Ultra-Wideband Differential Fed Hybrid Antenna With High-Cross Polarization Discrimination for Millimeter Wave Applications
Bibliographic record
Abstract
A wideband differential fed patch antenna with high cross-polarization discrimination is proposed at mm-wave range. For the purpose of increasing antenna bandwidth, capacitive coupling technique is used. Also, the differential feeding is utilized to ensure broadside radiation and low cross-polarization. The designed antenna has an ultra-wide bandwidth of 55% around 30 GHz with S11 ≤ -10 dB, and a peak gain of 8 dBi. The radiation pattern has a cross polarization level less than -20 dB over the operating frequency band. The differential feeding technique depends on equal power division and 180° phase difference for all the antenna bandwidth. Due to the wide bandwidth of the differential feeding antenna element, two designs of the feeding circuits (which include rat race and probe strip line transition) are used to cover the whole frequency band of the antenna. A gain enhancement has been achieved by adding a horn to the designed antenna with an efficient aperture efficiency. The designed antennas have fractional bandwidths of 28.73% (at center frequency 25.64GHz) and 26.3% (at the center frequency 32.2 GHz), for the lower and the upper bandwidths, respectively. An average gain of 14.5 dBi has been achieved for the frequency band from 21.8 GHz to 36.5 GHz. The antenna performance is verified through fabrication and measurement, where the simulated and measured results are in a good agreement.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".