Non-Orthogonal Transmission and Noncoherent Fusion of Censored Decisions
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Bibliographic record
Abstract
In this paper, we propose a novel signaling scheme and corresponding noncoherent fusion rules for wireless sensor networks. In the proposed scheme, sensors transmit censored decisions to the fusion center using signature vectors. To improve bandwidth efficiency the signature vector length can be chosen smaller than the number of sensors in the network resulting in non-orthogonal sensor channels. We derive the optimum noncoherent likelihood-ratio (LR) based fusion rule as well as a low-complexity energy-based fusion rule for the considered signaling scheme and Rayleigh fading. Furthermore, the performance of the energy-based fusion rule is analyzed. Numerical and simulation results show that with the proposed scheme significant improvements in bandwidth efficiency are possible at the expense of a small loss in power efficiency.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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