AI-Driven Unified Channel Management in Cognitive Radio IoT Networks: Integration of OFDM, SDN, MRC, RIS, and Cloud Computing
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".