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.
@article{arxiv.2605.17858,
title = {Transformer-Based Hybrid Beamforming with Reconfigurable Pixel Antenna for HAPS Communications},
author = {Ruiqi Wang and Ziwei Wan and Keke Ying and Zhen Gao},
journal= {arXiv preprint arXiv:2605.17858},
year = {2026}
}