Deep Learning Based Phase Calibration for Enhanced Beam Control Accuracy

Phased array antennas are a cornerstone of next-generation communication and radar systems, where precise beam steering is essential. In practical applications, the use of 6-bit Beamforming Integrated Circuits (BFICs) imposes discrete phase control, while mutual coupling between antenna elements introduces significant errors, making conventional beam steering calculations based on Array Factor (AF) theory inaccurate. This paper proposes a novel deep learning framework to overcome these limitations. We designed and compared three deep learning architectures—Multi-Layer Perceptron (MLP), 1D Convolutional Neural Network (1D-CNN), and Long Short-Term Memory (LSTM)—to predict the optimal phase correction values for a 4x4 microstrip patch array. The models are trained on a dataset generated via full-wave electromagnetic simulations to learn the complex, non-linear relationship between the target beam angle and the required phase compensation. The results demonstrate that the proposed models, particularly the 1D-CNN, can effectively learn to compensate for mutual coupling and quantization effects, predicting the precise phase configurations required to steer the beam to the target angle with high accuracy. This approach enables real-time, high-precision phase calibration without the need for complex iterative simulations, paving the way for intelligent antenna systems.