Dependability-Based Analysis for Spectrum Sensing and Spectrum Access in Cognitive Radio Networks with Heterogeneous Traffic

The Internet of Things (IoT) has experienced rapid growth in various applications, resulting in significant advancements that exhibit considerable variations in characteristics and requirements. Cognitive radio networks (CRNs) present a promising solution for ultra-reliable communication and dynamic spectrum sharing among IoT devices in 6G environment. The most critical task in CRNs is to identify unused spectrum opportunities, known as holes, across different times and locations. Addressing this challenge requires an effective spectrum sensing strategy at the medium access control layer to optimize spectrum use while minimizing interference with licensed user signals. In this paper, we have proposed a novel dynamic spectrum access scheme, which aims to address both spectrum availability and network reliability for various secondary user flows in IoT-centric CRNs. Our study examines the effect of random channel failure and their recovery on the performance of CRN. Moreover, we develop a continuous-time Markov chain model to examine the network performance across various key performance indicators (KPIs) in the presence of multiple channel failures and sensing errors. This analysis helps identify valuable trade-offs among the KPIs.