A Hyperbolic Secant-Based Pulse for Enhanced FTN Signaling in 5G/6G Systems
Bibliographic record
Abstract
This paper investigates pulse shaping optimization in faster-than-Nyquist (FTN) signaling to enhance spectral efficiency and reduce inter-symbol interference (ISI). We propose a novel hyperbolic secant root-raised cosine (HS-RRC) pulse, obtained by multiplying the conventional RRC pulse with a hyperbolic secant function. The HS-RRC pulse exhibits improved time localization, reduced ISI, and enhanced bit-error-rate (BER) performance. Extensive simulations show that, compared to the standard RRC pulse, the HS-RRC achieves notable gains in time-bandwidth product and BER across various roll-off factors. Unlike prior hyperbolic-based approaches tyrovolas2022novel, our multiplicative shaping method offers both improved spectral containment and implementation simplicity. For selected temporal compression factors (τ), the HS-RRC achieves up to a 4 dB SNR improvement at τ=0.5 and 2 dB at τ=0.8, at BER levels of 10-1 and 10-4. These enhancements significantly boost system performance, demonstrating the pulse’s ability to reduce ISI and improve system robustness under varying compression conditions, with promising applications for next-generation 5G/6G networks.
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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 | 0.000 |
| 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".