A Joint PAPR Reduction and Digital Predistortion Based on Real-Valued Neural Networks for OFDM Systems
Bibliographic record
Abstract
The peak-to-average power ratio (PAPR) reduction and linearization techniques are both effective methods to improve the efficiency of the transmitter in digital video broadcasting (DVB) systems. Traditional methods deploy the PAPR reduction model and the linearization model, respectively, without considering their mutual influence. Therefore, the joint optimizations of PAPR reduction and linearization techniques are proposed. However, these methods train the PAPR reduction model and the linearization model based on the time-division training method. It is difficult to meet the requirements of multiple objectives. To address this issue, this paper proposes a joint PAPR reduction and digital predistortion (DPD) method using the real-valued neural network (RVNN) for Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed method jointly trains the PAPR reduction function and the DPD function with multi-objective optimization, to achieve PAPR reduction and linearization simultaneously. Especially, this method unifies the PAPR reduction function and the DPD function into one model based on RVNN, and no extra processing is required at the receiver. Compared with the traditional methods, the experimental results show that the proposed method has superior performance in PAPR, adjacent channel power ratio (ACPR) and bit error rate (BER), while having lower computational complexity.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".