On Topology and Resilience of Large-Scale Cognitive Radio Networks Under Generic Failures

Abstract

It has been demonstrated that in wireless networks, blackholes, which are typically generated by isolated node failures, and augmented by failure correlations, can easily result in devastating impact on network performance. In order to address this issue, we focus on the topology of Cognitive Radio Networks (CRNs) because of their phenomenal benefits in improving spectrum efficiency through opportunistic communications. Particularly, we first define two metrics, namely the failure occurrence probability $p$ and failure connection function $g(\cdot) $ , to characterize node failures and their spreading properties, respectively. Then we prove that each blackhole is exponentially bounded based on percolation theory. By mapping failure spreading using a branching process, we further derive an upper bound on the expected size of blackholes. With the observations from our analysis, we are able to find a sufficient condition for a resilient CRN in the presence of blackholes through analysis and simulations.