PAPR reduction in OFDM systems using differentially encoded subcarriers
Bibliographic record
Abstract
A peak-to-average power ratio (PAPR) reduction technique that exploits the principle of differential encoding of subcarriers is proposed and investigated. The absolute maximum sample of the time-domain OFDM symbol is chosen as the reference to carry out the differential encoding process at the transmitter. A real multiplier (??) is applied to this reference to achieve appropriate PAPR level. Information about the proposed reference, however, is required to be communicated to the receiver, which performs the reverse effect to obtain back the original sequence of samples. The effectiveness of the proposed technique is evaluated through extensive computer simulations and complementary cumulative distribution function (CCDF) are obtained as a function of number of subcarriers and modulations. Numerical results confirm that significant reduction in PAPR can be achieved. For example, the proposed technique reduces the 0.1 percent PAPR to 1.5 dB for a 1024 subcarrier OFDM system, resulting in 10.3 dB reduction. Moreover, error performances of the OFDM system before and after applying the proposed technique are investigated using Monte Carlo simulations. Numerical results show that the average bit error rate performance of the proposed system does not degrade relative to the un-encoded system. An investigation of the complexity of the proposed technique with other techniques show that it is quite low complex.
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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 | 0.000 |
| 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) | 0.000 | 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".