Novel PAPR Reduction Algorithms for OFDM Signals
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Bibliographic record
Abstract
This paper proposes two efficient peak-to-average power ratio (PAPR) reduction algorithms for OFDM signals based on the principle of tone reservation. The first algorithm is performed in the time domain, whereas the second algorithm is a new clipping-and-filtering method. Both algorithms consist of two stages. The first stage, which is done off-line, precomputes a set of canceling signals based on the settings of the OFDM system. In particular, these signals are constructed to cancel signals at different levels of maximum instantaneous power that are above a predefined threshold. The second stage, which is online and iterative, reduces the signal peaks using modified versions of the canceling signals constructed in the first stage. When the reserved tones are distributed among the data tones, analysis and simulation results obtained with Data Over Cable Service Interface Specifications (DOCSIS) parameters show that the proposed algorithms achieve slightly better PAPR reduction performance and with significantly lower complexity when compared to the conventional algorithms.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 it