Learning-stage based decentralized adaptive access policy for dynamic spectrum access
Why this work is in the frame
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Bibliographic record
Abstract
We consider the problem of decentralized online learning and channel access in a cognitive radio network. Based on an existing distributed access policy proposed in [1], named the ρ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAND</sup> policy, we propose an adaptive decentralized access policy in which the distributed coordination among secondary users is adjusted at different stages of learning accuracy of the primary network. Specifically, we exploit a “perceived population” by each secondary user to reduce collision events at different learning stages. We design a metric that measures the level of learning accuracy and use that as an indicator to adjust the “perceived population” by each secondary user. Simulations show that our proposed adaptive policy improves the leading constant of the normalized regret and can provide substantial improvement over the ρ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RAND</sup> policy.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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