A New Algorithm for Peak/Average Power Reduction in OFDM Systems
Bibliographic record
Abstract
We present a new method (MMSE-threshold) for peak/average power reduction. This technique is derived from a constellation shaping algorithm, where the constellation points with lower average energy are selected from a larger set of points. There are multiple choices available to select the points with lower peak energy for a given sequence of data bits, and this flexibility is used to reduce the peak to average power ratio (PAPR). Subsequently, this selection algorithm, which is formulated in terms of a zero-one quadratic problem, is optimized by the semidefinite programming algorithm (SDPA). Simulation results show that the PAPR of SDPA is noticeably better than MMSE-Threshold, while the complexity of MMSE-Threshold is smaller than that of SDPA. MMSE-Threshold is compared with alternative techniques reported in the literature. We show that, while we obtain a PAPR that is similar or better than those of the compared techniques, the complexity of MMSE-Threshold is low. In addition, the MMSE-threshold method results in about 1 dB shaping gain (reduction in the average energy) with less than 1% rate loss for PAPR reduction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".