Citation

Tiang, Jun Jiat (2026) Dual-band stub-loaded monopole antenna with bandwidth enhancement using weighted figure-of-merit optimization. Frontiers in Artificial Intelligence, 9. ISSN 2624-8212

Text frai-9-1796177.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

This paper presents a compact dual-band stub-loaded T-monopole antenna optimized for WLAN (2.4 GHz) and 5–6 GHz (5G/IoT) applications using a weighted figure-of-merit (FOM) and artificial neural network (ANN) surrogate modeling. Low- and high-band stubs enable independent resonance control, achieving −10 dB impedance bandwidths of 1.7–2.7 GHz (45.5%) in the lower band and 5.1–5.9 GHz (14.5%) in the upper band, with return loss depths exceeding −20 dB at resonances. This outperforms a conventional reference design (22.2% lower/9.0% upper) and prior ML-optimized stub-loaded monopoles. The weighted FOM prioritizes upper-band performance for high-data-rate needs (weights w₂ = w₄ = 1.5). An ANN surrogate, trained on 210 HFSS-simulated samples, yields R2 > 0.99 (training)/>0.99 (validation), enabling rapid predictions (seconds vs. minutes per EM simulation). Radiation characteristics remain suitable (gain ~2–3.2 dBi lower/~3.5–4.3 dBi upper; efficiency >80–85%). The hybrid approach offers scalable, efficient methodology for next-generation dual-band antennas, with novelty in tunable band-prioritized FOM + ANN for legacy monopole enhancement.

Item Type:	Article
Uncontrolled Keywords:	Machine learning (ML), monopole antenna, stub-loaded antenna
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	07 Jul 2026 05:20
Last Modified:	07 Jul 2026 05:20
URII:	http://shdl.mmu.edu.my/id/eprint/16211

Downloads

Edit (login required)