ArticleScientific reports2025
High performance grid structured MIMO antenna with regression machine learning for high-speed sub THz and THz 6G IoT applications.
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 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Machine learning-assisted validation of a high-isolation THz MIMO antenna for 6G communication and IoT application.Scientific reports · 2026Article
- Circular MIMO antenna with ML-based bandwidth and isolation prediction for 6G communications.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
This study introduces an innovative approach that leverages machine learning techniques to optimize antenna gain for next-generation wireless communication and Internet of Things (IoT) systems operating in the Terahertz (THz) frequency spectrum. Designed on a 160 × 160 μm² polyimide substrate, the antenna is analyzed using CST-2018 simulations and RLC circuit modeling. The proposed antenna demonstrates outstanding performance by achieving high peak gains of 11.91 dB and 12.21 dB across two operational bands. Exceptional isolation values of 31.43 dB and 36.1 dB are maintained in their respective bands, along with a high radiation efficiency of 92.42% and 86.93%. The design effectively covers two wide frequency ranges: 0.081-1.36 THz (1.2 THz bandwidth) and 1.81-3.43 THz (1.6 THz bandwidth), making it highly suitable for THz communication scenarios. To enhance the validation of the model, an analogous RLC equivalent model is constructed via ADS, yielding S
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