End-to-End Delay Analysis in Cognitive Radio Ad Hoc Networks with Different Traffic Models

Abstract

Delay and throughput are important metrics for network performance. We analyze the end-to-end delay of cognitive radio ad hoc networks for two traffic models: backlogged and geometric, respectively. By modelling the primary users as a Poisson point process and the secondary network deploying multihop transmissions, we derive the closed-form expression for the end-to-end delay in secondary networks. Furthermore, we optimize the end-to-end delay in terms of the hop number and the secondary transmission probability, respectively. The range of the optimal hop number and the equation satisfied by the optimal transmission probability are obtained for backlogged source models. The equation met by the optimal hop number is presented for geometric source models.