Priority Queueing Models for Cognitive Radio Networks with Traffic Differentiation

TL;DR

This paper develops a comprehensive queueing model for cognitive radio networks with multiple traffic classes, analyzing the impact of different priority disciplines and network parameters on system performance.

Contribution

It introduces new analytical models and priority service disciplines tailored for opportunistic spectrum access networks, validated by simulations.

Findings

Packet system time increases with longer operating and interruption periods.

Priority queueing provides greater service differentiation in OSA networks.

Analytical tools are versatile for various OSA network traffic analyses.

Abstract

In this paper, we present a new queueing model providing the accurate average system time for packets transmitted over a cognitive radio (CR) link for multiple traffic classes with the preemptive and non-preemptive priority service disciplines. The analysis considers general packet service time, general distributions for the channel availability periods and service interruption periods, and a service-resume transmission. We further introduce and analyze two novel priority service disciplines for opportunistic spectrum access (OSA) networks which take advantage of interruptions to preempt low priority traffic at a low cost. Analytical results, in addition to simulation results to validate their accuracy, are also provided and illustrate the impact of different OSA network parameters on the average system time. We particularly show that, for the same average CR transmission link availability, the packet system time significantly increases in a semi-static network with long operating and interruption periods compared to an OSA network with fast alternating operating and interruption periods. We also present results indicating that, due to the presence of interruptions, priority queueing service disciplines provide a greater differentiated service in OSA networks than in traditional networks. The analytical tools presented in this paper are general and can be used to analyze the traffic metrics of most OSA networks carrying multiple classes of traffic with priority queueing service differentiation.