Ka-Band Multi-port Power Amplifier Calibration Experiment and Results
Why this work is in the frame
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Bibliographic record
Abstract
A multi-port power amplifier (MPA) is a multi-input multi-output system that is capable of amplifying multiple input signals simultaneously by a set of shared power amplifiers without mutual interference. In a practical MPA, the component imperfections reduce the port isolation, which introduces leakage or cross-port interference. A patent-pending type-based calibration technique is studied in this paper. It exploits the uniqueness of the statistics for a given communication signal to estimate a calibration matrix that minimizes the effects of the MPA impairments and suppresses the cross-port interference. Experimentation with a 4-port 20 GHz prototype MPA subsystem is conducted to evaluate the type-based calibration technique. Results show that it can accurately estimate the calibration matrix to improve the MPA performance significantly.
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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