A Wearable Circularly Polarized Antenna Backed by AMC Reflector for WBAN Communications
Bibliographic record
Abstract
A new flexible circularly polarized (CP) wearable antenna is proposed for Wireless Body-Area Network systems (WBAN) operating at 5.8 GHz. The circular polarization is enabled by combining a microstrip-line monopole feed and an inverted L-shaped conformal metal strip extended from a coplanar waveguide. The proposed antenna shows satisfactory performance in terms of gain and specific absorption rate (SAR) at a separation of 12 mm from a human body model. To minimize body-antenna separation, a flexible$2 \times 2$artificial magnetic conductor (AMC) was used as a reflector to achieve good performances in terms of gain and SAR. The total footprint of the proposed antenna is only$34.4\times34.4$mm2($0.384 \lambda _{0}^{2}$) board of semi-flexible Rogers RT-Duroid 5880 substrate. The final design provides a peak gain of 7.6 dBi and an efficiency of 94.7% when worn on the body. Furthermore, evaluation results indicate that the maximum SAR level decreased by up to 20.42% in comparison with the CP antenna without the AMC. Full-wave EM simulation and experimental results are performed, both in free space and proximity to the human body under different bending scenarios. Overall, the proposed antenna performance has been shown to be robust to structural deformation along the x-axis in comparison to previously reported designs. These features demonstrate that the proposed antenna is a strong candidate for off-body wearable applications.
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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 | 0.000 |
| 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 | 0.002 |
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".