Digital Predistortion Function Synthesis using Undersampled Feedback Signal
Bibliographic record
Abstract
This letter presents a new approach to synthesize the digital predistortion (DPD) function using an undersampled feedback signal. First, an expression for the DPD update algorithm that accommodates undersampling of the feedback signal is derived. This includes a direct learning algorithm that iteratively identifies the DPD function coefficients. Then, a delay estimation and alignment algorithm that employs a fractional delay filter is presented for estimating and compensating the non-integer delay between the sampled input and undersampled output signals of the power amplifier (PA). The new proposed approach is found to have comparable linearization capability compared to a conventional full-rate based indirect-learning DPD, even with a significantly undersampled feedback signal. For instance, it was successfully applied to linearize a 20 W GaN Doherty PA driven by a wideband modulated signal of up to 80 MHz bandwidth, and yield an ACLR of -49 dBc after linearization using a complex feedback signal sampled at 80 complex MSPs as opposed to 400 complex MSPs that would be required for conventional sampling.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".