Iterative Invertible Clipping Method for PAPR Reduction in Wireless MC-CDMA Communication Systems
Bibliographic record
Abstract
Background & Objective: One of the disadvantages of Multi-carrier Code Division Multiple Access (MC-CDMA) system is the high value of Peak to Average Power Ratio (PAPR). This paper proposes an approach called Iterative Invertible Clipping Method (IICM) to address the PAPR for MC-CDMA signal based on wireless communication system characteristics. This is mainly concentrated in clipping function making use of a polynomial of degree three at the transmitter, and its inverse at the receiver, to recover the original signal. Method: A Fast Fourier Transform (FFT)-based frequency domain filter is employed to reduce the overall peak regrowth which is supported by measuring the Adjacent Channel Power Ratio (ACPR) after soft clipping and filtering. This technique is compared with Ragusa method using a polynomial of degree 5. Result: The simulated results of the proposed method showed better performance in terms of PAPR reduction, Bit Error Rate (BER), and computational complexity requiring two iterations only. The Complementary Cumulative Complementary Density Function (CCDF) is served to measure and improve PAPR performance of the system. Conclusion: The channel coding process is applied to strengthen the obtained results in the Additive White Gaussian Noise (AWGN) channel and fading channel (Rayleigh). The High-Power Amplifier (HPA) is used for validation purposes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".