A Wearable Dual-Band Dielectric Patch Antenna for LTE and WLAN
Bibliographic record
Abstract
In this paper, a dual-band microstrip patch array antenna for both the Multiple Input Multiple Output (MIMO) 4G Long-Term Evolution (LTE) and the Wireless Local Area Network (WLAN) systems is developed. Design simulation and optimization processes are carried out with the aid of the Advanced Design System (ADS) electromagnetic simula-tor that uses the full–wave Method of Moment (MoM) numerical technique [1]. Rectangular microstrip patch antenna constructed from Multi-Walled Carbon Nanotubes (MWCNT) ink with electrical conductivity of 2.2 × 104 S/m and relative permittivity of 5-j1 is used as the conductor patch [2]. The patch is deposited on Rogers substrate RT-Durid 5880 single substrate with (εr = 2.2) and thickness of 62 mil. U-shape slot is used to provide the dual-band. The pro-posed antenna operates at 3.5 GHz for LTE and 5 GHz for WLAN. The proposed antenna parameters are compared with published result of copper patch [3,4] for the same application. Great enhancement in antenna size, matching, and frequency band width are achieved for the proposed antenna at the expense of gain and antenna efficiency compared with copper one. The proposed MWCNT dielectric antenna size is 18.4 mm × 27 mm compared with size of 27 mm × 37 mm for copper one respectively. The gain is better than 7dBi with antenna efficiency of 87.29% at 3.5GHz for cop-per patch antenna and is better than 1.692 dBi with antenna efficiency of 27.05% at 3.5GHz for MWCNT respectively.
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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 | 0.001 |
| 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) | 0.002 | 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".