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