Pattern classification techniques for cooperative spectrum sensing in cognitive radio networks: SVM and W-KNN approaches
Bibliographic record
Abstract
We consider novel cooperative spectrum sensing (CSS) algorithms based on the pattern classification techniques for cognitive radio (CR) networks. In this regard, support vector machine (SVM) and weighted K-nearest-neighbor (KNN) classification techniques are implemented for CSS. The received signal strength at the CR users are treated as features and fed into the classifier to detect the availability of the primary user (PU). Each instance of PU activity (i.e., availability and unavailability) is categorized into positive and negative classes (respectively). In the case of SVM, for minimization of classification errors the support vectors are obtained by maximizing the margin between the separating hyperplane and data. Towards this end, we investigate the effect of different kernels through quantifying in terms of detection probability by representing the receiver operating characteristic (ROC) curves. Furthermore, weighted KNN classification technique is proposed for CSS and the corresponding weights are calculated by evaluating the area under ROC curve of each feature. Our comparative results clearly reveal that the proposed SVM and weighted KNN algorithms outperform the existing state-of-the-art pattern classification-based CSS techniques.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".