Distributed opportunistic spectrum access with unknown population
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".