Adaptive Antenna Impedance Matching Using Low-Complexity Shallow Learning Model

In this paper, an automatic antenna impedance matching technique is presented for a wide range of frequency bands where low-complexity shallow learning model adaptively determines the component values circuit on real-time. general, networks require both real and imaginary parts to determine tuning parameters which involves expensive measurement equipment. The developed in work needs only magnitude reflection coefficients ( S 11 ) construct circuit. First, were theoretically calculated used computer simulations generate data. total 500 samples generated, which, 400 training 100 validation. proposed was applied novel inverted-F resonating at 2.45 GHz matching. achieved results show stable performances over frequencies from 2 3 GHz. For validation, simulated using predicted parameters. Results confirm comparable terms antenna’s resonance frequency, coefficients, operational 10-dB bandwidth. fast prediction ability low complexity makes it suitable real-time applications. Moreover, repeated xmlns:xlink="http://www.w3.org/1999/xlink">K -fold cross validation confirms 0.99985 accuracy when 15 times.