Tone Reservation for OFDM Systems by Maximizing Signal-to-Distortion Ratio
Bibliographic record
Abstract
The performance of Orthogonal Frequency Division Multiplexing (OFDM) systems is highly impacted by clipping distortions caused by non-linear amplifiers. One approach known as Tone Reservation (TR) method is to allocate/use a small number of sub-carriers to generate more suited signals and reduce the impact of these non-linear distortions. Traditionally, existing TR algorithms attempt to minimize the Peak-to-Average Power Ratio (PAPR). In this paper, we show that maximization of the Signal-to-Distortion Ratio (SDR) is a better criterion which achieves a better symbol error rate performance. Our results reveal that the proposed approach outperforms in terms of error probability rate for the same transmit power and same order of computational cost. Interestingly, the PAPR value for the proposed algorithm is not better than the state of the art algorithm in which directly optimizes the PAPR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".