Distributed stochastic learning for dynamic spectrum access adaptive to primary network conditions
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 among M secondary users (SUs) in a cognitive radio network. We aim at designing an adaptive policy that can effectively respond to different primary network conditions. By applying stochastic learning automata, we propose an adaptive decentralized access policy. Each SU probabilistically chooses one of the M-best channels to access. The channel selection probability is then updated based on collision events. Our proposed adaptive policy utilizes two underlying distributed learning algorithms: one is to learn from sensing history on the primary channel availability, and the other is to learn from collision history on channel selections among SUs to avoid further collision. Some previously proposed distributed access policies can be viewed as special cases of our proposed adaptive policy, with a set of pre-set channel selection probabilities. Simulation results demonstrate the effectiveness of our proposed adaptive policy in various distributions of mean channel availabilities across primary channels, as compared with other existing policies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it