Efficient Angle Calibration Method for Peak Beam Measurements in Transmitarray-Based Compact Antenna Test Range
Bibliographic record
Abstract
Due to the explosive growth of wireless communications, cost-effective streamline measurement of wireless devices’ peak beam is a highly desirable over-the-air testing demand. The directional antenna in the device under test (DUT) is usually subject to housing effects or manufacturing and assembly errors, causing deviations in its peak beam direction and imposing challenges on efficiently testing the beam peak. Regular antenna pattern measurements are time-consuming and complicated, thus, unsuitable for streamline tests. This article proposes an efficient calibration method via a transmitarray compact antenna test range (CATR). The relationship between deviation angle and received power is demonstrated, and the off-axis feed characteristics of the transmitarray CATR are analyzed, based on which a quick and convenient technique is present for the angle calibration of the maximum beam deviation. The entire calibration process requires only measuring three positions, for a linear array, without a need for extra test sites or equipment. By utilizing this method in the streamline measurements, the peak beam of the DUT antenna can be calibrated over an angular range of −30° to 30° conveniently and effectively. Experiments are conducted for verification, and results show excellent agreement with simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".