Pulse Shaping for Faster-than-Nyquist to Enable Low-Complexity Detection
Bibliographic record
Abstract
In this paper, we consider the problem of designing pulse shaping for faster-than-Nyquist (FTN) signaling. The proposed pulse shape is based on the optimization constraint such that the resulting intersymbol interference (ISI) matrix possesses super increasing sequence criteria. The low-complexity symbol-by-symbol sequence estimator which relies on this specific criteria can perform reasonably well even for lower values of time packing$\tau=0.6$. We formulate the problem as a finite impulse response (FIR) design and propose a second-order code program (SOCP) based solution. Simulation results show that with our proposed pulse shaping design for$\tau=\{0.8,0.6,0.5\}$, we obtain 2 dB or more performance improvement at a bit-error-rate (BER) of 10–3compared to the state-of-the-art existing detection schemes over an additive white Gaussian noise (AWGN) channel.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".