Opportunistic Spectrum Access with Hopping Transmission Strategy: A Game Theoretic Approach
Bibliographic record
Abstract
This paper presents a study on opportunistic spectrum access for secondary users (SUs) from a game-theoretic learning perspective. In consideration of the random return of primary users, it is assumed that a SU dynamically hops over multiple idle frequency-slots of a licensed frequency band, each with an adaptive activity factor. The problem of finding optimal activity factors of SUs is cast in a game-theoretic framework and is formulated as a potential game. Subsequently, the existence, feasibility and optimality of Nash Equilibrium (NE) are investigated analytically. Furthermore, an algorithm is developed in which each SU independently adjusts its activity factors based on the best response dynamics by learning other SUs' behavior from locally available information. Aiming to establish stability for the proposed algorithm, the convergence with probability 1 to an arbitrarily small neighborhood of the globally optimal solution is investigated with analysis and simulation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".