Cooperative composite sequential detection and its application in spectrum sensing
Bibliographic record
Abstract
In this study, the authors present a method to derive sequential detector (SD) for a general class of composite hypothesis problems. The authors first explain how the SD is constructed by employing a ‘weight function’. Then, the authors employ this method in the cooperative spectrum sensing (SS) in cognitive radio networks where the primary user transmits a phase shift keying (PSK) signal with unknown complex amplitude in additive white Gaussian noise. The noise power is assumed known in the first scenario and unknown in the second one. To evaluate the performance of the resulting SDs, the authors obtain the required average sample number (ASN) function to meet the bounds of false alarm and missed detection probabilities through some numerical evaluations. The results illustrate that the average sensing delay of the proposed SDs are less than the required number of observations in the traditional fixed sample size detectors. In the proposed SDs, the authors also demonstrate that the increase of signal‐to‐noise ratio leads to decrease of ASN.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".