CROSS-LAYER MODELING AND OPTIMIZATION OF MULTI-CHANNEL COGNITIVE RADIO NETWORKS UNDER IMPERFECT CHANNEL SENSING

Abstract

In this paper, we consider a multi-channel cognitive radio network with multiple secondary users (SUs) and analyze the performance of users in the network. We assume primary users (PUs) adopt the automatic repeat request (ARQ) protocol at the medium access control layer. We have two main goals. Our first goal is to develop a cross-layer performance model of the cognitive radio network by considering the retransmission characteristics of the ARQ protocol and the interference between PUs and SUs due to imperfect channel sensing. Using the cross-layer performance model we analyze the throughput performance of SUs and the delay performance of PUs. Our second goal is to propose an optimal channel sensing method that maximizes the throughput performance of SUs while a given delay requirement of PUs is guaranteed. To this end, using our cross-layer performance model, we formulate an optimization problem and solve it to get an optimal channel sensing method that satisfies the design objectives. Numerical and simulation results are provided to validate our analysis and to investigate the performance of the optimal channel sensing method.