Performance Enhancement of Real- Time Traffic in Cognitive Radio Networks with Channel Assembling Strategy

Abstract

Channel assembling techniques are applied in cognitive radio networks nowadays, in which more than one channel are merged to achieve higher throughput for ongoing services. Channel assembling techniques are effective, particularly for multimedia applications that require higher spectrum bandwidth for better quality. In this paper, a channel assembling strategy is proposed and investigated using a continuous time Markov chain model, within the existence of heterogeneous traffics: elastic and real-time. The proposed strategy aims at enhancing the performance of the real-time traffic. Higher priority is assigned to that real-time traffic which enables interrupting elastic traffic if the number of channels to be assembled is not available. Moreover, an arriving primary user to a full system interrupts the elastic traffic firstly. Numerical results show a significant improvement in the real-time traffic capacity and blocking probability. The main effect on the elastic traffic appears in the forced termination probability.