Erasure Based PAPR Reduction Using Minimally Expanded Constellations in OFDM Systems
Bibliographic record
Abstract
This paper develops a new method for peak-to- average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems using an expanded constellation of points in quadrature amplitude modulation (QAM). On each OFDM subcarrier, the scheme uses a specialized representation of QAM symbols that allows mapping of a group of symbols from the conventional constellation onto an alternative symbol from the external square of signalling points. The design of the expanded constellation maintains the minimum distance between signalling points with a minimal increase in the average power. Its advantage stems from the location of the alternative signalling point being "radially symmetric" to the group of original signalling points in QAM. In systems using coding, the alternative signalling points represent recoverable erasures introduced at the transmitter for the purpose of reducing PAPR. The proposed PAPR reduction method is especially applicable to QAM schemes with high number of constellation points such as M = 64 and M = 256. Significant PAPR reduction is achieved by careful mapping and selection of alternative signalling points for transmission that result in lower PAPRs. Specifically, the complementary cumulative distribution function (CCDF) of the modified OFDM signal shows (5) dB improvement in PAPR at a clipping probability of 10-4over the original OFDM signal.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".