A Dual-Band Butler Matrix-Based Millimeter-Wave Dual-Band Multibeam Antenna Array Using a Simple Dual-Mode Transmission Line Scheme
Bibliographic record
Abstract
This article reports a millimeter-wave (mmW) dual-band multibeam antenna array (MAA) based on a dual-band Butler matrix (BM) utilizing a novel dual-mode transmission line (DMTL) structure. This kind of DMTL scheme enables the realization of distinct operating modes at different frequency bands, facilitating the design of a dual-band device through an initial determination of the frequency ratio (FR) followed by topological and structural adjustments. Compared to conventional dual-band design techniques, this scheme significantly simplifies the design process while enhancing the efficiency. Based on this scheme and the proposed DMTL structure, a dual-band BM is designed and implemented with several dual-band couplers, crossovers, and shifters as a proof of concept. The structure incorporates nested sets of BMs operating at two distinct bands, providing antenna excitations with a uniform amplitude and progressive phase. Moreover, four wideband dual-polarized antennas are designed to constitute an MAA to verify its dual-band performance. A prototype operating at 24 and 30 GHz with FR =1.25 is designed, fabricated, and evaluated. The measured results show that excellent reflection coefficients (−19.2 dB) and port isolations (15.1 dB) are achieved, ensuring a stable dual-band multibeam performance. Additionally, four beam angles of [$14^{\circ },- 45^{\circ }$,$46^{\circ },- 12^{\circ }$]/[$12^{\circ },- 35^{\circ }$,$35^{\circ },- 11^{\circ }$] and cross-polarization discrimination (XPD) of 12.8 dB are realized across the required bands.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".