Channel Selection Algorithm for Cognitive Radio Networks with Heavy-Tailed Idle Times
S. Senthilmurugan, Junaid Ansari, Petri M\"ah\"onen, T.G. Venkatesh,, and Marina Petrova

TL;DR
This paper extends the Predictive Channel Selection Algorithm for cognitive radio networks to account for heavy-tailed primary user OFF times, demonstrating improved efficiency and reduced energy consumption in realistic scenarios.
Contribution
It introduces a heavy-tailed distribution model into the CSA framework, enhancing its applicability to real-world channel occupancy patterns.
Findings
Significant reduction in channel switches.
Lower energy consumption compared to existing CSA.
Effective modeling of heavy-tailed PU OFF times.
Abstract
We consider a multichannel Cognitive Radio Network (CRN), where secondary users sequentially sense channels for opportunistic spectrum access. In this scenario, the Channel Selection Algorithm (CSA) allows secondary users to find a vacant channel with the minimal number of channel switches. Most of the existing CSA literature assumes exponential ON-OFF time distribution for primary users (PU) channel occupancy pattern. This exponential assumption might be helpful to get performance bounds; but not useful to evaluate the performance of CSA under realistic conditions. An in-depth analysis of independent spectrum measurement traces reveals that wireless channels have typically heavy-tailed PU OFF times. In this paper, we propose an extension to the Predictive CSA framework and its generalization for heavy tailed PU OFF time distribution, which represents realistic scenarios. In particular,…
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Taxonomy
TopicsCognitive Radio Networks and Spectrum Sensing · Advanced MIMO Systems Optimization · Wireless Communication Networks Research
