Millimeter-Wave Device Characterization Under Wideband Modulated Signals Using Vector Network Analyzer Frequency Extenders
Bibliographic record
Abstract
This letter presents an innovative frequency extender based measurement system designed for the comprehensive characterization of millimeter-wave devices under both continuous-wave (CW) and modulated signal excitation. In addition to traditional CW-based measurement systems consisting of Vector Network Analyzers (VNAs) and VNA frequency extenders, the proposed system integrates an Intermediate Frequency (IF) Vector Signal Generator (VSG) and IF Vector Signal Analyzers (VSAs). During modulated signal testing, the IF VSG feeds the VNA frequency extender, enabling the generation of RF-modulated signals at the device-under-test (DUT) reference plane without the need for a mixer. Concurrently, the IF VSAs are connected to the VNA frequency extenders, facilitating the capture of wideband modulated signals at the DUT input and output reference planes. Additionally, the proposed system incorporates a novel Iterative Learning Control (ILC) algorithm formulated to linearize frequency multipliers (FMs) within VNA frequency extenders ensuring error-free RF modulated signal generation at the DUT input reference plane. To validate the proposed measurement system, proof-of-concept experiments were conducted at V-band (around 57.6 GHz) using an Oleson Microwave Labs (OML) VNA frequency extender. The novel ILC algorithm was employed to linearize the FM within the OML frequency extender, enabling the mixer-less generation of 256 QAM orthogonal frequency division multiplexing signals with modulation bandwidths up to 800 MHz. The measurement results showcase exceptional signal integrity achieved by the proposed system and enabled by the proposed ILC algorithm, achieving an adjacent channel power ratio and error vector magnitude at the DUT input reference plane of 51.6/48.7 dBc and 1.2%, respectively, when using an 800 MHz test signal case.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".