Traffic demand-based cooperation strategy in cognitive radio networks
Bibliographic record
Abstract
Cognitive radio networks (CRNs) enable spectrum channels to be used by secondary users (SUs) without interfering with the transmission of primary users (PUs). Cooperation among SUs in CRNs not only improves sensing performance but also increases spectrum efficiency. In this work, we study a cooperation strategy in multi-channel CRNs, which allows an energy-constrained SU to selectively participate in cooperative sensing. We consider CRNs where SUs can make distributed decisions on cooperative sensing based on traffic demand. We formulate this problem as a non-transferable utility (NTU) coalition formation game problem, where each SU in a coalition has a coalition value that takes into account traffic demand and energy efficiency. We also propose a sequential coalition formation (SCF) algorithm to find the coalition structure. Simulation results show that our proposed algorithm achieves higher throughput and energy efficiency with a lower computational complexity compared to previously proposed coalition formation algorithm in [1].
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".