A Metamaterial-Based Compact MIMO Antenna Array Incorporating Hilbert Fractal Design for Enhanced 5G Wireless Communication Networks
Bibliographic record
Abstract
A novel compact antenna array design, tailored for 5G applications, is introduced in this paper. The proposed antenna operates in the sub-6GHz frequency range, ensuring optimal wave propagation characteristics in local areas. To achieve this, a Hilbert fractal patch-based design is employed. The antenna is developed as a two-element array, catering to Multi-Input Multi-Output (MIMO) communication systems. The overall size of the antenna array is reduced by minimizing the spacing between the antenna elements, which is achieved through the integration of Metamaterial (MTM) Minkowski inclusions as defects on the back panel. The antenna exhibits a gain greater than 4.7dBi within the targeted frequency band, with a maximum coupling of -20dB. Two primary frequency bands, centered around 3.5 GHz and 5.5 GHz, are demonstrated by the antenna array. The proposed design is fabricated and subsequently subjected to experimental testing. The measured results exhibit excellent agreement with their corresponding simulation outcomes, confirming the effectiveness of the proposed antenna design.
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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".