Pre-equalized Faster-than-Nyquist Transmission
Bibliographic record
Abstract
Faster-than-Nyquist (FTN) transmission applies non-orthogonal linear modulation to increase spectral efficiency compared with the well-known orthogonal transmission at Nyquist rate. This comes at a price of inter-symbol interference (ISI), which usually is equalized through receiver processing. In this paper, we investigate the alternative approach of pre-equalization at the transmitter. First, we consider Tomlinson-Harashima precoding (THP) for FTN and propose two novel soft demapping algorithms to generate the soft-input for the error-correction decoder. The developed demappers effectively compensate the modulo-loss associated with conventional THP transmission. Second, we propose a linear pre-filtering strategy to pre-equalize the ISI induced by FTN. We show that the linear pre-equalization approach is equivalent to an orthogonal transmission with a modified pulse shape. It thus yields the optimal error-rate performance while affording higher spectral efficiency. We validate our proposed precoding algorithms through computer simulations of a coded coherent optical communication system as a practical application example for FTN.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".