ArticleScientific reports2025
Regression machine learning-based highly efficient dual band MIMO antenna design for mm-Wave 5G application and gain prediction.
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 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine learning-assisted validation of a high-isolation THz MIMO antenna for 6G communication and IoT application.Scientific reports · 2026Article
- Deep learning approach for hybrid beamforming design in MU-MISO mmWave systems.Scientific reports · 2026Article
- Predictive modelling and high-performance enhancement smart thz antennas for 6 g applications using regression machine learning approaches.Scientific reports · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
With the exponential growth of wireless communication systems, the need for compact, high-performance antennas operating at millimeter-wave (mm-Wave) frequencies has become increasingly critical. This paper presents a comprehensive design and performance analysis of a microstrip patch antenna system operating at dual frequencies of 28 GHz and 38 GHz, suitable for 5G and beyond applications. The antenna evolves from a single element to a 2-element array and a 4-port MIMO configuration, achieving high gains of 9 dB and 8.4 dB, respectively. It covers wide bandwidths of 2.55 GHz and 5.77 GHz within the operating ranges of 26.73-29.28 GHz and 34.96-40.73 GHz. Designed on a Rogers RT5880 substrate, the antenna measures 31.26 mm × 31.26 mm (2.92λ
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