Far-Field-Based Nonlinear Optimization of Millimeter-Wave Active Antenna for 5G Services
Bibliographic record
Abstract
This paper presents the design and characterization of millimeter-wave (28/38 GHz), circularly polarized (CP) active antennas, suitable for future 5G services. By augmenting the modeling capabilities of commercially available nonlinear CAD tools, the active antenna design can simultaneously optimize figures of merits for both radiation and nonlinear (NL) performance. The radiating part is computed and optimized layoutwise by means of an artificial neural network (ANN), suitably trained off-line. For the NL design purpose, the harmonic neural network (HNN) of the antenna is subsequently implemented as a standard circuit component, to include the antenna behavior at all the harmonics of its NL regime. This allows avoiding time-consuming electromagnetic (EM)-simulations in the harmonic balance optimization loop. In this way, a welldefined interface between the antenna and the amplifier can be avoided. To demonstrate the effectiveness of the design approach, one active integrated antenna (AIA), consisting of a class AB amplifier feeding a CP patch at 38 GHz, has been fabricated with the standard 0.1-μm AlGaAs-InGaAs pHEMT technology and extensively measured with respect to both the electrical and radiation performances. To reduce fabrication costs, hybrid integration of the antenna and amplifier connection is used, and the antenna is incorporated into the main PCB.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".