RETRACTED: Experimental investigations of dual functional substrate integrated waveguide antenna with enhanced directivity for 5G mobile communications
Post-publication record
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Bibliographic record
Abstract
Antennas with higher gain and efficiency deliver superior performance across a wide frequency range. Achieving these characteristics at high frequencies while keeping a compact size necessitates sophisticated design approaches. This research presents a substrate-integrated waveguide (SIW) cavity-backed slotted patch antenna (SPA) tailored for the 28 GHz and 34 GHz frequency bands. Additionally, a linear tapered slot antenna is designed with a compact profile of 27.5 mm × 7.5 mm × 0.254 mm. The SIWs are implemented using vias on the outer profile of the antenna, and circular and rectangular slots are etched on the radiating surface. The goal of optimizing the antenna geometry is to enhance return loss within the desired frequency bandwidth, which means the Genetic Algorithm (GA) will determine the optimal antenna shape to achieve lower return loss than the original design within this bandwidth. The antenna exhibits dual resonance at 28 GHz and 38 GHz in the millimeter-wave range, providing an impedance bandwidth of 211 MHz (27.72 GHz-27.94 GHz) at 28 GHz and 127 MHz (37.88 GHz-37.98 GHz) centered at 38 GHz. The proposed antenna demonstrates gains of 8.04 dBi and 9.72 dBi at these operating bands. A prototype of the antenna is fabricated on RT/duroid 5880 and its characteristics are measured. The overall VSWR of the antenna ranges from 1 to 2, with a radiation efficiency of 94 %. The proposed antenna achieves dual-band performance with increased directivity and stable gain, exhibiting enhanced electric field distribution, radiation patterns, and reflection coefficient (S11), all of which contribute to a comprehensive understanding of the antenna's performance. This study compares the designed antenna's performance to that of the fabricated prototype. The proposed antenna is ideal for 5G applications due to its small size, broad spectral coverage, and excellent gain.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".