Printed <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math> </inline-formula>-Band Multibeam Antenna With Luneburg Lens-Based Beamforming Network
Bibliographic record
Abstract
A simple and low-cost single substrate layer Luneburg lens-based beamforming network (BFN) operating in W-band is proposed. A multibeam antenna is then realized using the novel BFN and fabricated using a low-cost printed circuit board process. The Luneburg lens is realized by drilling holes in a substrate, and to extend the refractive index variation, a high dielectric constant substrate is embedded at the lens center. The lens is excited by radially placed open-ended substrate integrated waveguides (SIWs) at the lens circumference, feeding a subset of antenna ports placed on the opposite side of the lens. The proposed BFN thus provides half-power beamwidth (HPBW) and beam steering angle flexibility for multibeam antennas. An antenna generating seven beams was prototyped at 79.5 GHz to validate this BFN, providing measured beam steering of approximately ±41° with a broadside HPBW of 10°. The bandwidth of the proposed BFN is determined by the single-mode operation of the SIW. The fabricated multibeam antenna impedance bandwidth is, however, about 2% at 79.5 GHz because of the radiating elements. The radiating elements are series fed slot arrays placed on SIWs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.200 | 0.116 |
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".