Intelligent CSMA-based opportunistic spectrum access: Competition and cooperation
Bibliographic record
Abstract
This paper presents a CSMA-based opportunistic spectrum access for secondary users (SUs) from a game-theoretic perspective. A key requirement for efficient design of CSMA-based access schemes for SUs is to address competition among SUs. Thus, to enable contention control in consideration of competition among SUs, an adaptive SU access approach based on a modified CSMA scheme is presented in which each SU accesses multiple idle frequency-slots of a licensed frequency band with different probabilities. The problem of finding optimal access probabilities of SUs is cast in a game-theoretic framework to highlight the issues of competition and cooperation among SUs. Subsequently, the existence, uniqueness and efficiency of Nash Equilibrium (NE) are investigated. To improve the efficiency of the unique NE in the competitive design, the game is transformed into a more cooperative framework exploiting a pricing mechanism. Finally, an algorithm based on the best response dynamics is developed in which each SU independently updates its access probabilities until convergence to the unique NE.
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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 | 0.000 | 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".