Journal of ISMAC
IRO Journals
Volume 6 — Issue 4 — December 2024

AI-Driven Unified Channel Management in Cognitive Radio IoT Networks: Integration of OFDM, SDN, MRC, RIS, and Cloud Computing

https://doi.org/10.36548/jismac.2024.4.007
Pages: 395-412
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Sharadha Kodadi
Infosys, Texas, USA
Durga Praveen Deevi
O2 Technologies Inc, California, USA
Naga Sushma Allur
Astute Solutions LLC, California, USA
Koteswararao Dondapati
Everest Technologies, Ohio, USA
Himabindu Chetlapalli
9455868 Canada Inc, Ontario, Canada
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How to Cite

Kodadi, Sharadha, Durga Praveen Deevi, Naga Sushma Allur, Koteswararao Dondapati, and Himabindu Chetlapalli. 2025. “AI-Driven Unified Channel Management in Cognitive Radio IoT Networks: Integration of OFDM, SDN, MRC, RIS, and Cloud Computing”. Journal of ISMAC 6 (4): 395-412. https://doi.org/10.36548/jismac.2024.4.007.
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Keywords

— AI
— OFDM
— SDN
— MRC
— RIS
— CRIoT
Published: 12-02-2025

Abstract

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.

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