Abstract
We designed the wire monopole antenna bent at three points by applying a machine learning technique to achieve a good impedance matching characteristic. After performing the deep neural network (DNN)-based training, we validated our machine learning model by evaluating mean squared error and R-squared score. Considering the mean squared error of about zero and R-squared score of about one, the performance prediction by the resulting machine learning model showed a high accuracy compared with that by the numerical electromagnetic simulation. Finally, we interpreted the operating principle of the antennas with a good impedance matching characteristic by analyzing equivalent circuits corresponding to their structures. The accomplished works in this research provide us with the possibility to use the machine learning technique in the antenna design.
Highlights
The wire monopole antenna is one of the most popular antennas because it provides an omni-directional radiation pattern, high radiation efficiency, and easy fabrication, etc. [1,2,3]
The geometry of a wire monopole antenna can be modified in various manners to improve antenna performances: control of operating frequency bands by adding resonating elements [4,5], increment of antenna bandwidth by applying the tapered structure for a feeder [6], enhancement of the radiation gain by equipping with director and reflector wires [7,8], reduction of antenna size by folding the antenna body [9,10,11], achievement of favorable impedance matching by placing shorting wires or capacitive loads at the optimum location of an antenna body [9,12,13,14], and control of both radiation pattern and polarization by rendering antenna arms bent or folded [15,16,17]
Among the modified wire monopole antennas, the bent wire antennas such as an inverted-L antenna have good performances in the reduction of antenna size, as well as impedance matching because the angles between wire elements and the lengths of the wire elements affect the antenna impedance and the current induced on the wire [3,9,17,18]
Summary
The wire monopole antenna is one of the most popular antennas because it provides an omni-directional radiation pattern, high radiation efficiency, and easy fabrication, etc. [1,2,3]. As is well known, the reduction of the antenna size simultaneously yields the increase in quality (Q) factor resulting in the diminishment of the antenna bandwidth [19,20]. To overcome the aforementioned limitation in antenna design, machine learning (ML) techniques can be one of the solutions because the resulting ML model approximately provides the proper structure of an antenna without the EM simulation [23,24,25]. In [23], machine learning (ML) techniques such as least absolute shrinkage and selection operator (lasso), artificial neural networks (ANNs), and k-nearest neighbor (kNN) provide an efficient framework to identify optimal design for a T-shaped monopole antenna. Inspired by the powerful applicability of the machine learning techniques, we designed the bent monopole antenna having a good impedance matching characteristic using a deep neural network (DNN).
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