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

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, we calculate the probability of channel being idle for hyper-exponential OFF times to use in CSA. We implement our proposed CSA framework in a wireless test-bed and comprehensively evaluate its performance by recreating the realistic PU channel occupancy patterns. The proposed CSA shows significant reduction in channel switches and energy consumption as compared to Predictive CSA which always assumes exponential PU ON-OFF times.Through our work, we show the impact of the PU channel occupancy pattern on the performance of CSA in multichannel CRN.