Transformer-Based Hybrid Beamforming With Dynamic Subarray for Near-Space Airship-Borne Communications

2026-02-07

IEEE Wireless Communications Letters

AI Summary

This work addresses the challenge of energy-efficient hybrid beamforming in near-space airship communications, where stringent power constraints hinder the deployment of large-scale MIMO systems. To tackle this issue, the authors propose a dynamic subarray-based hybrid beamforming framework that synergistically integrates model-driven design with deep learning. The architecture employs three cooperatively trained Transformer encoder networks—namely, an analog beamforming network (ABFNet), an antenna selection network (ASNet), and a digital beamforming network (DBFNet)—to jointly optimize antenna connectivity and beamforming weights. A weighted minimum mean square error (WMMSE) criterion is embedded into the learning process to enhance robustness under imperfect channel state information. Experimental results demonstrate that the proposed scheme significantly outperforms existing baselines in both spectral and energy efficiency, while exhibiting strong scalability and practical deployment potential.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Mixture of Experts (MoE)
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• hybrid beamforming
• near-space airship
• energy efficiency
• massive MIMO
• dynamic subarray

Innovation

• Transformer-based beamforming
• dynamic subarray
• hybrid beamforming
• near-space communications
• energy efficiency