Spectrum Broadening of Faster-Than-Nyquist Signaling Using GTMH Precoding
Bibliographic record
Abstract
As a promising technique to eliminate the intersymbol interference (ISI) caused by the faster-than-Nyquist (FTN) signaling, the G-to-minus-half (GTMH) precoding could result in severe spectrum broadening and reduce the spectrum efficiency. Combining the baseband-shaping filter with the precoding matrix, we define the transfer function of the FTN signaling using the GTMH precoding. By further analyzing the effect of the GTMH precoding on the transfer function, we prove how the FTN signaling makes its ISI matrix ill-conditioned. Then, we reveal the relationship between the transfer function and the eigenvalues of the ISI matrix. The derivation shows that due to the minimal eigenvalues of the ill-conditioned ISI matrix, the inverse operation of the GTMH precoding gives in the maximal elements for the transfer function, thereby resulting in the spectrum broadening. Furthermore, we present and validate the spectrum-broadening ratio and the maximum spectrum efficiency improvement of the GTMH precoding, which serve as additional evaluative tools for the GTMH precoding of the FTN signaling, complementing the conventional bit-error-ratio-based evaluation methodology.
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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 | 0.001 |
| 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".