Characterization and Performance Measurement of Minimum Bandwidth Ratio UWB CP Antenna Using Machine Learning
Bibliographic record
Abstract
In this article, a novel compact ultrawideband (UWB) circularly polarized (CP) inverted L-shaped-hook monopole (ILSHM) antenna is presented. The proposed ILSHM antenna incorporates a defective ground loop plane with two rectangular slits introduced to the primary radiator. These modifications play a crucial role in controlling current distribution and widening both the impedance bandwidth (IBW) and axial ratio bandwidth (ARBW). The measured UWB IBW is from 2.243 to 20.653 GHz (160.82%), while the ARBW is less than 3 dB from 3.216 to 18.985 GHz (143.45%). As a result, a minimum bandwidth ratio (BR) <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text {BR}|_{(10/3)\text {dB}}$ </tex-math></inline-formula> of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx ~1.121$ </tex-math></inline-formula> is achieved, ensuring nearly stable performance with minimal variation across different bandwidths. Furthermore, machine learning (ML) techniques, such as artificial neural networks (ANNs), are employed to predict the optimal design parameters of the proposed antenna. This approach automates and optimizes the design process, enhancing both adaptability and reliability. The ANN model is trained to identify the best parameter set for optimizing IBW and ARBW, streamlining the process of achieving high-performance characteristics. This novel approach to the UWB CP ILSHM antenna features a simple, compact design, minimum BR, and nearly stable radiation patterns and is highly suitable for UWB wireless applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.000 |
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 teacher head, 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".