Improved Handset Antenna Performance via an Electrically Extended Ground Plane
Bibliographic record
Abstract
A novel and practical approach is presented providing improved antenna performance without enlarging the antenna or the ground plane. The approach electrically extends the ground plane using wire(s) that behave as surface metal extensions of the ground plane. The wire extensions can be accommodated within typical handset housing or as part of the stylish metal used on the handset’s exterior perimeter; hence don’t require enlargement of the device. Consequently, this approach avoids the costs and limitations traditionally associated with physically lengthening of a ground plane. Eight variations are presented and compared with baseline antenna performance. Both far-field patterns and near-field electromagnetic scans demonstrate that the proposed approach controls the electrical length of the ground plane and hence its chassis wavemodes, without negatively impacting the characteristics of the antenna. Improvements in performance of up to 56% in bandwidth at 900 MHz and up to 12% in efficiency with a reduction of up to 12% in the specific absorption rate (SAR) are achieved. An 8% increase in efficiency with a 1.3% improvement in bandwidth and a 20% reduction in SAR is achieved at 1880 MHz. Thus, improvements in bandwidth are achieved without compromising efficiency. Further, improvements at lower frequencies do not compromise performance at higher frequencies.
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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) | 0.002 | 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".