QoS-Driven Stochastic Analysis for Heterogeneous Cognitive Radio Networks

The future 5G wireless network is largely driven by the increasing heavy traffic and spectrum scarcity. Cognitive Radio (CR) techniques provide a potential solution for improving the spectrum efficiency. In this paper, we study the stochastic framework for the CR networks, considering different quality of service (QoS) requirements. To analyze the performance of the CR network, we adopt a poisson point process (PPP) to capture the mobility and randomness of user location. A stochastic-network-calculus (SNC) based approach is proposed to model the wireless transmission and evaluate the network performance. In order to achieve the performance metrics of end-to-end (E2E) delay and backlog in the entire network, we propose a new conception named as effective service process (ESP) which is able to capture the QoS requirements of users. Furthermore, we evaluate the performance in the exponential domain, which can present the E2E analysis more directly. The simulation results verify the theoretical analysis and show that the performance in the CR networks can be derived perfectly with the proposed approach, considering the stochastic traffic arrival and designed service model in our schedule.