On the Comparative Performance Analysis of PAPR Reduction Techniques in OFDM System
Bibliographic record
Abstract
Selected Mapping (SLM) and Partial Transmit Sequence (PTS) are two very well-known Peak-to-average Power Ratio (PAPR) reduction techniques for Orthogonal Frequency Division Multiplexing (OFDM) systems. Both these schemes show good PAPR reduction capabilities. However, for any PAPR reduction technique, the nature of spectral occupancy and the associated computational complexity also need to be taken into account when the overall performance is considered. In this paper, our goal is to perform a comparative performance analysis of SLM and PTS techniques by considering these three parameters, i.e. PAPR reduction, computational overhead and spectral compactness. For this, we at first look for the optimum values in terms of number of sequences in SLM and no. of sub-blocks in PTS. And then based on this finding, we perform performance analysis. Our finding shows that, PTS outperforms SLM when compared on the parameters mentioned above. Finally, to explore the feasibility of further improvement, we apply Walsh-Hadamard Transform to PTS scheme and show that it further reduces PAPR and improves spectral compactness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".