ArticleHeliyon2023
Machine learning-based technique for resonance and directivity prediction of UMTS LTE band quasi Yagi antenna.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Data-driven approaches for predictive modeling of N77/N78 band 5G microstrip patch array antenna using curve fitting and supervised regression.Scientific reports · 2026Article
- Machine learning-optimized compact wearable frequency reconfigurable antenna for sub-6 GHz/mm-wave 5G integration.Scientific reports · 2025Article
- Machine learning-optimized dual-band wearable antenna for real-time remote patient monitoring in biomedical IoT systems.Scientific reports · 2025Article
- Metamaterial based tri-band compact MIMO antenna system for 5G IoT applications with machine learning performance verification.Scientific reports · 2025Article
- Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applications.Scientific reports · 2025Article
- Performance prediction and optimization of a high-efficiency tessellated diamond fractal MIMO antenna for terahertz 6G communication using machine learning approaches.Scientific reports · 2025Article
- 3D highly isolated 6-port tri-band MIMO antenna system with 360° coverage for 5G IoT applications based machine learning verification.Scientific reports · 2025Article
- Machine learning-based technique for gain prediction of mm-wave miniaturized 5G MIMO slotted antenna array with high isolation characteristics.Scientific reports · 2025Article
- Machine learning-based novel-shaped THz MIMO antenna with a slotted ground plane for future 6G applications.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we have presented our findings on the deployment of a machine learning (ML) technique to enhance the performance of LTE applications employing quasi-Yagi-Uda antennas at 2100 MHz UMTS band. A number of techniques, including simulation, measurement, and a model of an RLC-equivalent circuit, are discussed in this article as ways to assess an antenna's suitability for the intended applications. The CST simulation gives the suggested antenna a reflection coefficient of -38.40 dB at 2.1 GHz and a bandwidth of 357 MHz (1.95 GHz-2.31 GHz) at a -10 dB level. With a dimension of 0.535
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.