Composite Hypothesis Testing for Cooperative Spectrum Sensing in Cognitive Radio
Bibliographic record
Abstract
In this paper, we present a composite hypothesis testing approach for cooperative spectrum sensing. We derive the optimal likelihood ratio test (LRT) statistic based on the Neyman-Pearson (NP) criterion at the fusion center for both hard (one-bit) and quantized (multi-bit) local decisions. We show that the LRT statistic depends on the modulation type and second- and fourth- order statistics of the primary signal. However, such side information is not commonly available to the secondary network. Therefore, we propose to apply composite hypothesis testing methods, such as the Rao test, which do not require any prior knowledge about the primary signal, in a cooperative sensing scenario. We derive a modified Rao test statistic for decision making at the fusion center for both cases of hard and quantized local decisions. We also apply the locally most powerful (LMP) detector at the fusion center for weak primary signals and derive its corresponding test statistic. These methods are much simpler than the optimal NP-based method and do not require estimation of the primary signal statistics while having a very close performance to the optimal method.
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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 | 0.021 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".