Collaborative Spectrum Sensing for Cognitive Radio: Diversity Combining Approach
Why this work is in the frame
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Bibliographic record
Abstract
In this paper it is shown that cyclostationary spectrum sensing for Cognitive Radio networks, applying multiple cyclic frequencies for single user detection can be interpreted (with some assumptions) in terms of optimal incoherent diversity addition for “virtual diversity branches” or SIMO radar. This approach allows proposing, by analogy to diversity combining, suboptimal algorithms which can provide near optimal characteristics for the Neyman-Pearson Test (NPT) for single user detection. The analysis is based on the Generalized Gaussian (Klovsky-Middleton) Channel Model, which allows obtaining the NPT noise immunity characteristics: probability of misdetection error (PM) and probability of false alarm (Pfa) or Receiver Operational Characteristics (ROC) in the most general way. Some quasi-optimum algorithms such as energetic receiver and selection addition algorithm are analyzed and their comparison with the noise immunity properties (ROC) of the optimum approach is provided as well. Finally, the diversity combining approach is applied for the collaborative spectrum sensing and censoring. It is shown how the diversity addition principles are applied for distributed detection algorithms, called hereafter as SIMO radar or distributed SIMO radar, implementing Majority Addition (MA) approach and Weighted Majority Addition (WMA) principle.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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