Design of an E-sectoral horn based on PRGW technology for 5G applications
Bibliographic record
Abstract
Abstract E-plane sectoral horn antenna based on printed ridge gap waveguide (PRGW) technology is designed for 5G applications. It is implemented on the top plate of the PRGW structure to avoid the losses and dispersion associated with conventional feeding mechanisms. The quasi-transverse electromagneticPRGW-based antenna is excited through a planar microstrip transition. First, the single horn antenna element is introduced with the microstrip feeding section. It shows an impedance bandwidth of fractional bandwidth 26% from 45.7 to 55.4 GHz with the realized gain of 12.7 dBi and radiation efficiency of 90%. In order to maximize the realized gain, a four-element linear horn array is introduced. The same impedance bandwidth is maintained with the array having a gain of 18.6 dBi from 45.7 to 55.4 GHz. The overall antenna array performance in the entire operating frequency range is stable with a radiation efficiency around 85%. Three matching sections are implemented to achieve better impedance matching. One is used to match the horn with the feeding aperture via the PRGW line. Another section is designed to match microstrip transition with PRGW ridge. Finally, two-stage quarter wavelength transformers are required to match the power divider with the array feeding network. A prototype of single-element horn antenna was fabricated to verify the concept of the design. Simulated and measured results show that the proposed antenna can operate in the frequency band of 45–55 GHz with good agreement of radiation performance. Moreover, the proposed designs are implemented and simulated using two microwave simulation tools (CST and HFSS) to verify the radiation performance, which exhibits good agreement. The design of an E-sectoral horn antenna and its array with high gain based on PRGW is demonstrated for the first time which is considered a novel issue. It can be integrated with other passive and active elements in communication systems. Thus, it can be a valuable component in 5G communication due to its high gain, compact size and ultra-wide band.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".