SPCp1-04: Bit Mapping and Error Insertion for FEC Based PAPR Reduction in OFDM Signals
Bibliographic record
Abstract
This paper proposes a new method for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems using forward error correction (FEC). The scheme combines the intentional insertion of correctable errors into the data stream with a specialized bit mapping. The latter deviates from traditional Gray encoding of bits into modulation symbols in order to maximize the benefits of FEC-based PAPR reduction. Using the code redundancy, the proposed scheme achieves significant reduction in PAPR and satisfactory bit error rate (BER) performance at the expense of acceptable computational complexity. Specifically, the complementary cumulative distribution function (CCDF) of the coded signal shows about a 5dB improvement over the original OFDM signal. In addition, BER performance in an additive white Gaussian noise demonstrates trade-offs between reduction in PAPR and remaining error correction capability of the deployed codes.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".