Lowering the Peak to Average Power Ratio by using the PTS method for high-speed application systems
Bibliographic record
Abstract
Orthogonal Time-Frequency Space is a promising modulation waveform for beyond-5G or 6G communication systems: superior robustness in high mobility and multipaths is achieved from the delay Doppler domain; however, one severe challenge of this OTFS was its high peak-to-average ratio of power, constraining the linearity of efficient power amplifiers at the transmitter which degrades further the overall performance of the entire system. This paper presents a new application of the Partial Transmit Sequence (PTS) method in reducing the PAPR for OTFS systems, which will ensure compatibility with future wireless networks that have stringent requirements. The PTS is a distortion less technique that divides the input signal into sub-blocks and applies optimized phase factors to reduce the peak power of the signal. It will help to eliminate computational complexity existing with traditional PTS and incorporates enhanced optimization techniques like heuristics algorithm, reduced space searching, hence ensuring a dramatic PAPR reduction with complexity that is minimized. Simulation analysis proved the ability of this proposed work for achieving efficient results in lowering the PAPR with relatively little effect on the bit error rate performances. The results demonstrate the feasibility of PTS-based PAPR reduction to further improve the energy efficiency, spectral efficiency, and reliability of OTFS systems, thereby making it an attractive solution for beyond 5G communication scenarios. The numerical results reveals that the proposed PTS obtain an energy saving of $\mathbf{2 5 \%}$ and reduce the PAPR by 3.9 dB to 5.8 dB.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".