Augmented hammerstein predistorter for linearization of broad-band wireless transmitters
Bibliographic record
Abstract
In this paper, an augmented lookup-table-based Hammerstein predistorter is proposed for the first time in order to further improve the pre-correction capability of the traditional Hammerstein predistorter in the context of broad-band high-power wireless transmitters. The predistorter scheme consists of two separate modules, and its parameters are determined in two steps, which are: 1) static predistorter identification and then 2) dynamic part identification. The performance assessment of the newly proposed predistorter is carried out on a wireless transmitter prototype, which includes an L-band push-pull GaAs field-effect transistor 45-dBm peak-envelope power amplifier. Moreover, one- and three-carrier Third-Generation Partnership Projects frequency-division duplex wide-band code-division multiple-access signals are used as test signals to verify the robustness of this novel predistorter under different bandwidth signals. The linearized transmitter prototype output spectrum demonstrates noticeable superiority of the proposed augmented predistorter in suppressing the spectrum regrowth caused by the memory effects in comparison to the traditional Hammerstein predistorter.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".