Digitally Assisted Analog/RF Predistorter With a Small-Signal-Assisted Parameter <newline/>Identification Algorithm
Bibliographic record
Abstract
This paper proposes a digitally assisted analog/radio frequency predistorter (ARFPD) and a linear small-signal-assisted parameter identification algorithm suitable for the linearization of power amplifiers driven with wideband and carrier aggregated communication signals. It starts by describing the newly proposed finite-impulse-response assisted envelope memory polynomial (FIR-EMP) model which allows for reduction of hardware implementation complexity while maintaining good linearization capacity and low power overhead. Furthermore, a linear two-step small-signal-assisted parameter identification algorithm is devised to estimate the parameters of the two main blocks of the FIR-EMP model. Measurement results obtained by using the FIR-EMP predistorter demonstrate its excellent linearization capacity when used to compensate for distortion exhibited by gallium nitride Doherty power amplifiers driven by digitally modulated signals with a bandwidth up to 80 MHz. This confirms the potential of ARFPD as a very promising candidate for the linearization of small cell base stations power amplifiers while simultaneously reducing the power overhead compared to the popular digital predistortion technique.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".