Cooperative Spectrum Sensing Under a Random Geometric Primary User Network Model
Why this work is in the frame
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Bibliographic record
Abstract
We propose a novel cooperative spectrum sensing algorithm for a cognitive radio (CR) network to detect a primary user (PU) network that exhibits some degree of randomness in topology (e.g., due to mobility). We model the PU network as a random geometric network that can better describe small-scale mobile PUs. Based on this model, we formulate the random PU network detection problem in which the CR network detects the presence of a PU receiver within a given detection area. To address this problem, we propose a location-aware cooperative sensing algorithm that linearly combines multiple sensing results from secondary users (SUs) according to their geographical locations. In particular, we invoke the Fisher linear discriminant analysis to determine the linear coefficients for combining the sensing results. The simulation results show that the proposed sensing algorithm yields comparable performance to the optimal maximum likelihood (ML) detector and outperforms the existing ones, such as equal coefficient combining, OR-rule-based and AND-rule-based cooperative sensing algorithms, by a very wide margin.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 it