Convolutional Neural Network Algorithm and Application Method for Real-Time Beam Steering in RF System

This paper presents a novel artificial intelligence (AI)-based phase shift system in a beamforming system implemented with field programmable gate array (FPGA)-based hardware by integrating a conventional convolutional neural network (CNN) algorithm. The position of the target can be determined through a phase shifter in a beamforming system using artificial intelligence. In a system that emits a beam from a radio frequency (RF) transmitter and receives a beam from an RF receiver, artificial intelligence can control the phase. It controls the phase of the transmitter for beam scanning and the phase to optimize the signal-to-noise ratio (SNR) of the receiver. The position of the target was detected by learning the signal input data from the receiver. Targets were detected through two-beam scanning processes in a 3D space. The first is a coarse process of detecting the approximate position of the target in the entire space, and the second is a fine process of detecting the area in detail after detecting the first approximate position. The phases of the individual antennae should be controlled for optimal beamforming based on the 5x 5 antenna, and the phase is detected at high speed by holding the phase large in the first coarse tuning. The second scan entails a narrow range scan with a small phase to detect it at a high speed accurately. This study shows that with FPGA, AI beamforming can be implemented through two scanning methods without image sensors. Based on the receiver's 5x5 antenna, the CNN input feature consisted of 35x35 classifies the class with high accuracy.