AI-Driven Unified Channel Management in Cognitive Radio IoT Networks: Integration of OFDM, SDN, MRC, RIS, and Cloud Computing - Kodadi, Sharadha; Deevi, Durga Praveen; Allur, Naga Sushma; Dondapati, Koteswararao; Chetlapalli, Himabindu (Journal of ISMAC, 2025)
Cognitive Radio Internet of Things (CR-IoT) networks are becoming more complicated, leading for reliable spectrum management solutions. By combining OFDM, SDN, MRC, and RIS, an AI-driven unified channel management framework successfully meets these needs. This framework optimizes energy consumption, spectrum efficiency, and dependability while facilitating smooth real-time adaptability to changing wireless network conditions. By utilizing OFDM for spectral efficiency and adaptive subcarrier allocation, SDN for centralized network control, MRC for signal reliability through multi-signal combination, and RIS for optimized signal propagation through phase shifts, AI enables dynamic spectrum management. Meanwhile, cloud computing handles massive data processing for in-the-moment decision-making. Developed to improve spectrum management, network scalability, signal reliability, and energy efficiency, the suggested AI-based model outperforms traditional methods like SAP, FBMC, IBN, and DAS in anomaly detection and efficiency, attaining a 92% anomaly detection rate, with 94% accuracy, 93% scalability, and a 95% F1 score. By combining these technologies, the framework improves wireless network performance and tackles important problems like energy efficiency and spectrum scarcity in extensive IoT installations.