PAPR Reduction Techniques for MC-CDMA System
Bibliographic record
Abstract
A complex pseudo-random (PN) sequence is proposed and used in a Multi-carrier Code Division Multiple Access (MC-CDMA) system. The correlation properties of this proposed PN-sequence are examined, illustrated, and compared with the well-known Walsh and Gold sequences. The Peak-to-Average Power Ratio (PAPR) properties of signals in an MC-CDMA system with complex PN-sequence are examined. Also, techniques such as clipping based on symbol statistics and improved Partial Transmit Sequence (PTS) are used for PAPR reduction in the MC-CDMA system. Numerical results show that the proposed complex PN-sequence exhibits good correction properties and the PAPR performance of MC-CDMA system is a function of the PN-sequence and the PAPR reduction method used. It is shown that significant PAPR reduction can be attained using the clipping based on symbol statistics technique and it is controlled by the value of the clipping factor (a) used in the system. In addition, when the improved PTS technique is applied for PAPR reduction in the MC-CDMA system, a PAPR reduction of nearly 4. 6 dB can be achieved compared to the system without this technique.
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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) | 0.001 | 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".