A mm-Wave 5G System Architecture With Enhanced-Gain Antenna Solution
Bibliographic record
Abstract
A millimeter-wave (mm-wave) system architecture with enhanced-gain antenna solution is proposed for fifth-generation (5G) wireless communications. To enhance the antenna gain, the surface wave currents are converted to constructive far-field radiations through a novel two-stage metallic and dielectric rings. By applying low-profile dielectric ring, the electric near-field distributions of the grounded quarter-wavelength metallic ring are modified to create radiation apertures with appropriate polarization and orientation. The mutual coupling between transmit/receive antennas and cross polarizations is reduced by 7 and 5 dB, respectively, and the gain is enhanced over 4 dB. Low-loss mm-wave transitions are implemented in a standard FR-4 printed circuit board and in an interposer substrate to integrate the active antenna with the rest of the wireless system on a compact universal serial bus (USB)-interface platform. To evaluate the interposer package and define the output power of the embedded 60-GHz RF IC, an evaluation module with a standard WR-15 waveguide interface is designed and implemented. A prototype of the architecture, which is entirely realized using low-cost organic substrates, is presented and evaluated by experiments. Excellent correlations are achieved between simulations and measurements, demonstrating more than 25 dBm of equivalent isotropically radiated power (EIRP) over the unlicensed 60-GHz 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".