电波科学学报2026，Vol.41Issue(1)：89-97,9.DOI:10.12265/j.cjors.2025160
深度学习辅助的编码超表面阵列智能设计
Deep learning-assisted intelligent design of coding metasurface arrays
摘要
Abstract
To address the communication demands in complex scenarios such as satellite-terrestrial collaborative communication,low-altitude economy,and smart cities,this study proposes a convolutional neural network(CNN)for fast beamforming with coding metasurface arrays,enabling a direct inverse design process.The CNN is primarily based on the visual geometry group(VGG)network architecture,incorporating channel attention mechanisms to further enhance its prediction accuracy.Dataset is collected through phase compensation and multi-population genetic algorithm(MPGA).The trained network can generate coding matrices for both single-beam and dual-beam within milliseconds,achieving 40%faster training convergence,and 1.31%higher prediction accuracy compared to existing benchmark networks.This network significantly improves the design efficiency of large-scale coding metasurface arrays,offering a feasible solution for fast and real-time control of coding metasurface arrays.关键词
编码超表面/波束赋形/深度学习/卷积神经网络(CNN)/通道注意力Key words
coding metasurface/beamforming/deep learning/convolutional neural network(CNN)/channel attention mechanism分类
信息技术与安全科学引用本文复制引用
张子奕,张嘉男,游检卫..深度学习辅助的编码超表面阵列智能设计[J].电波科学学报,2026,41(1):89-97,9.基金项目
国家自然科学基金(62401132) （62401132）
国家重点研发计划(2023YFC2411100,2023YFB3813100) （2023YFC2411100,2023YFB3813100）
东南大学科研启动经费(RF1028623061)National Natural Science Foundation of China(62401132) （RF1028623061）
National Key Research and Development Program of China(2023YFC2411100,2023YFB3813100) （2023YFC2411100,2023YFB3813100）
Southeast University Research Start-up Fund(RF1028623061) （RF1028623061）
