SOURCE-LINKED INTELLIGENCE

Efficient Graph Neural Networks for Multicarrier Wideband Hybrid Beamforming Optimization

arXiv · AI, language, vision and robotics · article · Sep 9, 2026 · UTC

6G wireless technology is poised to adopt higher and wider frequency bands, leveraging highly directional beamforming. However, the vast bandwidths amplify the impact of beam squinting. Traditional solutions, such as adding a true-time-delay filter to each antenna, are cost-prohibitive due to the required hardware scale. This paper proposes a signal processing alternative using Graph Neural Networks (GNNs) to optimize hybrid beamforming in multicarrier wideband systems. Using a bipartite graph to represent a shared analog beamformer among multiple subcarriers, we develop three GNN structures w

Read original source ↗ Open in workspace

recordType

paper

region

Global