a minimal opportunity-discovery delay, and propose asuboptimal sequence that presents a near optimal performance while incurring significantly lesscomputational overhead than the DP algorithm. To facilitate fast opportunity discovery, we alsopropose a channel-management strategy that maintains a list of backup channels to be used atbuilding the optimal sequence. A hybrid of maximum likelihood (ML) and Bayesian inference isintroduced as well for flexible estimation of ON/OFF channel usage patterns, which selectivelychooses the better between the two according to the frequency of sensing and ON/OFF durations.The performance of the proposed schemes, in terms of the opportunity-discovery delay, isevaluated via in-depth simulation, and for the scenarios we considered, the proposed suboptimalsequence achieves a near-optimal performance with only an average of 0.5% difference from theoptimal delay, and outperforms the previously-proposed probabilistic scheme by up to 50.1%. Inaddition, the backup channel update scheme outperforms the no-update case by up to 49.9%. Ambit lick Solutions Mail Id: Ambitlick@gmail.com , Ambitlicksolutions@gmail.Com

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