Evidence map›Paper›PMID 42560968›Full record

ArticlePloS one2026

Machine learning-inspired design of a modified spider-shaped MIMO antenna for 5G-advanced wireless communications.

Ashwini Kumar, Ashish Kumar, Gurprince Singh, Neeraj, I M Ibrahim, A J A Al-Gburi

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

2 · The registry

The trial behind it

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ashwini KumarDeaprtment of Computer Engineering, Poornima Institute of Engineering and Technology, Jaipur, India.
Ashish KumarChitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.ORCID https://orcid.org/0000-0002-8465-5973
Gurprince SinghDepartment of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India.ORCID https://orcid.org/0000-0003-2693-6387
NeerajDepartment of Computer Science Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India.
I M IbrahimCenter for Telecommunication Research & Innovation (CeTRI), Fakulti Teknologi Dan Kejuruteraan Elektronik Dan Komputer (FTKEK), Jalan Hang Tuah Jaya, Durian Tunggal, Melaka, Universiti Teknikal Malaysia Melaka (UTeM), Malacca, Malaysia.ORCID https://orcid.org/0000-0002-7177-0542
A J A Al-GburiStrategic Research Institute (SRI), Asia Pacific University, Jalan Teknologi 5, Taman Teknologi Malaysia, Kuala Lumpur, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A modified Spider Shaped Four Element Multi Input Multi Output Antenna (SSFEMIMOA) is suggested for the 5G-advanced, and sub-6 GHz advanced wireless communications. The proposed SSFEMIMOA exhibits a low-profile and compact configuration with an overall size of 0.36λ² (52 × 52 × 1.57 mm³), achieved through the optimal placement of the constituent antenna elements. The top layer of the unit-cell antenna is designed in a modified spider-shaped geometry incorporating an additional oval-shaped structure. The modified partial ground structure alters the current distribution and electromagnetic coupling within the antenna, thereby improving impedance matching and broadening the operating bandwidth. The diversity parameters of the SSFEMIMOA have been attained by placing the elements in orthogonal position to each other. The anticipated design attains the BW ≈ 5.5 GHz (3.5-9.05) with maximum gain of 6.1 dB and isolation between -20 to -40 dB while satisfying the MIMO diversity parameters in terms of Envelope Correlation Coefficient (ECC), Directive Gain (DG), Channel Capacity Loss (CCL) and Total Active Reflection Coefficient (TARC). To enhance the design optimization process, multiple Machine Learning (ML) algorithms were employed and systematically evaluated. Based on the obtained performance metrics, Gaussian Process Regression (GPR) exhibited the best predictive capability, yielding the lowest prediction error and highest accuracy compared to the other investigated algorithms. Finally, the proposed SSFEMIMOA was fabricated and validated through measurements of S-parameters, transmission coefficients, radiation characteristics, and other relevant performance parameters. The experimental results exhibit excellent impedance matching and radiation performance, making the antenna a promising candidate for emerging 5G/6G advanced wireless communication systems.

Indexed as

Machine LearningWireless TechnologyEquipment Design

Identifiers

PMID42560968
PMCPMC13446664

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.