Sparse Memory Taps-Injected Neural Network for Digital Predistortion of RF Power Amplifiers
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Bibliographic record
Abstract
Neural network (NN) has been widely applied in radio frequency power amplifier (RFPA) digital predistortion (DPD) due to its robust nonlinear fitting capability. Conventional NN DPD architectures typically design input layers based on the fading characteristics of the memory effect, yet they overlook redundant memory taps within the memory depth, potentially resulting in limited performance. To overcome this issue, we thoroughly redesign the input layer by injecting high-contribution memory taps chosen via a greedy algorithm. Moreover, the sparse memory taps (SMTs)-injected NN (SMTINN) model is proposed, which can accurately capture the memory effect of RFPAs while significantly reducing computational cost. Experimental results on a 3.5-GHz RFPA with 100-MHz signal bandwidth demonstrate that the SMTINN can achieve more optimal modeling accuracy and linearization performance than state-of-the-art NN DPD architectures while achieving 42% parameter reduction.
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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 | 0.000 | 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