Two-Port Network Theory-Based Design Method for Broadband Class J Doherty Amplifiers
Bibliographic record
Abstract
This paper proposes a new methodology for designing wideband Class J Doherty power amplifiers (DPAs). The paper begins by presenting a network analysis that leads to a set of design equations to govern the synthesis of the combiner network parameters that satisfy Class J load requirements. The combiner network produces a complex-to-complex Doherty load modulation capable of avoiding potential transistor voltage clipping due to a mismatch between the fundamental and harmonic impedances. Consequently, this method improves the high power AM-AM characteristic and reduces the DPA's peak power variation versus frequency. A wideband Class J DPA was designed as a proof-of-concept demonstrator to operate from 2.7 GHz to 4.3 GHz. Under continuous wave stimuli, over the entire band, the measured AM-AM distortion in the Doherty region of the fabricated DPA was found to be lower than 1.2 dB with a relatively constant output power of 38.9 ± 0.3 dBm at saturation. Moreover, good drain efficiencies of about 42% and 54% were recorded at 6-dB output back-off and saturation powers, respectively. In addition, the linearizability of the fabricated DPA was confirmed under both intra- and inter-band carrier-aggregated signal stimuli. In fact, measurements using an 80 MHz inter-band carrier-aggregated signal revealed that the proposed DPA could deliver an adjacent channel leakage ratio of better than -48 dBc after digital pre-distortion with a good average drain efficiency of 45% - 49% over the entire band of interest.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".