Cooperative Quickest Spectrum Sensing in Cognitive Radios with Unknown Parameters
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Bibliographic record
Abstract
In this paper, cooperative quickest spectrum sensing for cognitive radios is studied. Various cooperative schemes are considered based on the cumulative sum (CUSUM) algorithm, for different memory and communication constraint scenarios. The optimal CUSUM statistics are derived for each of these cooperative sensing schemes in the noisy channel scenario. In practice, due to unknown parameters in the distribution of the observations, the CUSUM-based approaches are not directly applicable to cognitive radios. Therefore, we propose to apply a linear test, which does not require any prior knowledge or estimates of the unknown parameters, for quickest spectrum sensing of cognitive radios. We derive linear-based CUSUM statistics for different cooperative sensing scenarios. The proposed approach results in fast and simple algorithms for cooperative quickest detection with unknown parameters, while maintaining a performance close to that of the perfectly known parameter schemes.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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