A Permutated Partial Transmit Sequence Scheme for PAPR Reduction in Polar-Coded OFDM-IM Systems
Bibliographic record
Abstract
In this article, a permutated partial transmit sequence (PTS) scheme is proposed to reduce the peak-to-average-power ratio (PAPR) in polar-coded orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems without side-information (SI) transmission. For generating candidate signals, the proposed PTS method combines two operations of phase rotations and frequency-domain permutations by implementing circular shifting and factor multiplications in time domain, where the phase rotation factors are determined by the frozen bits of polar codes. Additionally, according to the transmitting and polar-coded structures, an SI-free decoder based on successive cancellation lists (SCL) algorithm is developed at the receiving end. Compared with conventional PTS and existing PAPR reduction schemes, the proposed PTS scheme exhibits significantly higher PAPR reduction performance with less complexity. Based on stimulation results, the proposed SI-free receiver is able to achieve error performance similar to that with the receiver utilizing perfect side information in both Additive White Gaussian noise (AWGN) and frequency selective channels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".