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In this paper, we present a method for obtaining the power density value, which is the standard for radio frequency (RF) electromagnetic field (EMF) human exposure from mmWave mobile devices, using a deep learning network. An mmWave mobile communication device that uses an array antenna requires a large number of phase conditions for covering a wide communication range. However, the power density values must be repeatedly obtained every time the phase conditions are changed, which incurs a lot of time and cost. For implementing the process seamlessly, we present a deep learning network that can input the phase conditions of the mmWave array antenna and simultaneously obtain the power density results for the phase conditions of the array antenna as an output. For a 4 × 1 array patch antenna, which is commonly used in 5G mobile communication devices, the phases of the antenna were changed, and 5,832 electric and magnetic field data were acquired, which were then converted to power density values and learned thereafter. We examined whether appropriate power density values were output when inputting arbitrary phase sets of array antennas for the learned deep learning network. With the learned deep learning network, it was confirmed that when inputting unlearned phases for a 4 × 1 array antenna, the power density values similar to the actual simulation were quickly obtained as output.

In this paper, we present a hybrid technique for designing RAM optimally to reduce the RCS of complex targets in a wide-band frequency range. The technique combines a high-frequency method and a genetic algorithm (GA) to obtain an optimal RAM in complex targets. By the virtue of the high-frequency method, such as the physical optics (PO) method and the method of equivalent currents (MEC), the proposed technique can be applied to complex targets with relative ease. However, the high-frequency method needs a classification of shadow regions as pre-processing. A Z-buffer algorithm is employed in this process. The procedure results in designing the optimal RAM which significantly reduces the RCS of complex targets.

We propose a deep neural network (DNN) to determine the matching circuit parameters for antenna impedance matching. The DNN determines the element values of the matching circuit without requiring a mathematical description of matching methods, and it approximates feasible solutions even for unimplementable inputs. For matching, the magnitude and phase of impedance should be known in general. In contrast, the element values of the matching circuit can be determined only using the impedance magnitude using the proposed DNN. A gamma-matching circuit consisting of a series capacitor and a parallel capacitor was applied to a conventional inverted-F antenna for impedance matching. For learning, the magnitude of input impedance S11 of the antenna was extracted according to the element values of the matching circuit. A total of 377 training samples and 66 validation samples were obtained. The DNN was then constructed considering the magnitude of impedance S11 as the input and the element values of the matching circuit as the output. During training, the loss converged as the number of epochs increased. In addition, the desired matching values for unlearned square and triangular waves were obtained during testing.