AI-Driven Unified Channel Management in Cognitive Radio IoT Networks: Integration of OFDM, SDN, MRC, RIS, and Cloud Computing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it