An innovative fractal monopole MIMO antenna for modern 5G applications
Bibliographic record
Abstract
Proposed in this paper is the design of an innovative and compact antenna array which based on four radiating elements for multi-input multi-output (MIMO) antenna applications used in 5G communication systems. The radiating elements are fractal curves excited using an open-circuited feedline through a coplanar waveguide (CPW). The feedline is electromagnetically coupled to the inside edge of the radiating element. The array’s impedance bandwidth is enhanced by inserting a ground structure composed of low–high-low impedance between the radiating elements. The low-impedance section of the ground is a staircase structure that is inclined at an angle to follow the input feedline. This inter-radiating element essentially suppresses near-field radiation between adjacent radiators. A band reject filter based on a composite right/left hand (CRLH) structure is mounted at the back side of the antenna array to reduce mutual coupling between the antenna elements by choking surface wave propagations that can otherwise degrade the radiation performance of the array antenna. The CRLH structure is based on the Hilbert fractal geometry, and it was designed to act like a stop band filter over the desired frequency bands. The proposed antenna array was fabricated and tested. It covers the frequency bands in the range from 2 to 3 GHz, 3.4–3.9 GHz, and 4.4–5.2 GHz. The array has a maximum gain of 6. 2dBi at 3.8 GHz and coupling isolation better than −20 dB. The envelope correlation coefficient of the antenna array is within the acceptable limit. There is good agreement between the simulated and measured results.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".