Current-Biasing of Power-Amplifier Transistors and Its Application for Ultra-Wideband High Efficiency at Power Back-Off
Bibliographic record
Abstract
A new biasing scheme is proposed for transistors used in power-amplifier applications. In the proposed biasing scheme, a constant current source is used as the power supply for the transistor's output terminal. A whole new family of amplifier classes can be defined using this biasing scheme. Analytical equations are obtained and verified for current and voltage waveforms of a current-biased transistor for both resistive and tuned load impedances. Using the proposed current biasing scheme, the transistor presents completely different behaviors compared with the conventional voltage biasing scheme. These properties can be utilized for new design concepts and can provide new possibilities in the future designs and applications. Some of the differences between current-biased and voltage-biased amplifiers are discussed. One of the different behaviors shown by the current-biased transistor amplifiers is the reversed load modulation. Using this property of the current-biased amplifiers, a reversed modulation dual branch (RMDB) amplifier structure is proposed for ultra-wideband high efficiency at power back-off. Due to the reversed load modulation of current-biased transistors, a multibranch amplifier can be implemented to obtain high efficiency at power back-off without the need for an impedance inverter at the output of the current-biased amplifier. By using the proposed amplifier structure, a wideband RMDB amplifier was fabricated and tested exhibiting higher than 37% efficiency for long-term evolution (LTE) signals in 0.8-2.2-GHz bandwidth, which is equivalent to 93% fractional bandwidth.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".