Distributed opportunistic spectrum access with unknown population
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
We consider a cognitive radio network where M secondary users compete with each other to access one of the N available channels. Channel availability statistics are assumed to evolve as i.i.d. Bernoulli random processes with means unknown to the secondary users. In addition, the number of secondary users M is unknown to each user. The main objective here is to design a distributed online learning and access policy which maximizes the total throughput of the secondary users. It has previously been shown that this problem can elegantly be modeled as a decentralized multi-armed bandit (DMAB) problem when M is known. We propose a truly decentralized online learning algorithm based on DMAB problem for unknown M. We show that using distributed access policies with wrong knowledge of M results in linear growth of regret, and underestimation incurs more significant loss than overestimation does. For distributed online learning of M, we propose a dynamic thresholding method, where the thresholds are dynamically determined using virtual systems built upon the current estimates of mean channel availabilities. Our algorithm allows both overestimation and underestimation in estimating M over time, and thus is capable of tracking the population change of secondary users.
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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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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