Novel Dual-Band Matching Network for Effective Design of Concurrent Dual-Band Power Amplifiers
Bibliographic record
Abstract
In this paper, a systematic approach to the synthesis of a novel dual-band matching network is proposed and applied to design a dual-band power amplifier (PA) capable of maintaining high power efficiency at two arbitrary, widely spaced frequencies. The proposed network incorporates two different stages. The first one transforms the targeted complex impedances, at the two operating frequencies, to a real one. The second stage is a dual band filter that ensures the matching of the former real impedance to the termination impedance. An additional transmission line is incorporated between the two stages to adjust the impedances at the second and third harmonics without altering the impedances seen at the fundamental frequencies. The harmonic termination control is very effective in enhancing the efficiency of radio frequency transistors, especially when exploiting the Class J design space. The proposed dual-band matching network synthesis methodology was applied to design a dual band PA using a packaged 45 W Gallium Nitride (GaN) transistor. The PA prototype maintained a peak power efficiency of about 68% at operating frequencies of 800 MHz and 1.9 GHz. In addition, a Volterra based digital pre-distortion technique was successfully applied to linearize the PA response around the two operating frequencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".