Transformer-based Hybrid Beamforming with Dynamic Subarray for Near-Space Airship-Borne Communications

TL;DR

This paper introduces a Transformer-based hybrid beamforming framework with a dynamic subarray structure for near-space airship communications, significantly improving spectral efficiency and energy efficiency in energy-constrained environments.

Contribution

It presents a novel joint dynamic hybrid beamforming network (DyHBFNet) utilizing Transformer encoders for adaptive antenna-RFC connections and optimized beamforming in airship-borne MIMO systems.

Findings

Enhanced spectral efficiency and energy efficiency demonstrated in simulations.

Robust performance under imperfect CSI conditions.

Scalable solution suitable for practical near-space airship communications.

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.