This paper proposes a hybrid beamforming framework for massive multiple-input multiple-output (MIMO) in near-space airship-borne communications. To achieve high energy efficiency (EE) in energy-constraint airships, a dynamic subarray structure is introduced, where each radio frequency chain (RFC) is connected to a disjoint subset of the antennas according to channel state information (CSI). The proposed joint dynamic hybrid beamforming network (DyHBFNet) comprises three key components: 1) An analog beamforming network (ABFNet) that optimizes the analog beamforming matrices and provides auxiliary information for the antenna selection network (ASNet) design, 2) an ASNet that dynamically optimizes the connections between antennas and RFCs, and 3) a digital beamforming network (DBFNet) that optimizes digital beamforming matrices by employing a model-driven weighted minimum mean square error algorithm for improving beamforming performance and convergence speed. The proposed ABFNet, ASNet, and DBFNet are all designed based on advanced Transformer encoders. Simulation results demonstrate that the proposed framework significantly enhances spectral efficiency and EE compared to baseline schemes. Additionally, its robust performance under imperfect CSI makes it a scalable solution for practical implementations.

翻译：本文提出了一种用于临近空间飞艇通信大规模多输入多输出系统的混合波束赋形框架。为在能量受限的飞艇上实现高能效，引入了一种动态子阵列结构，其中每个射频链根据信道状态信息连接至不相交的天线子集。所提出的联合动态混合波束赋形网络包含三个关键组件：1) 模拟波束赋形网络，用于优化模拟波束赋形矩阵并为天线选择网络设计提供辅助信息；2) 天线选择网络，动态优化天线与射频链间的连接关系；3) 数字波束赋形网络，通过采用模型驱动的加权最小均方误差算法优化数字波束赋形矩阵，以提升波束赋形性能与收敛速度。所提出的模拟波束赋形网络、天线选择网络与数字波束赋形网络均基于先进的Transformer编码器构建。仿真结果表明，与基线方案相比，该框架能显著提升频谱效率与能效。此外，其在非理想信道状态信息下的鲁棒性能使其成为实际部署中可扩展的解决方案。