Pre-equalized Faster-than-Nyquist Transmission
Why this work is in the frame
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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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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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