Novel Wideband Reflectarray Antenna Supporting Multiple V2X Links
Bibliographic record
Abstract
A novel circularly polarized (CP) wideband high gain reflectarray antenna (WHG-RA) is designed to cater to the requirements of future Vehicle-to-Everything (V2X) communication links. The WHG-RA offers a gain bandwidth spanning from 12 to 32 GHz, encompassing crucial V2X frequency bands, including vehicle-to-satellite (V2S) Ku/Ka downlinks and uplinks frequencies (12.25–12.75, 14.0–14.5, 19.6–21.2, and 29.4– 31.0 GHz), the 24.25-26.65 GHz range designated for vehicle-tovehicle (V2V) short-range radars, and all 5G millimeter-wave spectrums, such as n258 (24.25-27.5 GHz) and n257 (26.5-29.5 GHz), intended for vehicle-to-5G networks (V2N) links. The design of the wideband unit cell within the WHG-RA is rooted in a rotational technique grounded in geometric phase theory and has multiple resonances. As a proof-of-concept, a 180 × 180 mm² planar WHG-RA consisting of 900 unit cells is designed, fabricated, and rigorously tested across the 12 – 32 GHz frequency range. Both simulation and measurement results affirm the WHGRA's prowess, showcasing consistent radiation patterns, a commendable peak aperture efficiency (41.1%), a high gain of 30.3 dBi, and side-lobe levels (< −17.2 dB) averaged over 12 to 32 GHz, offering a substantial 3-dB and 4-dB gain bandwidths of 51% and 91%. These outcomes underscore the WHG-RA's potential to augment the performance and reliability of future V2X systems.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".