Optimized Canceling Signals for PTS Schemes to Improve the PAPR of OFDM Systems Without Side Information
Bibliographic record
Abstract
This paper introduces a novel blind partial transmission sequence (PTS) scheme to lower the peak-to-average-power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) systems. Unlike existing PTS schemes in which the first sub-block (SB) is preserved as a phase reference for other SBs, we propose to add an optimized canceling signal (CS) to the first SB to further reduce the PAPR. The CS is designed such that they can be reconstructed by the receiver, and subtracted from the received signals before demodulation without requiring side information (SI). Since errors in reproducing the CS at the receiver can degrade the error performance, we design a novel CS protection mechanism specifically to protect the reconstruction of the CS. The proposed method is shown to significantly reduce the PAPR and symbol error rate (SER) without sacrificing the data rate due to using SI as many other existing PTS schemes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".