Enhanced High Gain Planar Endfire Dipole Antenna Using Third Higher Order
Bibliographic record
Abstract
This paper presents an enhanced endfire antenna that combines a compact size and low-profile structure with high radiation performance. The antenna utilizes the third Higher Order Mode (HOM) of a Planar Dipole Antenna (PDA) in an endfire configuration to achieve significant gain enhancement compared to traditional planar endfire implementations. Unlike standard designs where the ground plane functions primarily as a passive reflector, the proposed approach redefines the ground plane as an active radiating component, effectively operating as a secondary dipole that contributes directly to the overall radiation. By optimizing the phase difference and spatial separation between the primary and secondary dipole elements, the design forms an endfire array configuration. The endfire radiation is further enhanced by exciting both dipoles in their third HOM, resulting in a peak gain of 9.14 dBi and radiation efficiency exceeding 98.5%. This high performance is achieved within a fully planar, balun-free, via-less architecture with compact dimensions of 1.24λ0 ×0.54λ0. The proposed design is also integrated as a driven element in a quasi-Yagi configuration, demonstrating its adaptability and offering an innovative solution for gain enhancement. By incorporating only a single-stage director pair, the antenna achieves a significant gain improvement over conventional planar quasi-Yagi configurations, reaching a peak gain of 11.32 dBi with a compact size of only 1.16λ0 × 0.773λ0. Experimental measurements from the fabricated prototypes closely match simulation results, validating the effectiveness of the gain enhancement approach and highlighting its potential as an innovative solution for compact, high gain planar antenna applications. This makes it particularly suitable for advanced wireless systems, including vehicular communication networks, fifth generation (5G) infrastructure, point-to-point (P2P) microwave links, ground-penetrating radar (GPR), and Internet of Things (IoT) platforms.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".