Dual-band linear filter assisted envelope memory polynomial for linearizing multi-band power amplifiers
Bibliographic record
Abstract
This paper proposes a dual-band linear filter assisted envelope memory polynomial model (EMP) devised for analog-RF predistortion (ARFPD) systems. The proposed model consists of two finite-impulse-response (FIR) filters preceding a newly formulated dual-band EMP block in forming the pruned 2D-FIR-EMP model. A linear estimation algorithm is devised to identify the coefficients of the proposed pruned 2D-FIR-EMP model. Furthermore, a proof of concept of the digitally-assisted dual-band ARFPD system based on two RF vector multipliers (RF-VM) built using off-the-shelf (OTS) components is presented. Measurement results obtained using the proposed pruned 2D-FIR-EMP proof-of-concept predistorter has demonstrated excellent linearization results in compensating for the distortions exhibited by a gallium nitride Doherty power amplifier driven by dual-band signals.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".