Employment of Self-Adaptive Bayesian Neural Network for Systematic Antenna Design: Improving Wireless Networks Functionalities.

Khaled Aliqab, Muhammad Ammar Sohaib, Farman Ali, Ammar Armghan, Meshari Alsharari

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Article in Micromachines, 2023. 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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5 · Who and what money

Authors and funding

5 authors.

Khaled AliqabDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.ORCID 0000-0002-6507-206X

Muhammad Ammar SohaibDepartment of Electrical Engineering, Qurtuba University of Science and IT, Dera Ismail Khan 29050, KP, Pakistan.

Farman AliDepartment of Electrical Engineering, Qurtuba University of Science and IT, Dera Ismail Khan 29050, KP, Pakistan.

Ammar ArmghanDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.ORCID 0000-0002-9062-7493

Meshari AlsharariDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia.ORCID 0000-0002-7989-3360

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The performance of wireless networks is related to the optimized structure of the antenna. Therefore, in this paper, a Machine Learning (ML)-assisted new methodology named Self-Adaptive Bayesian Neural Network (SABNN) is proposed, aiming to optimize the antenna pattern for next-generation wireless networks. In addition, the statistical analysis for the presented SABNN is evaluated in this paper and compared with the current Gaussian Process (GP). The training cost and convergence speed are also discussed in this paper. In the final stage, the proposed model's measured results are demonstrated, showing that the system has optimized outcomes with less calculation time.

Indexed as

antenna computation timeoptimized antenna patternself-adaptive Bayesian neural networkwireless network

Identifiers

PMID36985001

DOI10.3390/mi14030594

PMCPMC10053144

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LicenceCC BY

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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.