Learning-Based Opportunistic Spectrum Access with Adaptive Hopping Transmission Strategy
Bibliographic record
Abstract
This paper presents an adaptive hopping transmission strategy for secondary users (SUs) to access temporarily idle frequency-slots of a licensed frequency band in consideration of the random return of primary users (PUs), aiming to maximize the overall SU throughput. A SU dynamically hops over multiple idle frequency-slots, each with an adaptive activity factor to avoid high-risk data loss due to possible PU return. SU activity factor optimization problems are formulated to develop the optimal opportunistic spectrum access (OSA) algorithms for SUs based on the Lagrange dual decomposition method. Subsequently, a fully distributed learning-based OSA algorithm is developed in which each SU independently adapts its activity factors to the optimal values over time by learning other SUs' behavior from locally available information. The convergence and convergence rate that characterize its asymptotic behavior and efficiency are analyzed. It is shown that the proposed learning-based OSA algorithm converges with probability of 1 to the optimal solution. Illustrative results confirm its effectiveness and performance gain as compared to existing OSA schemes.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".