On the throughput performance of cluster-based cognitive radio networks
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses effect of reporting channel bandwidth on cognitive radio (CR) networks. A cluster based approach is considered where the secondary base station is replaced by a fusion center and a global reporting channel is used instead of local ones. A new approach to select the fusion center based on the general centre scheme in graph theory is proposed. The minimal dominating set (MDS) clustering approach is used to minimise the set of clusters that keeps the network connected. The effect of various parameters such as cluster size and number, quality of the reporting channel and sensing time on sensing efficiency, accuracy and per node throughput are investigated. Results show cluster based cooperative sensing throughput outperforms conventional cooperative sensing especially when the reporting channel has high probability of error. Systematic ways to determine optimum number of clusters and optimum sensing time are developed.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it