Demand-Matching Spectrum Sharing in Cognitive Radio Networks: A Classified Game
Bibliographic record
Abstract
Cognitive Radio (CR) has been proposed as a promising technique to solve spectrum scarcity problem in wireless communications. For the implementation of CR, one major challenge is to design distributed spectrum sharing, which needs to efficiently coordinate CRs in accessing the spectrum opportunistically based on only local information. To address this problem, in this paper, we make use of the heterogeneity among users in cognitive radio networks (CRNs) and propose a distributed cooperative game with classified players. A prioritized CSMA/CA technique is adopted so that CRs select channels and their priority to access channel based on their satisfaction history, a public signal for CRs to collaborate to achieve the Correlated Equilibrium (C.E.). A no-regret learning algorithm is adopted to learn the C.E. Simulation results show that the proposed C.E. based classified game (CECG) can achieve up to 40% better performance compared to the unclassified one.
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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 | 0.001 | 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 | 0.000 | 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".