Real-Time Pattern Synthesis for Large-Scale Phased Arrays Based on Autoencoder Network and Knowledge Distillation

Abstract

In this article, a deep learning (DL) method based on autoencoder network is proposed to achieve the inverse design of phase retrieval for large-scale antenna arrays. The inverse problem between the beam pattern and antenna phases is established first in the context of planar phased array. Inception-Resnet-V2 with prior knowledge (IR-PK) is proposed as an efficient model, which involves the prior knowledge of array factor to guide neural network (NN) learning for stronger fitting ability. To obtain the real-time phase retrieval in small terminals, a MobileNet-distilled IR-PK (MD-IR-PK) model combining lightweight architecture and knowledge distillation (KD) is then designed under the condition of limited resources. The method is validated for array beamforming and hologram. Compared with popular solutions, IR-PK shows the advantages of good accuracy, fast convergence, and computational efficiency. Experiments have been carried out for metasurface-based holography, with the measured results agreeing well with the simulated ones. The proposed method is competitive for complex electromagnetic (EM) inverse problems involving high nonlinearity.