Wideband high-efficiency linearized PA design with reduction in memory effects and IMD3
Bibliographic record
Abstract
ABSTRACT An analytical method is proposed to reduce the memory effects and third-order intermodulation distortions for improving the linearity of wideband power amplifier (PA). An excellent linearity can be obtained by reducing the second-harmonic output power levels and reducing the envelope voltage components in the megahertz range. An improved wideband Chebyshev low-pass matching network including the bias network is analyzed and designed to validate the proposed method. The measured results indicate that a wideband high-efficiency linearized PA is realized from 1.35 to 2.45 GHz (fractional bandwidth = 58%) with power added efficiency of 60–78%, power gain of 10.8–12.3 dB, and output power of 40.0–41.2 dBm. For a 20 MHz LTE modulated signal, the adjacent channel leakage ratios (ACLRs) of the proposed PA with digital pre-distortion (DPD) linearization are −55.7 ~ −53.9 dBc across 1.5–2.4 GHz at an average output power of 32.4–33.6 dBm. For a 40 MHz two-carrier LTE modulated signal, the ACLRs of the proposed PA with DPD linearization are −51.1 ~ −48.2 dBc at an average output power of ~30.5 dBm in the frequency range from 1.5 GHz to 2.4 GHz.
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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) | 0.001 | 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 | 0.000 |
| 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".