Broadband and Energy-Efficient Power Amplifier Architectures
Bibliographic record
Abstract
In this dissertation, power amplifier circuits and architectures are proposed that provide high efficiency for spectral-efficient high peak-to-average power ratio signals that are being used in modern communications systems. Using the proposed amplifier architectures and circuits high efficiency can be achieved in large frequency bandwidth. The TLLM (Transformer-Less Load-Modulated) amplifier proposed in this dissertation is an amplifier that has similar efficiency performance to the Doherty amplifier, while it does not utilize any power combiner at the output. In the proposed TLLM amplifier, the two amplifier branches are connected directly together and provide high efficiency for high PAPR (Peak-to-Average Power Ratio) signals. A complete analysis is given for designing the two amplifier branches in the TLLM amplifier and a complete and comprehensive design procedure is provided for designing broadband TLLM amplifiers. Three different amplifier prototypes are also implemented using the TLLM architecture showing its performance and capability. The second architecture proposed in this dissertation is a Doherty amplifier that utilizes three-port input and output networks. The analysis and flexible design procedure for designing a Doherty amplifier with three-port input/output networks is provided. The proposed analysis and design procedure can be used to design a Doherty amplifier with any output power ratio from the branches, and power division between the branch inputs. The proposed amplifier eliminates the need for any impedance inverter and offset lines at the input or output of the amplifier. In the next part, a new biasing technique is proposed for transistors. It is shown that using this new biasing, transistors exhibit completely different behaviors from the conventionally biased transistors that can be used for different purposes. Two of the applications are studied in this dissertation. First, a multi-branch amplifier is presented which can provide Doherty-like efficiency in a very large bandwidth. The second application is a linearizing driver amplifier. It is shown that using the proposed biasing scheme, a controlled amount of gain expansion can be achieved. The gain expansion can be used to compensate for the gain compression of the conventional power amplifiers to improve the amplifier's linearity without the need for additional linearizing circuitry or digital pre-distortion.
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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) | 0.000 | 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".