Predictive Channel Access in Cognitive Radio Networks Based on Variable order Markov models

Abstract

The concept of Cognitive radio enables the unlicensed users to share the spectrum with licensed users, on the condition that the licensed users have preemptive priority. The use of the channel by unlicensed users should not result in more than acceptable interference level to the licensed users, if interference occurs. The sense and react strategy by unlicensed users sometimes does not lead to acceptable level of interference while maintaining an acceptable data transfer rate for the unlicensed users. Proactive channel access has been proposed for the purpose of reducing the interference to primary users and to reduce the idle channel search delay for the secondary users. There are many methods used in the literature to model the channel state fluctuations. Based on these models the future channel states are predicted. In this thesis we introduce a predictive channel usage scheme which is capable of reducing the interference caused by the unlicensed users. Furthermore our scheme is capable of increasing the data rates the unlicensed users experience through the reduction of the idle channel identification delay. In our scheme no assumptions are made about the distribution of licensed user channel usage. We learn the traffic characteristics of the channels using a learning scheme called Probabilistic Suffix Tree algorithm.