2-W Broadband GaN Power-Amplifier RFIC Using the $f_{T}$ Doubling Technique and Digitally Assisted Distortion Cancellation
Bibliographic record
Abstract
The method of derivative superposition is enhanced with digital techniques to cancel the intermodulation distortion generated by a 2-W power amplifier (PA) RF integrated circuit over a broad band of 6 GHz. Two amplifiers were fabricated and tested: a baseline PA without distortion cancellation and a PA with digitally assisted distortion cancellation to demonstrate the effectiveness of the new technique. The PAs are biased in class-A mode and have anOP1dBof 31 dBm and aPSATof 33 dBm. Measurements reveal that the output third-order intercept point (OIP3) of the PA with digitally assisted distortion cancellation can be increased to 50.25±3.75 dBm between 1-6 GHz relative to the OIP3 of the baseline PA, which is 40.25±2.75 dBm over the same frequency span. The level of distortion cancellation is not only dependent on the frequency of the incident signal, but also on its power level. Data is presented that shows how the proposed digitally assisted distortion cancellation method also improves the OIP3 of the PA when the RF input power level is taken into account.
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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) | 0.002 | 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".