Primary Traffic Characterization and Secondary Transmissions

TL;DR

This paper analyzes real-world WLAN channel idle times, models them with hyper-exponential distributions, and proposes adaptive secondary transmission strategies to improve performance in non-stationary environments.

Contribution

It introduces a hyper-exponential distribution model for WLAN idle times and proposes a novel adaptive transmission strategy for secondary users in non-stationary conditions.

Findings

Channel idle times are well modeled by hyper-exponential distributions.

Stationary transmission strategies can cause performance loss in non-stationary environments.

The proposed adaptive strategy improves secondary user performance in real-world WLAN data.

Abstract

Channel idle time distribution based secondary transmission strategies have been studied intensively in the literature. Under various performance metrics, the ultimate performance of secondary devices are eventually dictated by the presumed channel idle time distribution. Such distributions can take any arbitrary form in practice. In this work, we study idle time distributions in wireless local area networks (WLAN) using large amount of the channel idle time data collected in real-world WLAN networks. We demonstrate with experimental data that the channel idle time distribution can be closely modeled by hyper-exponential distribution. Furthermore, one can treat the primary packet arrival process as a semi-Markov modulated Poisson process. Several secondary transmission strategies are discussed under this model. It is shown that using only one hyper-exponential distribution, the secondary user can achieve a desirable performance when the primary packet arrival process is stationary. However, experimental data suggests that in practice, this process is not stationary and the secondary user can experience a large performance loss with stationary transmission strategy. We propose a novel transmission strategy that achieves suboptimal secondary user performance when the idle time distribution is not stationary. The performances of secondary transmission strategies are demonstrated using experimental data.