On the Performance of a Photonic Reconfigurable Electromagnetic Band Gap Antenna Array for 5G Applications
Bibliographic record
Abstract
In this paper, a reconfigurable Multiple-Input Multiple-Output (MIMO) antenna array is presented for 5G portable devices. The proposed array consists of four radiating elements and an Electromagnetic Band Gap (EBG) structure. Planar monopole radiating elements are employed in the array with Coplanar Waveguide Ports (CWPs). Each CWP is grounded on one side to a reflecting L-shaped structure that has an effect of improving the antenna’s directivity. It is shown that by inductively connecting Minkowski fractal structure of 1storder to the radiating element, the impedance matching is improved that results in enhancement in the array’s bandwidth performance. The EBG structure is used to provide the isolation between antenna elements in the MIMO array. The fractal structure is connected to the L-shaped reflector through four photosensitive light dependent resistor (LDR) switches. The effect of various LDR switching configurations on the performance of the antenna is investigated. The proposed array provides a novel performance in terms of S-parameters with enhancements in the radiation properties. Such enhancements are achieved with low separation gaps between antenna elements (about λo/16 at 3.5 GHz). It is shown that the array’s operational bands centered at 3.5 GHz and 4.65 GHz can be selected by activating certain LDR switches. The electromagnetic exposure of the array on the human body is investigated by determining the specific absorption rate (SAR). It is found that the proposed antenna shows lower SAR values compared to other antennas reported in literature. With the proposed EBG structure, the gain of the array is increased 7.5 dB (from -3.5 dBi to +4 dBi) at 3.5 GHz and by 14.3 dB (from -8.7 dBi to + 5.6 dBi) at 4.65 GHz. The average radiation efficiency between 3.5 GHz and 5.5 GHz increased by 42% from 20% to 62%. Excellent radiation characteristics of the EBG the array makes it suitable for 5G portable devices such as tablets.
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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".