1. Optimal Online Sensing Sequence in Multi-Channel Cognitive Radio
Networks
We address the problem of rapidly discovering spectrum opportunities for seamless service
provisioning in cognitive radio networks (CRNs). In particular, we focus on multi-channel
communications via channel-bonding with heterogeneous channel characteristics of ON/OFF
patterns, sensing-time, and channel capacity. Using dynamic programming (DP), we derive an
optimal online sensing-sequence incurring a minimal opportunity-discovery delay, and propose a
suboptimal sequence that presents a near optimal performance while incurring significantly less
computational overhead than the DP algorithm. To facilitate fast opportunity discovery, we also
propose a channel-management strategy that maintains a list of backup channels to be used at
building the optimal sequence. A hybrid of maximum likelihood (ML) and Bayesian inference is
introduced as well for flexible estimation of ON/OFF channel usage patterns, which selectively
chooses 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, is
evaluated via in-depth simulation, and for the scenarios we considered, the proposed suboptimal
sequence achieves a near-optimal performance with only an average of 0.5% difference from the
optimal delay, and outperforms the previously-proposed probabilistic scheme by up to 50.1%. In
addition, the backup channel update scheme outperforms the no-update case by up to 49.9%.
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