An Optimal Low Complexity PAPR Reduction Technique for Next Generation OFDM Systems
Bibliographic record
Abstract
Orthogonal frequency division multiplexing (OFDM) is an efficient multi-carrier modulation technique that underlies most of the current and probably future high-speed wireless communication systems. However, the OFDM waveform is characterized by a high peak-to-average power ratio (PAPR), especially when a large number of subcarriers are used. A high PAPR is a major waveform defect since it leads to non-linear distortion when passing through the transmitter's power amplifier. Most of the PAPR reduction techniques found in the literature reduce the PAPR mainly at the cost of either excessive computational complexity or degrading the transmission bit error rate (BER). We propose a low-complexity technique for PAPR reduction based on linear scaling of a portion of signal coefficients by an optimal factor. This paper is backed up by the extensive analysis of various performance metrics, which leads to optimal choices of key parameters and hence maximum achievable gains. The analytic and simulated results show that the proposed technique is capable of reducing the PAPR effectively with negligible effect on BER in return for a slight reduction in data rate. For example, for 1024 subcarriers, the PAPR can be reduced from 13 dB to below 7.4 or 6.9 dB, in return for only 1% or 2% reduction in data rate, respectively. In addition, the achievable PAPR varies very slightly in response to increasing the number of subcarriers. This offers a highly competitive and flexible tradeoff compared with those provided by current techniques found in the literature. Therefore, this technique has a very good potential for practical application in current and future OFDM-based systems, especially those which employ a very large number of subcarriers, such as LTE, DVB-T2, and 5G systems.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".