Adaptive Power Amplifiers for Modern Communication Systems with Diverse Operating Conditions
Bibliographic record
Abstract
In this thesis, novel designs for adaptive power amplifiers, capable of maintaining excellent performance at dissimilar signal parameters, are presented. These designs result in electronically reconfigurable, single-ended and Doherty power amplifiers (DPA) that efficiently sustain functionality at different driving signal levels, highly varying time domain characteristics and wide-spread frequency bands. The foregoing three contexts represent those dictated by the diverse standards of modern communication systems. \nFirstly, two prototypes for a harmonically-tuned reconfigurable matching network using discrete radio frequency (RF) microelectromechanical systems (MEMS) switches and semiconductor varactors will be introduced. Following that is an explanation of how the varactor-based matching network was used to develop a high performance reconfigurable Class F-1 power amplifier. \nAfterwards, a systematic design procedure for realizing an electronically reconfigurable DPA capable of operating at arbitrary centre frequencies, average power levels and back-off efficiency enhancement power ranges is presented. Complete sets of closed-form equations are outlined which were used to build tunable matching networks that compensate for the deviation of the Doherty distributed elements under the desired deployment scenarios. Off-the-shelf RF MEMS switches are used to realize the reconfigurability of the adaptive Doherty amplifiers. \nFinally, based on the derived closed-form equations, a tri-band, monolithically integrated DPA was realized using the Canadian Photonics Fabrication Centre (CPFC®) GaN500 monolithic microwave integrated circuit (MMIC) process. Successful integration of high power, high performance RF MEMS switches within the MMIC process paved the way for the realization of the frequency-agile, integrated version of the adaptive Doherty amplifier.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".