Iterative Filtering PAPR Reduction Method for OFDM Modulation in Fifth-generation Cellular Networks
Bibliographic record
Abstract
Background & Objective: Orthogonal Frequency Division Multiplexing (OFDM) is an ordinarily used waveform in the fifth generation (5G) cellular networks for uplink links. However, there is a prominent disadvantage in the form of a high peak-to-average power ratio (PAPR) which yields distortion in the timing signal generated at the output of the high-power amplifier (HPA). Methods: A new method called Iterative Filtering PAPR Reduction (IFP) has been suggested in this paper and maintains backward compatibility. The basic concept behind this algorithm is to obtain a filter based on a constant-envelope signal that is intimate to the original signal as far as power is concerned. The constant-envelope signal is then compared to the output between the product of the convolution of the original signal with the filter in question, allowing for the calculation of the impulse response of the filter. Such a process can be repeated several times with different filters to realize the best reduction in PAPR. Results: The simulated results of the IFP method proved better performance in terms of PAPR reduction, Bit Error Rate (BER), and computational complexity requiring two iterations only. We can see a gain of 3.1dB in terms of PAPR reduction, 17dB in terms of BER, and a factor of 33 times in terms of computational complexity compared to the TR method. The Complementary Cumulative Complementary Density Function (CCDF) has assisted in measuring and improving the PAPR performance of the system. Conclusion: The theoretical analysis shows that a single iteration (NF= 1) is sufficient, and the simulation results exposed in this paper show a gain of 3.1 dB in PAPR reduction.
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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 | 0.001 |
| 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) | 0.002 | 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".