PAPR Reduction using Frequency Domain Multiplexed Pilot Sequences
Bibliographic record
Abstract
We investigate the feasibility of applying the peak-to-average power ratio (PAPR) reduction method using pilot sequences, originally proposed for orthogonal frequency division multiplexing (OFDM) signals, for single-carrier (SC) signals with frequency domain multiplexed (FDM) pilots. The idea is to select the FDM pilot sequence with which the transmitted signal produces the lowest PAPR. We also investigate the applicability of the sum of square error (SSE) selection rule for high order modulation of SC signals. The SSE rule selects the pilot sequence which produces the minimum SSE between the transmitted signal and a pre-defined threshold, proportional to the saturation level of a high power amplifier (HPA). It is found that for both SC and OFDM systems, the PAPR reduction capabilities of orthogonal Walsh-Hadamard (W-H) sequences and cyclic shifted Chu (CS-Chu) sequences are similar for small block size, but not for large block size. Using CS-Chu sequences produces better PAPR reduction capability. With an appropriate choice of the value of input backoff power of a HPA, the SSE selection rule produces similar results as that of the minimum PAPR selection rule. The effects of out-of-band radiation for SC and OFDM signals with PAPR reduction using FDM pilot sequences after HPA depends on the amount of non-linearity portion of the HPA. The out-of-band radiation improvement is obvious for a HPA that approximates a linear clipper.
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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".