Dual-Band Microstrip Corporate Feed Network Using an Embedded Metamaterial-Based EBG
Bibliographic record
Abstract
This paper demonstrates the design and experimental validation of a dual-band microstrip (MS) corporate feed network using an embedded metamaterial-based electromagnetic bandgap (MTM-EBG) structure. The MTM-EBG is designed to appear as a 35.4 Ω MS transmission line (TL) with a 90° electrical length at both 2.4 and 5.0 GHz. This structure is then used in place of a conventional single-band quarter-wavelength transformer in a 50 Ω MS T-junction power divider, rendering it dual band with 10 dB return-loss fractional bandwidths of 82.3% and 12.0% at center frequencies of 2.4 and 5.0 GHz, respectively. The result of this modification shows a very good performance at both operating frequencies, as well as substantial rejection over a prescribed bandwidth of intermediate frequencies. The dualband transformer formed by the embedded MTM-EBG is 30.5% miniaturized at 2.4 GHz relative to a conventional MS quarterwavelength transformer, which indicates its potential for use in size-restricted scenarios and embedded filtering applications. A dual-band four-way corporate feed network is then formed by cascading the MTM-EBG-loaded T-junction power dividers, which is suitable for antenna array applications. This structure has an improved performance at 2.4 and 5.0 GHz compared to unloaded networks designed for these frequencies having the same overall size. The experimental 10 dB fractional bandwidth for the MTM-EBG-loaded corporate feed network was found to be 52.0% and 10.8% around 2.4 and 5.0 GHz, respectively.
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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) | 0.000 | 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 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".