Broadband GaN Class-E Power Amplifier for Load Modulated Delta Sigma and 5G Transmitter Applications
Bibliographic record
Abstract
The paper presents a design of a broadband high-efficiency class-E power amplifier (PA) for the advanced efficiency enhancement architectures applications. A sequential load pull methodology to design broadband class-E power amplifiers using a packaged gallium nitride power transistor is presented. Two different broadband matching synthesis techniques have been proposed using lumped elements have been presented and implemented in the manuscript. A fourth-order low-pass impedance transformation topology is designed as the output matching network to provide the optimum load reflection coefficients in the targeted bandwidth (1.8-2.7 GHz). A combination of input and output matching network has been proposed in the manuscript to satisfy the given fractional bandwidth requirements. For practical validation, a Wolfspeed (Cree) CGH40025 package transistor has been used. Under continuous wave test condition the fabricated PA showed more than 50% power added efficiency (PAE) with up to 29 W output power for 40% fractional bandwidth from 1.8-2.7 GHz. Furthermore, the proposed broadband Class E PA is deployed in efficiency enhancement architecture like delta-sigma modulation based transmitters. The PA shows more than 48% PAE all over the frequency band when driven with a delta-sigma modulated LTE downlink signal while maintaining high signal quality and PA reliability.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".