Clipping-Noise Guided Sign-Selection for PAR Reduction in OFDM Systems
Bibliographic record
Abstract
The peak-to-average power ratio (PAR) of orthogonal frequency division multiplexing (OFDM) systems can be reduced by using an optimal set of subcarrier signs. However, this sign selection is a hard discrete optimization problem. We therefore consider the use of the clipping noise, generated when the OFDM signal is clipped at a given threshold level, to find a good set of signs. The key idea of clipping-noise guided sign-selection (CGS) is to iteratively flip the signs of those subcarriers with high levels of clipping noise. In each iteration, the key task is to determine the number and locations of such subcarriers. We develop suitable criteria for this task and derive CGS algorithms that can handle both unitary (e.g., M-ary phase shift keying) and nonunitary (e.g., M-ary quadrature amplitude modulation) signal constellations. The simulation results show that the PAR reduction of CGS is about 1 dB larger than that of derandomization and tone reservation for a 256-subcarrier system, and is about 1-2 dB larger than that of partial transmit sequence (PTS) and selective mapping (SLM). CGS also removes the error floor due to nonlinear amplifiers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".