ArticleScientific reports2025
A taguchi neural network-based optimization of a dual-port, dual-band MIMO antenna encompassing the 28/34 GHz millimeter wave regime.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Dual and wideband mmWave 28/38 GHz quad port MIMO antenna for 5G and beyond applications.Scientific reports · 2025Article
- Flexible four-port MIMO antenna loaded with frequency selective surface for on-body applications.Scientific reports · 2025Article
- Regression machine learning-based highly efficient dual band MIMO antenna design for mm-Wave 5G application and gain prediction.Scientific reports · 2025Article
- A shared aperture multiport antenna for rural wireless communication and safety monitoring using TVWS, ISM, and 5G mmWave bands.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents a novel printed antenna design that operates at the millimeter-wave frequencies of 28 and 34 GHz, which are crucial for the current and upcoming mobile communication generations. The radiating component in the antenna is a slot-etched rectangular ring that is fed through a stepped impedance microstrip line feed. Using advanced machine learning techniques, the design parameters of the suggested antenna have been fine-tuned to ensure optimal impedance matching at 28 GHz within the frequency range of 27.61-28.49 GHz. Additionally, the antenna also provides excellent impedance matching at 34.5 GHz within the frequency range of 33.61-34.27 GHz. Using the designated antenna, a Multiple Input Multiple Output (MIMO) system with two ports is constructed. The MIMO system's performance is evaluated by analyzing channel capacity loss (CCL), diversity gain (DG), and envelope correlation coefficient (ECC), which showcases outstanding outcomes. The study further explores the optimization of a antenna's structure using a Taguchi-based Neural Network (Taguchi NN) approach to predict the reflection coefficient (|S
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Registered trials
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