Load-Balancing Spectrum Decision for Cognitive Radio Networks

In this paper, we present an analytical framework to design system parameters for load-balancing multiuser spectrum decision schemes in cognitive radio (CR) networks. Unlike the non-load-balancing methods that multiple secondary users may contend for the same channel, the considered load-balancing schemes can distribute the traffic loads of secondary users to multiple channels. Based on the preemptive resume priority (PRP) M/G/1 queueing theory, a spectrum decision analytical model is proposed to evaluate the effects of multiple interruptions from the primary user during each link connection, the sensing errors (i.e., missed detection and false alarm) of the secondary users, and the heterogeneous channel capacity. With the objective of minimizing the overall system time of the secondary users, we derive the optimal number of candidate channels and the optimal channel selection probability for the sensing-based and the probability-based spectrum decision schemes, respectively. We find that the probability-based scheme can yield a shorter overall system time compared to the sensing-based scheme when the traffic loads of the secondary users is light, whereas the sensing-based scheme performs better in the condition of heavy traffic loads. If the secondary users can intelligently adopt the best spectrum decision scheme according to sensing time and traffic conditions, the overall system time can be improved by 50% compared to the existing methods.