Millimeter-Wave Wideband Active Load—Pull System Using Vector Network Analyzer Frequency Extenders
Bibliographic record
Abstract
This letter presents a novel wideband active load—pull (LP) system that leverages vector network analyzer frequency extenders (VNA-EXTs) for precise millimeter-wave (mmW) device characterization under modulated signal excitation. Unlike conventional VNA-EXT-based LP systems, typically limited to continuous-wave (CW) testing, the proposed system integrates advanced signal generation and analysis capabilities to enable comprehensive LP measurements with wideband modulated signals. At its core, an iterative linearization algorithm with a novel adaptive cost function is proposed to simultaneously linearize the frequency multipliers (FXs) within the VNA-EXTs and synthesize accurate load impedances at the device-under-test (DUT) output reference plane. Proof-of-concept experiments were conducted at 57.6 GHz using Oleson Microwave Labs (OMLs) VNA-EXTs with a V-band power amplifier (PA) as the DUT. Active LP measurements used a 256-QAM orthogonal frequency division multiplexing (OFDM) signal with a 400-MHz modulation bandwidth. The system demonstrated accurate load synthesis across a 1.2-GHz bandwidth, achieving voltage standing wave ratio (VSWR) circles up to 5:1 and a load synthesis error below −39.1 dB. Furthermore, the proposed algorithm enabled precise reflection coefficient generation over frequency, including arbitrary responses such as the letter “A.” The system was also used to evaluate the DUT’s resilience to VSWR variations with and without linearization. Adjacent channel power ratio (ACPR) and error vector magnitude surfaces were measured across the Smith chart, providing insights into the impact of load variations on DUT’s linearity.
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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".