Machine learning driven four-elements high gain MIMO antenna for wireless connectivity

Research title : Machine learning driven four-elements high gain MIMO antenna for wireless connectivity Title DOI/Link : https://doi.org//10.1007/s10586-024-04613-1 Author role : Corresponding author Journal name : Cluster Computing Impact factor : 4.4 Publisher : Springer Journal ranking : Q1
Machine learning driven four-elements high gain MIMO antenna for wireless connectivity
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Machine learning driven four-elements high gain MIMO antenna for wireless connectivity - Cluster Computing

With innovative technologies developing at a rapid pace, machine learning has become essential to improve interdisciplinary applications such as wireless communications systems. In order to optimize, design and develop a compact $$(0.6 \lambda \times 0.6\lambda \times 0.03\lambda \, \text {mm}^3)$$ ( 0.6 λ × 0.6 λ × 0.03 λ mm 3 ) four-elements MIMO antenna at 2.4 GHz resonating frequency, this study uses a machine learning technique. A single element antenna is selected among several steps (1–4) and thereafter, an antenna (A3) turns out to be the best option out of the antennas that were taken into consideration (A1–A3). The optimal antenna (A3) dimensions were carefully ascertained by means of a thorough investigation of 245,700 distinct iterations. The proposed antenna consists of a petal-shaped structure on the center of the radiating plane and rectangular ring with defected semicircles on the ground plane. The antenna exhibits 72.3% (simulated) and 70.2% (measured) impedance bandwidth with a high value of 20 dB of isolation (at 3 GHz). The suggested antenna achieves a maximum gain and radiation efficiency of 9.2 dB and 80% at 2.1 GHz, respectively. Moreover, MIMO characteristics such as Envelope Correlation Coefficient (ECC), Total Active Reflection Coefficient (TARC) and Channel Capacity Loss (CCL) of the intended antenna are studied and found to be well within acceptable limits. The suggested antenna is analysed by means of EM simulator high-frequency structure simulator (HFSS), thereafter it is fabricated and measured and findings show a fair degree of agreement.

With innovative technologies developing at a rapid pace, machine learning has become essential to improve interdisciplinary applications such as wireless communications systems. In order to optimize, design and develop a compact ð0:6k 0:6k 0:03kmm3Þ four-elements MIMO antenna at 2.4 GHz resonating frequency, this study uses a machine learning technique. A single element antenna is selected among several steps (1–4) and thereafter, an antenna (A3) turns out to be the best option out of the antennas that were taken into consideration (A1–A3). The optimal antenna (A3) dimensions were carefully ascertained by means of a thorough investigation of 245,700 distinct iterations. The proposed antenna consists of a petal-shaped structure on the center of the radiating plane and rectangular ring with defected semicircles on the ground plane. The antenna exhibits 72.3% (simulated) and 70.2% (measured) impedance bandwidth with a high value of 20 dB of isolation (at 3 GHz). The suggested antenna achieves a maximum gain and radiation efficiency of 9.2 dB and 80% at 2.1 GHz, respectively. Moreover, MIMO characteristics such as Envelope Correlation Coefficient (ECC), Total Active Reflection Coefficient (TARC) and Channel Capacity Loss (CCL) of the intended antenna are studied and found to be well within acceptable limits. The suggested antenna is analysed by means of EM simulator high-frequency structure simulator (HFSS), thereafter it is fabricated and measured and findings show a fair degree of agreement.

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Machine Learning
Mathematics and Computing > Statistics > Statistics and Computing > Machine Learning
Antenna Complex
Life Sciences > Biological Sciences > Plant Science > Photosynthesis > Antenna Complex
Microwaves, RF Engineering and Optical Communications
Technology and Engineering > Electrical and Electronic Engineering > Microwaves, RF Engineering and Optical Communications
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Technology and Engineering > Electrical and Electronic Engineering > Electronic Circuits and Systems > Electronics Design and Verification
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Advancements in Service-Oriented Software 2024

The aim of the special issue is to provide a platform for researchers and practitioners to showcase their work and share their experiences on the application of service-oriented software in various domains. The special issue aims to cover a wide range of topics related to the practical applications of microservices, service orientation, and cloud computing.

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