A Deep Learning-Based Approach for Radiation Pattern Synthesis of an Array Antenna
Jae Hee Kim, Sang Won Choi
Abstract
In this article, we propose a deep neural network (DNN)for the radiation pattern synthesis of an antenna. The DNN utilizes the radiation patterns as inputs and the amplitude and phase of the antenna elements as outputs. Consequently, the radiation patterns of the array antenna can be easily obtained from the outputs of the trained DNN, which are amplitude and phase of the antenna elements. However, it is difficult to determine the amplitude and phase of each antenna element from the desired pattern in an environment where inter-element coupling exists. For this purpose, 6,859 radiation pattern samples for a 4×1 array patch antenna were generated by changing the phases of the antenna elements, and those patterns were leveraged to train the proposed DNN with low complexity. The radiation patterns of the ideal square and triangular array shapes, which are practically infeasible to implement, were used as inputs to the DNN. It was confirmed that the radiation pattern generated from the output signals of the DNN was very similar to the input radiation pattern.