Baseband Equivalent Volterra Series for Behavioral Modeling and Digital Predistortion of Power Amplifiers Driven With Wideband Carrier Aggregated Signals
Bibliographic record
Abstract
This paper presents a new baseband equivalent (BBE) Volterra-series model suitable for the behavioral modeling and linearization of wideband RF power amplifiers (PAs). Starting with passband Volterra series, and following a number of signal transformations, a discrete expression relating the envelopes of the output and input signals to the carrier frequency was derived. The new BBE Volterra series reduces the number of kernels, and hence, avoids resorting to the pruning approaches commonly applied in the literature to the classical low-pass equivalent (LPE) Volterra formulation. The proposed baseband Volterra series has similar modeling and linearization performance to the full classical LPE Volterra series. It also outperforms the pruned LPE Volterra models, which use a dynamic deviation reduction approach, in terms of both linearization performance and complexity. The proposed BBE Volterra series was successfully used to linearize different PAs (200-W LDMOS Doherty and 45-W broadband GaN PA) driven with wideband and intra-band carrier aggregated signals (mixed LTE and WCDMA 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".