Digital Predistortion for Microwave Transmitters
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Bibliographic record
Abstract
Microwave transmitters comprise the cascade of several subsystems. Among these subsystems, the power amplifier (PA) is identified to be the main source of nonlinearity. Thus, modeling and compensating for the transmitter nonlinearity is often trimmed down to the modeling and compensation of the power amplifier's nonlinearity. Such compensation allows for the use of power‐efficient and nonlinear PA topologies, which results in reducing the energy consumption and cost of wireless transmitters. This article gives an overview of the nonlinear PA behavior and the origins of intermodulation distortions in this subsystem. It defines the most used distortion quantification metrics and the different types of distortions. Power amplifier modeling and digital predistortion principles are explained. The procedure and identification steps of digital predistortion are highlighted, and the different models used in digital predistortion technique are covered. Advanced digital predistortion models for linearizing the impairments in multiple‐input–multiple‐output (MIMO) PAs, dual‐band PAs, and quadrature modulators are also covered.
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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 it