Enhanced Decision Making Processes in Cognitive Radio Networks (CRNs) for Analyzing Spectrums using Security enabled Decentralized System Architecture

Cognitive Radio Networks (CRNs) offer a dynamic solution to spectrum scarcity by enabling unlicensed secondary users (SUs) to opportunistically utilize unused licensed spectrum bands. This study presents a collaborative decision-making model for enhanced spectrum management in Cognitive Radio Networks (CRNs), focusing on a decentralized and security-enabled architecture. Using real-time spectral occupancy data from GSM and Wi-Fi bands, the model implements a multi-module approach integrating primary user (PU) characterization, secondary user (SU) arrival prediction, and spectrum opportunity feedback. The model employs multi-criteria decision-making (MCDM) techniques, including Feedback Fuzzy Analytical Hierarchical Process (FF AHP) and Simple Additive Weighting (SAW), to optimize spectral resource allocation while minimizing interference and handoff failures. Simulations across varied traffic scenarios indicated that a moderate collaboration level (50%) among SUs yielded the best results, achieving a spectrum efficiency of 82.5% in low-traffic GSM conditions and reducing interference to as low as 8.1 %. Handoff failure rates were minimized to 5.9% in GSM low-traffic and 7.1 % in Wi-Fi low-traffic scenarios, demonstrating the model's adaptability and effectiveness. Additionally, the model achieved an average handoff success rate of 89.7% for real-time applications in low-traffic Wi-Fi scenarios. These findings underscore the potential of balanced collaboration in CRN s for enhancing resource utilization and maintaining network performance across different application types.