Differential sensing in cognitive personal area networks with bursty secondary traffic and varying primary activity factor
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 technology necessitates accurate and timely sensing of primary users' activity on the chosen set of channels. We assume that the number of sensing nodes is smaller than the number of channels; the results of sensing are cooperatively combined to form a coherent channel map. Since each secondary user has its own bursty data traffic, it is unavailable for sensing until that data is transmitted. Furthermore, we assume that idle channels ought to be sensed more frequently than the active ones, so that the end of transmission opportunities are sensed with greater accuracy. The article presents a probabilistic analysis of the sensing mechanism under these circumstances, and investigates the range of parameter values in which the sensing process is capable of maintaining an accurate view of the status of the working channel set.
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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 | 0.001 |
| 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