Modelling and Design Methodology of High-Efficiency Harmonic Tuned Power Amplifiers for 5G Applications
Bibliographic record
Abstract
Radio Frequency (RF) base station unit moves toward qualification of next generation gallium nitride devices to meet the expectation of fifth generation (5G) wireless communication transmitters. Critical to the enablement and the fulfilment of next-generation network energy efficiency, Power amplifiers (PAs) remain a centre of focus to RF power base station markets. With an immense increase in cellular traffic, the performance of PAs should be constantly improved in terms of power efficiency, linearity, and bandwidth. This thesis focuses on several aspects for enabling high efficiency and bandwidth for small cell PAs for 5G applications. A variety of PA design methodologies have been proposed and implemented to achieve state of the art performance. Innovative input and output controlled harmonic tuned PAs have been presented to enable application of advanced waveform engineering in RF base station products. Harmonic optimization techniques have been proposed for efficiency and bandwidth enhancement of the PA performance. For experimental validation, various prototypes for different peripheries, RF specifications, and targeted performance are fabricated using different technologies. The issues related to bandwidth extension, device reliability, and miniaturization of PA base unit are studied and mitigated. This thesis proposes a novel comprehensive analysis of harmonically tuned amplifiers with respect to input harmonic sensitivity, highlighting the significance of input and output controlled amplifiers and their impact on the linearity of gallium nitride (GaN) based high efficiency PAs. For the first time, this thesis presents class GF and GF-1 amplifiers which have demonstrated outstanding performance by simultaneously tackling both input and output harmonic. In particular, this work demonstrated methods for effective on wafer active load pull that minimizes the amount of time required on the measurement system, and how to directly achieve maximum efficiencies by a combination of fundamental, second, and third harmonic at load and source of the active device. The conventional problem of so-called 2nd harmonic efficiency “null”, has been addressed in this thesis. The study reveals new directions for harmonic load pull and design procedure for high-efficiency PAs, which enables PAs in context of wide bandwidth, high efficiency without compromising the quality of service.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".