Rapid Calibration of Variable Gain Phase Shifters: A Novel Characterization Approach with Sparse Measurements
Bibliographic record
Abstract
This paper introduces a new approach to characterize variable gain phase shifters (VGPSs), aiming to address the prolonged measurement time associated with the calibration of high-resolution VGPSs. The proposed method employs a judiciously chosen set of constellation states to capture the VGPS behavior. Subsequently, the acquired characterization dataset serves as the basis for the creation of six mapping functions, each elucidating a specific aspect of the VGPS response. These mapping functions are then harnessed to precisely determine the code words necessary to achieve a desired constellation, mitigating the need for exhaustive measurements. To validate the efficacy of the rapid calibration process, the proposed approach is implemented for calibrating a commercially available VGPS featuring an 8-bit phase resolution and 8-bit gain tuning resolution. Only 58 constellation states are needed for constructing the mapping functions and generating control code words. The outcome demonstrates the capability of achieving phase and gain root mean square errors of approximately 0.49 degrees and 0.15 dB, respectively.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".