A MIMO antenna array featuring dual wideband and high gain for 5G NR n257/n258/n260/n261 bands applications
Bibliographic record
Abstract
Abstract This article introduces the development of a Multi-Input Multi-Output (MIMO) antenna array specifically designed for 5G millimeter-wave (mm-wave) communication systems. The suggested MIMO configuration consists of four antenna arrays, each comprising two elements arranged evenly, operating at 26 GHz and 37 GHz with a physical size of 43 mm × 32.5 mm × 0.8 mm using a Rogers RT/Duroid 5880 substrate. The proposed MIMO configuration provides dual bands, with frequency bands extending from 23.8 to 30 GHz (IBW = 6.2 GHz) and 32.5 to 41 GHz (IBW = 8.5 GHz), accompanied by high gains of around 18.5 dB for the first band and 16.4 dB for the second band. The designed antenna also shows broad circular polarization with 3 dB Axial Ratio Bandwidth (ARBW) of 4.75 GHz, ranging from 25.05 to 29.8 GHz. A physical prototype has been fabricated for the proposed 4 port MIMO antenna array and tested to verify the results acquired from simulations. The comparison between simulation and measurement results in terms impedance and radiation parameters such as S-parameters, isolation, gain, axial ratio (AR), efficiency, radiation patterns, and various necessary MIMO metrics demonstrates a strong alignment. This antenna covers various 5G New Radio (NR) application bands such as 28 GHz n257 (26.50–29.50 GHz), 26 GHz n258 (24.25–27.50 GHz), 28 GHz n260 (37–40 GHz) and 28 GHz n261 (27.50–28.35 GHz) utilized across different countries including Canada, Australia, China, France, Germany, India, Italy, Japan, South Korea, United Kingdom, and United States of America.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".