Energy Efficient Collaborative Spectrum Sensing Based on Trust Management in Cognitive Radio Networks
Bibliographic record
Abstract
An energy efficient collaborative spectrum sensing (EE-CSS) protocol, based on trust management, is proposed. The protocol achieves energy efficiency by reducing the total number of sensing reports exchanged between the honest secondary users (HSUs) and the secondary user base station (SUBS) in a traditional collaborative spectrum sensing (T-CSS) protocol. It is shown that the minimum total number of sensing reports required to satisfy a target global false alarm (FA) and missed detection (MD) probabilities in T-CSS is higher than that in EE-CSS. Expressions for the steady-state average SU trust value τ̅ and total number N̅ of SU sensing reports transmitted are derived, as is an expression for the energy consumption, in EE-CSS and T-CSS. The global FA and detection probabilities Qfand Qdare obtained for a commonly used decision fusion technique. The impact of link outages on τ̅, N̅, Qf, and Qdis also analyzed. The results show that the energy consumption in EE-CSS can be much lower compared to that in T-CSS for long range communications where the transmit energy is dominant.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".