On Spectrum Broadening of Pre-Coded Faster-Than-Nyquist Signaling
Bibliographic record
Abstract
The faster-than-Nyquist (FTN) signaling has been of interest in the research literature due to recent advances in pre-coding and equalization techniques allowing practical removal of the intersymbol interference (ISI). Since the structure of ISI is deterministic in the FTN signaling, a data pre-coding at the transmitter to combat ISI is of practical importance. It is shown, however, that such pre-coding in FTN can significantly broaden the transmission spectrum and alter the shape of the power spectral density. In this paper, we analyze the power spectral density of convolutionally pre-coded FTN signals on linear time-invariant channels. We also identify sufficient conditions on the pre-coding coefficients for preventing the spectrum broadening. Simulation results with several pulse shapes are provided which agree with the analysis. The analysis and the simulation show that for many practically used pulses the pre-coded FTN suffers from the spectrum broadening. This suggests that pre-coding in the FTN signaling must be handled with care.
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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".