A Planar Array Beamforming Method Based on Transfer Learning

A training method based on transfer learning is proposed in this article, which completes the beamforming task of a deep neural network based on ResNet through two stages: "data approximation" and "pattern approximation". It is shown in the numerical simulations that the proposed method achieves better performance in beamforming tasks, compared to the Dolph-Chebyshev method and the Genetic Algorithm. The model can generate complex excitation for arbitrary beam directions within the 0∼45° off-axis angle range and control the sidelobe levels within the 20dB∼40dB range. Furthermore, with the support of suitable computation platform, the synthesis process can be completed in about 0.04 seconds, which meets practical requirements in real-time synthesis, especially in the field of 5G communications.