Deep Unfolding Network for PAPR Reduction in Multicarrier OFDM Systems
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Bibliographic record
Abstract
In this letter, we propose a Deep Unfolding Network called PR-DUN to reduce the peak-to-average power ratio (PAPR), which is a thorny problem in Orthogonal Frequency-Division Multiplexing (OFDM) systems. The proposed multi-layer model is constructed by unrolling an iterative algorithm resulting in layers with trainable parameters, which are optimized to minimize a loss function related to the PAPR value. The deep unfolding model uses the backpropagation algorithm to transfer gradients backwards to adjust parameters. Furthermore, the proposed scheme can accommodate any transmit power constraint, and therefore can control the power increase caused by the auxiliary signal. Simulation results show that the proposed PR-DUN model achieves a larger PAPR reduction and a smaller bit error rate while being less computationally intensive than related solutions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it