VNA-Based Testbed for Accurate Linearizability Testing of RF Beamforming Arrays Under Modulated Signals
Bibliographic record
Abstract
This paper presents a vector network analyzer (VNA) based testbed for accurate phased arrays linearity and linearizability testing under wideband modulated signals. The proposed testbed relies on a standard horn based channel calibration to de-embed over-the-air receiver hardware frequency response and utilizes two of the VNA’s receivers to simultaneously capture accurate representation of the array input and radiated signals. The testbed corrects the linear and nonlinear distortions exhibited by the transmitter underlying components (e.g., arbitrary waveform generator, up-converter, driver amplifiers, and couplers) as well as for the channel and receiver hardware frequency responses so that the linearizability testing is solely indicative of the performance of the array under test. Experiments conducted using an 8x8 RF beamforming array operated at 28 GHz confirmed the capacity of the proposed testbed to support digital predistortion based linearization testing under 5G NR 400 MHz OFDM test signal. More importantly, the pre- correction of the linear and nonlinear distortions exhibited by the testbed yielded an improvement of the adjacent channel power ratios of the array radiated signal by up to 2-3 dB compared to the uncorrected case while using 48% less number of coefficients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".