CF technique with CPM mappers in OFDM systems for reduction of PAPR
Bibliographic record
Abstract
An OFDM system with CPM mappers is considered. Such a system in general is known to enhance bit error rate performance by virtue of the memory introduced by the CPM mapper compared to OFDM systems with conventional memoryless mappers such as BPSK and QPSK. In this paper, the ability of CPM mappers in an OFDM system to reduce Peak-to-Average Power Ratio (PAPR) is examined as a function of modulation parameters of mappers used in the system. Various subclasses of CPM mapper such as single-h CPFSK, multi-h CPFSK, and asymmetric multi-h CPFSK are also considered. Next, these mappers in conjunction with Clipping and Filtering (CF) technique are considered and it is shown that repeated clipping and frequency domain filtering in OFDM systems can significantly reduce the PAPR of the transmitted signal. The technique causes no increase in out-of-band power and significant PAPR reduction can be achieved with only moderate levels of clipping. A comparison of PAPR reduction capability of CPM mappers relative to memoryless BPSK mappers in an OFDM system is presented. It is noted that, in general, CPM mappers offer superior PAPR performance compared to memoryless mappers in an OFDM system.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".