Avoiding PAPR degradation in Convolutional Coded OFDM Signals
Bibliographic record
Abstract
Orthogonal Frequency Division Multiplexing (OFDM) is a promising technique for high bit rate transmission in wireless communications systems. Convolutional coding is often used in conjunction with OFDM to improve the reliability of transmission. However, in this paper, we show that the peak to average power ratio (PAPR) statistics of convolutional coded OFDM (C-COFDM) signals can be significantly degraded when compared with uncoded-OFDM. We have found that this degradation can occur for code rates R < 1/2 and relatively low constraint lengths K=3 through K=6. For these codes, it is especially important to use PAPR reduction techniques to counteract this degradation. We further demonstrate that the use of Guided Scrambling (GS) as a PAPR reduction technique does not help in all of the cases, and therefore that reduction techniques applied after convolutional encoding, such as Selected Mapping (SLM), should be used instead.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".