Novel PAPR Reduction Method for OFDM Signals With Tone Reservation and Index Modulation
Bibliographic record
Abstract
This paper introduces a novel method to minimize the peak-to-average power ratio (PAPR) and at the same time enhance the data rates of orthogonal frequency-division multiplexing (OFDM) systems by combining tone reservation (TR) and index modulation (IM). Unlike conventional TR methods, in which a number of tones (or subcarriers) with fixed positions are reserved for canceling the peaks in OFDM signals, the TR-IM method treats the positions of the reserved tones (TR tones) as random and embeds extra information in their positions using IM. In the proposed system, the amplitudes of the TR tones are quantized with a novel quantization method, which not only helps the receiver distinguish between data tones and TR tones, but also enables the TR tones to carry data on their amplitudes. Based on that, we propose a novel forward error correction (FEC) structure to increase the reliability of detection without requiring extra overhead. The proposed FEC design encodes the IM activation pattern rather than the index data bits, and carries the resulting parity bits with a novel mechanism that exploits the extra bits carried on the amplitudes of the TR tones. Simulation results show that, not only does our proposed system have significantly higher data rates, but it can also achieve remarkable PAPR reduction performance as well as a lower bit error rate than the original OFDM systems under the influence of the non-linear distortion caused by power 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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".