On partial transmit sequences for PAR reduction in OFDM systems
Bibliographic record
Abstract
Partial transmit sequences (PTS) is a popular technique to reduce the peak-to-average power ratio (PAR) in orthogonal frequency division multiplexing (OFDM) systems. PTS is highly successful in PAR reduction and efficient redundancy utilization, but the considerable computational complexity for the required search through a high-dimensional vector space and the necessary transmission of side information (SI) to the receiver are potential problems for a practical implementation. In this paper, we revisit PTS for PAR reduction and tackle these two problems. To address the complexity issue, we formulate the search problem of PTS as a combinatorial optimization (CO) problem. This enables us to (i) unify various search strategies proposed earlier in the PTS literature and (ii) adapt efficient search algorithms known from the CO literature to PTS. We also propose a modified PTS objective function, which reduces the number of multiplications required for PTS. Numerical results show that, perhaps surprisingly, simple random search yields the best performance-complexity tradeoff for moderate PAR reduction, whereas two novel CO-based methods excel if close-to-optimum PAR reduction is desired. The SI transmission problem is solved by a simple preprocessing of the data stream before PAR reduction. This preprocessing introduces the minimal possible redundancy and allows SI embedding without affecting the PAR reduction capability of PTS or causing peak regrowth.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".