Peak-to-average power ratio analysis in multicode CDMA
Bibliographic record
Abstract
This paper analyzes the peak-to-average power ratio (PAPR) problem in multicode-code division multiple access (MC-CDMA) systems. The statistical distribution of PAPR is derived and the achievable PAPR reduction for a given code rate is estimated. We show that the PAPR in MC-CDMA communications systems can be reduced by partial transmit sequence (PTS) and selected mapping (SLM) approaches. In PTS, the subblocks are multiplied by a set of phase factors that are optimized to minimize PAPR. In SLM, several independent data frames are generated and the data frame with the lowest PAPR is selected for transmission. We also show that the BER performance improves when the PAPR-reduced MC-CDMA signal is passed through a nonlinear amplifier. SLM reduces the error floor caused by amplifier nonlinearities and offers an SNR gain of 6 dB at 10/sup -5/ BER with an input back-off of 1 dB.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".