Dual-band stub-loaded monopole antenna with bandwidth enhancement using weighted figure-of-merit optimization

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 w_₂  = w_₄  = 1.5). An ANN surrogate, trained on 210 HFSS-simulated samples, yields R ² > 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.