Robust Semi-Blind Packing Ratio Estimation for Faster-Than-Nyquist Signaling
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Bibliographic record
Abstract
Accurate packing ratios are indispensable for the faster-than-Nyquist (FTN) signaling; therefore, we propose two robust semi-blind packing ratio estimation algorithms in this letter. Concretely, we construct a pilot-based FTN transmission scheme over the channel with frequency offset and phase noise. We conduct correlation operations on downsampled pilots, assuming a predetermined downsampling factor. To mitigate the impacts of frequency offset and phase noise, we employ differential post-detection integration (DPDI) and differential-generalized post-detection integration (DGPDI) for correlation operations. The packing ratio is estimated by selecting the downsampling factor corresponding to the maximum decision value. Simulation results demonstrate that the coherent correlation yields optimal estimation accuracy in the absence of frequency offset and phase noise. Otherwise, the estimation algorithms using DPDI and DGPDI, i.e., PRE-DPDI and PRE-DGPDI, realize higher estimation accuracy than that using the coherent correlation. When the packing ratio and the normalized frequency offset are 0.8 and 0.2, signal-to-noise ratios required for PRE-DPDI and PRE-DGPDI to achieve a probability of false alarm of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$10^{-4}$ </tex-math></inline-formula> are about 7 and 3 dB, respectively.
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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 | 0.001 | 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