Bibliographic record
Abstract
A cooperative spectrum‐sensing problem has been considered here, in which a network of secondary users (SUs) assists a fusion centre (FC) in detecting the presence of a primary user (PU). Assuming communication links with unlimited capacity of the SUs and FC and known channel gains and noise variances, the optimal Neyman–Pearson detector is derived. Assuming limited capacity between the SUs and FC and unknown channel gains and noise variances, three different spectrum‐sensing protocols have been studied; namely, amplify‐and‐forward (AF), compress‐and‐forward (CF) and detect‐and‐forward (DF), where each SU transmits an amplified or compressed version of its observed signal, or its local binary decision to the FC, respectively. The Edgeworth expansion is used to obtain novel expressions for the performance of these detectors. The theoretical analysis and numerical results show that the CF and OR detectors outperform the other proposed detectors. In addition, the simulation results show that the performance of the coded protocols (CF and DF) improves as the number of samples increases or as the noise variance at the SUs decreases, whereas such a behaviour cannot be guaranteed in the uncoded AF protocol.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".