Performance Analysis of Cognitive-Radio IoT Networks

Due to the ever-expanding applications of the Internet-of-Things (IoT), designing spectrally-efficient transmission schemes to support massive connections and devices is inevitable and still challenging. Thus, cognitive-radio (CR) systems are becoming more indispensable for future IoT networks. This paper analyzes the performance of CR-IoT networks, where closed-form expressions for network metrics, such as GoodPut, collision probability and average packet delay are derived. In addition to the interference caused by spectrum sensing errors, our analysis also incorporates the primary user (PU) return interference into the different network metrics. Furthermore, the effect of primary network traffic behavior and IoT network parameters are investigated. To account for delay-sensitive packets, the average end-to-end delay of packets in the IoT network is mathematically formulated and analyzed as a quality-of-service (QoS) measure for network stability. Numerical results are presented to validate the analytical derivations, and shed light on the dependency of the GoodPut, collision probability and stability on the different network parameters.