Abstract
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 $4\times 1$ 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 $4\times 1$ array antenna, the power density values similar to the actual simulation were quickly obtained as output.
Highlights
INTRODUCTIONThe mmWave frequency spectrum is different from the propagation characteristics of frequencies less than 6 GHz which are used by the existing 2G, 3G, 4G, and some of 5G mobile communications [6][7]
Mobile communication systems have evolved from 2G toThe mmWave frequency spectrum is different from the propagation characteristics of frequencies less than 6 GHz which are used by the existing 2G, 3G, 4G, and some of 5G mobile communications [6][7]
When an arbitrary phase is input to the deep learning network for a 4 × 1 array antenna, the deep learning data and the actual simulation result were compared to check whether an appropriate antenna power density value was output
Summary
The mmWave frequency spectrum is different from the propagation characteristics of frequencies less than 6 GHz which are used by the existing 2G, 3G, 4G, and some of 5G mobile communications [6][7]. In the field of antenna design and analysis, deep learning technology has been widely applied to far-field conditions such as radiation pattern synthesis. A deep learning method that can efficiently obtain the most similar power density results is presented for systems wherein a beam book composed of multiple phase conditions of an array antenna is input. The proposed method can quickly determine the possibility of a change in phase condition and construct a beam book without measuring or simulating the power density This method can be used to predict EMF human exposure requirements beforehand, without any measurement and simulation, when the overall phase of the array antenna needs to be changed according to the communication coverage adjustments in a mmWave mobile communication device.
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