Ridge Gap Waveguide Beamforming Components and Antennas for Millimeter-Wave Applications
Bibliographic record
Abstract
With the improvement of mobile communication technologies and their broad applications, mobile communication will have more impact on our life. Such systems will support a variety of personal communication services with high-data rate and very low latency applications. To achieve such demands, many proposals associated with the development of 5G identify a set of requirements for which different technological directions are independently emerging. One direction is utilizing the millimeter-wave (mm-Wave) frequency bands where more spectrums are available. Millimeter-wave frequencies offer the advantage of physically smaller components that results in cost-effective RF transceivers and feasible large-scale integrated phased arrays. The smart RF transceivers of 5G along with the potential high-frequency innovative designs must satisfy the growing consumer and technology requirements. This implies utilizing the state-of-the-art guiding structures, especially printed ridge gap waveguide (PRGW), that have low loss and minimal dispersion compared with traditional PCB-based structures. The present chapter focuses on the necessary components for a beamforming antenna system which is implemented using PRGW technology. Millimeter wave antennas with different polarizations have been addressed. Power combining and dividing components have been also developed. These components have been used for integration in a complete beamforming antenna system working at an mm-Wave frequency band.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.011 |
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".