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 20 dB to 40 dB range. Furthermore, with the support of a 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.