This paper proposes a Transformer-based hybrid beamforming framework for reconfigurable pixel antenna (RPA)-equipped massive multiple-input multiple-output (MIMO) in high-altitude platform station (HAPS) communications. The proposed pattern reconfigurable hybrid beamforming network (PR-HBFNet) comprises two key components: 1) a pattern reconfigurable network that leverages a Transformer encoder to determine the radiation pattern for each antenna element, and 2) a hybrid beamforming network that employs model-driven residual learning to compute analog and digital precoders over SVD-based initializations. Simulation results demonstrate that the proposed PR-HBFNet closely approaches the spectral efficiency of a greedy benchmark while significantly reducing computational complexity.

翻译：本文提出了一种基于Transformer的混合波束赋形框架，用于高空平台站（HAPS）通信中配备可重构像素天线（RPA）的大规模多输入多输出（MIMO）系统。所提出的模式可重构混合波束赋形网络（PR-HBFNet）包含两个关键组件：1）模式可重构网络，利用Transformer编码器确定每个天线单元的辐射模式；2）混合波束赋形网络，采用模型驱动的残差学习方法，基于SVD初始化计算模拟和数字预编码器。仿真结果表明，所提出的PR-HBFNet在显著降低计算复杂度的同时，其频谱效率接近贪婪基准方案。