Robust Semi-Blind Packing Ratio Estimation for Faster-Than-Nyquist Signaling
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$10^{-4}$are about 7 and 3 dB, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".