An Interpolation and Resampling Framework for Efficient Reduction of Peak-to-Average Power Ratio in OFDM Systems
Bibliographic record
Abstract
Conventional OFDM systems notoriously suffer from a high peak-to-average power ratio (PAPR), which makes hardware implementation problematic. We propose a general framework for reducing PAPR using a combination of time-domain interpolation and resampling. In essence, this combination allows for both pulse shaping as well as phase changes to be performed. It is demonstrated that the framework offers not only improved flexibility, but also reduced computational complexity compared to existing methods. The former benefit is due to the availability of various user-controllable parameters in the framework, while the latter is a result of performing PAPR reduction directly in the time domain. We also show that the accompanying channel estimation and equalization procedures can be achieved in a practical manner, with only modest modifications. The simulation results illustrate that, even though the complexity requirements are substantially lower, the PAPR reduction capability of this framework is comparable to that of existing PAPR reduction methods.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".