Aware Channel Assignment Algorithm for Cognitive Networks

Several recent studies show that Cognitive Networks (CNs) can effectively address the spectrum shortage problem in wireless networks mainly caused by the increasing number of wireless services and applications operating in unlicensed channels.However, these studies fail in considering the difference between the channel characteristics, i.e. they do not consider that channels are on different frequencies.In this paper, we fill this gap providing a technique to classify channels based on their operating frequency and we provide to each cognitive device the ability of choosing the best channel depending on its traffic demand.We focus on the IEEE 802.22 physical layer in order to analyze and classify channels and we propose the aware channel assignment algorithm for cognitive networks (Aaron).Aaron assigns channels to cognitive devices with the goal of satisfying the capacity demand of the largest number of end-users in order to maximize the throughput.We evaluate Aaron using an ad-hoc event-driven simulator for CNs and we compare it with the Dumb algorithm, where cognitive devices do not have the ability of characterize channels, and with the Upper Bound, where no packet is lost due to the channel assignment algorithm.Simulation studies demonstrate that Aaron performs widely better than Dumb and very close to the Upper Bound.