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
Like

Share this post

Choose a social network to share with, or copy the URL to share elsewhere

This is a representation of how your post may appear on social media. The actual post will vary between social networks

Read the paper

SpringerLink
SpringerLink SpringerLink

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.

Please sign in or register for FREE

If you are a registered user on Research Communities by Springer Nature, please sign in

Follow the Topic

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
Electronics Design and Verification
Technology and Engineering > Electrical and Electronic Engineering > Electronic Circuits and Systems > Electronics Design and Verification
Communications Engineering, Networks
Technology and Engineering > Electrical and Electronic Engineering > Communications Engineering, Networks

Related Collections

With collections, you can get published faster and increase your visibility.

Content-Aware Caching and Scheduling for Scalable Transformer Model Serving in Cluster Environments

This special issue seeks to advance the emerging paradigm of content-aware system design, where caching decisions and scheduling policies are dynamically tailored to the unique semantic and computational characteristics of incoming requests, specifically within the context of cluster environments.

Submission Guidelines: Authors should prepare their manuscript according to the Instructions for Authors available from the Cluster Computing website. Authors should submit through the online submission site at Cluster Computing and select “Content-Aware Caching and Scheduling for Scalable Transformer Model Serving in Cluster Environments" when they reach the “Article Type” step in the submission process. Submitted papers should present original, unpublished work, relevant to the topics of the special issue. Papers extending previously published conference papers are acceptable, as long as the journal submission provides a significant contribution beyond the conference paper, and the overlap is described clearly at the beginning of the journal submission. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process. Final decisions on all papers are made by the Editor in Chief.

Publishing Model: Hybrid

Deadline: Aug 31, 2025