HANDSET ANTENNA DESIGN: PRACTICE AND THEORY
Bibliographic record
Abstract
In this paper, an attempt is made to present a theory for the design of handset antennas, which results from the long experience that the authors have in the field of handset antenna design.The proposed theory is based on the well-known skin effect and constructs the antenna using a thin wire model that represent the backbone of the final antenna.The analytical solution for the thin wire model is first obtained, and the main properties (such as the return loss and the radiation properties) of the antenna can then be studied using this analytical solution.Once the antenna backbone is constructed, other elements, such as stubs, patches, etc., can be added to optimize the match at the desired frequency bands.A number of numerical and analytical examples are provided throughout the paper to validate the theory.Different antenna types, such as wire antennas and planar antennas, are tested and designed using the thin wire model.The correspondence between the analytical results and those from the numerical simulations using full-wave solvers agree very well in all examples.The authors also present in this paper a novel design of three small antennas for handset applications, which are based on the simple wire monopole, but in a three-dimensional form.The proposed three-dimensional monopole antennas have multi-band and broadband properties that cover most frequency bands being used for the handset device.The antennas feature remarkable properties while occupying a significantly small space, which makes them strong candidates for handset applications and for the future multi-antenna applications too.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".