Machine Learning-Driven Inspired MTM and Parasitic Ring Optimization for Enhanced Isolation and Gain in 26 GHz MIMO Antenna Arrays.

Linda Chouikhi, Chaker Essid, Bassem Ben Salah, Mongi Ben Moussa, Hedi Sakli

Abstract read

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Article in Micromachines, 2025. 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.

Linda ChouikhiSERCOM Laboratory, Tunisia Polytechnic School, University of Carthage, La Marsa P.O. Box 743-2078, Tunisia.ORCID 0000-0002-6388-1226

Chaker EssidSERCOM Laboratory, Tunisia Polytechnic School, University of Carthage, La Marsa P.O. Box 743-2078, Tunisia.ORCID 0000-0002-0594-1681

Bassem Ben SalahSERCOM Laboratory, Tunisia Polytechnic School, University of Carthage, La Marsa P.O. Box 743-2078, Tunisia.ORCID 0000-0002-1378-8904

Mongi Ben MoussaDepartment of Physics, College of Sciences, Umm Al-Qura University, Makkah 21955, Saudi Arabia.

Hedi SakliLR-Sys'Com-ENIT, Communications Systems LR-99-ES21, Ecole Nationale d'Ingénieurs de Tunis, University of Tunis El Manar, Tunis 1002, Tunisia.ORCID 0000-0002-7702-6781

Funding

Umm Al-Qura University, Saudi Arabia 25UQU 4331171GSSR04

6 · The paper itself

Abstract

This paper presents an intelligent design framework for a high-performance 26 GHz MIMO antenna array tailored to 5G applications, built upon a compact single-element patch. The 11.5 mm × 11.5 mm × 1.6 mm microstrip patch on FR4 exhibits near-unity electrical length, an ultra-deep return loss (S11 < -40 dB at 26 GHz), and a wide operational bandwidth from 24.4 to 31.2 GHz (6.8 GHz, ~26.2%). A two-element array, spaced at

Indexed as

26 GHz5Ginspired metamaterial (MTM)isolation enhancementmachine learningMIMO antenna arrayMulti-Layer Perceptronparasitic ringRandom Forest

Identifiers

PMID41156329

DOI10.3390/mi16101082

PMCPMC12565774

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