Multi-Band Complexity-Reduced Generalized-Memory-Polynomial Power-Amplifier Digital Predistortion
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Bibliographic record
Abstract
This paper expounds a complexity-reduced generalized memory polynomial (CR-GMP) model for multi-band power amplifier (PA) digital predistortion (DPD). First, PA block diagrams characterizing the behavior of PAs under multi-band stimulus are proposed. Second, CR-GMP forward models are derived from the feedback block diagrams of the PA, driven with both dual- and tri-band signals, leading to a general formulation for PAs driven with multi-band signals. The resulting models are used to linearize two PAs driven with dual- and tri-band signals. The proposed CR-GMP models are compared to a dual-input digital predistortion (2D-DPD) model and a triple-input digital predistortion (3D-DPD) model and show similar linearization performance while requiring fewer coefficients. Due to the presence of cross terms in the dual-band CR-GMP formulation, the proposed model is robust against time-delay misalignment between dual-band signals, whereas the 2D-DPD is not. With a reduced number of coefficients and the presence of cross terms, the proposed CR-GMP models represent excellent candidates for the linearization of highly nonlinear PAs driven with multi-band signals.
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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