Analysis of Clipping Noise and Tone-Reservation Algorithms for Peak Reduction in OFDM Systems
Bibliographic record
Abstract
Orthogonal frequency division multiplexing (OFDM) suffers from a high peak-to-average power ratio (PAR). Tone reservation is a popular PAR reduction technique that uses a set of reserved tones to design a peak-canceling signal. In a previous paper by Krongold and Jones, an active-set approach was developed to efficiently compute the peak-canceling signal. In this paper, we consider the use of clipping noise, which is generated when the OFDM signal is clipped at a predefined threshold, to design the peak-canceling signal. To this end, the clipping noise is analyzed as a series of parabolic pulses under tone-reservation constraints. The single-pulse case and the multiple-pulse case are treated. The analysis explains peak regrowth and the constancy of the clipping noise power spectrum over the whole OFDM band. Moreover, the clipping noise at the end of several clipping and filtering iterations is shown to be approximately proportional to that generated in the first iteration. The constant of proportionality is estimated via the level-crossing theory for high clipping thresholds. Using this analysis, a constant-scaling algorithm and an adaptive-scaling algorithm are proposed for tone reservation. These algorithms scale the filtered first-iteration clipping noise to compensate for peaks that are above the threshold. The simulation results show that the proposed algorithms achieve a larger peak reduction and lower complexity than the active-set algorithm.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".