Analysis and Implementation of Markov Chain-Based Model for Spectrum Defragmentation in Cognitive Radio-Enabled IoT Networks in 5G

In the context of high demand for high-traffic applications and the increasing need for Internet of Things (IoT) in 5G and beyond 5G networks, Cognitive Radio Networks (CRNs) have emerged as a potential solution due to the scarcity of RF spectrum. However, implementing IoT faces various challenges, including vulnerabilities to changing environmental conditions, efficient bandwidth distribution and utilization, and the high cost of purchasing RF spectrum. To address these challenges, effective spectrum management is crucial, and one aspect of it is spectrum defragmentation. Although it is impossible to completely avoid spectrum fragmentation, efficient spectrum allocation policies can minimize its impact. This paper suggests utilizing Discrete Time Markov Chain (DTMC) models to analyze the busy and idle times of primary users (PUs) in CRNs. The goal is to study the effect of defragmentation on the blocking probability of connections and propose a hybrid proactive-reactive defragmentation model that prioritizes secondary user (SU) traffic while ensuring the confidentiality and integrity of packet data delivery in IoT implementation in 5G. The analytical and simulation results demonstrate the positive impact of defragmentation in reducing the blocking probability and supporting SU traffic.