Channel Selection Algorithm for Cognitive Radio Networks with Heavy-Tailed Idle Times

摘要

We consider a multichannel Cognitive Radio Network (CRN), where secondaryusers sequentially sense channels for opportunistic spectrum access. In thisscenario, the Channel Selection Algorithm (CSA) allows secondary users to finda vacant channel with the minimal number of channel switches. Most of theexisting CSA literature assumes exponential ON-OFF time distribution forprimary users (PU) channel occupancy pattern. This exponential assumption mightbe helpful to get performance bounds; but not useful to evaluate theperformance of CSA under realistic conditions. An in-depth analysis ofindependent spectrum measurement traces reveals that wireless channels havetypically heavy-tailed PU OFF times. In this paper, we propose an extension tothe Predictive CSA framework and its generalization for heavy tailed PU OFFtime distribution, which represents realistic scenarios. In particular, wecalculate the probability of channel being idle for hyper-exponential OFF timesto use in CSA. We implement our proposed CSA framework in a wireless test-bedand comprehensively evaluate its performance by recreating the realistic PUchannel occupancy patterns. The proposed CSA shows significant reduction inchannel switches and energy consumption as compared to Predictive CSA whichalways assumes exponential PU ON-OFF times.Through our work, we show the impactof the PU channel occupancy pattern on the performance of CSA in multichannelCRN.