Trust-Based Energy Efficient Spectrum Sensing in Cognitive Radio Networks
Bibliographic record
Abstract
An energy efficient collaborative spectrum sensing (EE-CSS) protocol, based on trust management, is proposed. The protocol reduces the total number of sensing reports exchanged between the secondary users (SUs) and the secondary user base station (SUBS) when compared to a traditional collaborative spectrum sensing (T-CSS) protocol in which each SU transmits a sensing report to the SUBS. In addition, the minimum total number of sensing reports required to satisfy a target global false alarm and miss detection probabilities in T-CSS is shown to be higher than that in EE-CSS. Expressions for the average steady-state trust values of SUs and the average total number of sensing reports transmitted by the SUs to the SUBS in EE-CSS are derived. The global false alarm (FA) and miss detection (MD) probabilities are analyzed for a commonly used decision fusion technique.
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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".