Adaptive based machine learning approach for cooperative energy detection in cognitive radio networks
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
Abstract In cognitive radio networking, spectrum can be utilized by a secondary user while insuring no interference to the primary user of the spectrum. This helps enhancing the utilization of the spectrum while considering the rights of its primary users. Secondary users need to actively detect the existence/absence of the primary user to deploy a cognitive radio network. By cooperating, secondary users can enhance the detection capabilities, especially in environments with fading and noise, thereby increasing the reliability of spectrum sensing. The objective of this work is to employ machine learning with feature extraction and random forest classifier to enhance the individual secondary user energy detection accurateness in presence of a high level of noise power density. Clustering method is used to organize the secondary users for cooperative decision making on the existence of the primary user. The detection probability is analysed based on the ROC, where it reaches approximately 0.95 at a probability of false alarm of about 0.05, indicating a highly efficient detection capability.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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