Sensor Allocation and Quantization Schemes for Multi-Band Cognitive Radio Cooperative Sensing System
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Bibliographic record
Abstract
We investigate cooperative sensing schemes for a cognitive radio (CR) network operating in multiple primary bands. The CR spectrum sensors use energy detection to identify the presence of the primary signals. We consider a parallel fusion architecture in which all the sensing devices send their binary quantized sensing information to an access point, which then applies a fusion rule to determine the presence of a primary user in each band. In this paper, we first propose schemes to assign sensors to various primary bands. We then study efficient quantization schemes when `OR' fusion rule is used in each band at the access point. We show that the optimal quantization scheme is, in general, non-convex and propose a suboptimal solution based on convex restriction of the original problem. We further study quantization schemes for general k-out-of-N fusion rule. We compare the performance of the proposed schemes using simulations.
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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 | 0.001 | 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 it