A Transformer-Less Load-Modulated (TLLM) Architecture for Efficient Wideband Power Amplifiers
Bibliographic record
Abstract
An architecture and design procedure for transformer-less load-modulated power amplifiers (PAs) having high efficiency at power back-off is presented. This architecture utilizes a comparable load modulation concept as in the Doherty PA; however, contrary to the Doherty PA, it neither requires an output impedance transformer, nor offset lines, which are the main limiting factors in designing wideband Doherty amplifiers. In this architecture, the proper load modulation at the transistor drain levels is directly dependant on the design of the main and peaking amplifiers' matching networks. It is shown that the design of the output impedance of the peaking amplifier as a frequency-varying susceptance rather than as a quasi-open circuit impedance (as in Doherty amplifiers) helps in achieving better wideband performance. Based on the analytical results, a step-by-step procedure is proposed for designing wideband load-modulated PAs having high efficiency over a wide power back-off region. Based on the proposed architecture and its design procedure, a 10-W PA is designed and prototyped in the 1.96-2.46-GHz band exhibiting a power-added efficiency of more than 40% at a 6-dB power back-off. In comparison with a symmetrical Doherty amplifier design using the same device, the proposed architecture exhibits a 250% enhancement in its 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) | 0.002 | 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".