A PAPR Reduction for OFDM Signals Based on Self-Adaptive Multipopulation DE algorithm
Bibliographic record
Abstract
One of major drawbacks of orthogonal frequency division multiplexing (OFDM) systems is the high peak-to-average power ratio (PAPR). A signal with high PAPR leads to nonlinear distortion caused mainly by power amplifiers in wireless transmitters. Partial transmit sequence (PTS) is one of the most attractive methods to reduce the PAPR in OFDM systems. It achieves considerable PAPR reduction without distortion, but it requires an exhaustive search over all the combinations of the given phase factors, which results in a computational complexity that increases exponentially with the number of partitions. For this optimization problem, we propose in this paper a suboptimal PTS method based on the self-adaptive multipopulation differential evolution algorithm (SAMDE). The self adaptation of control parameters and structured population, is able to obtain high quality solutions with low computational cost by evolving each sub-population of individuals over successive generations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".