Learning-Based Cooperative Spectrum Sensing in Hybrid Underlay-Interweave Secondary Networks
Bibliographic record
Abstract
In this paper, a cooperative Secondary Network (SN) is proposed that operates under a hybrid underlay-interweave model. The narrowband sensing problem under the interweave model was formulated as a binary hypothesis problem. The Fusion Center (FC) uses learning techniques, namely the Gaussian Mixture Model (GMM), Support Vector Machine (SVM), and Naive Bayes' (NB) to classify the state of the channel. An SVM kernel was chosen to best fit our hybrid network, since a nonlinear relationship exists between the energy vectors collected at the FC. Furthermore, the degree of the polynomial SVM kernel was manipulated to minimize classification errors. The multi-class SVM (MSVM) algorithm was reformulated to fit our multiple hypothesis problem in the underlay model. The performance of the hybrid network was evaluated based on the Receiver Operating Characteristics (ROC) and classification accuracy. In addition, the accuracy of the MSVM is improved through the cooperation of the SUs. Our results show that the proposed learning-based hybrid model is robust to low SNR environments, and yields an improved performance compared with traditional cooperative sensing techniques. Moreover, we show that the Gaussian SVM kernel surpasses other proposed learning algorithms achieving as high as an 80% detection rate with as low as 10% false alarm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".