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Life sciences · Preprint
arXiv · September 9, 2026
Raises a question worth testing. It does not answer one.
This is an unrefereed computational preprint proposing three graph neural network architectures to optimize hybrid beamforming in multicarrier wideband wireless systems, with the stated aim of reducing hardware complexity and beam squinting artefacts. The work reports simulation-based performance claims and ablation studies but has not undergone peer review and contains no clinical, regulatory, or real-world deployment evidence.
Preprint. Intervention: Three GNN structures with distinct digital beamformer representations: at subcarrier nodes, at edges, and integrating singular-value decomposition solutions.. Compared with: Traditional optimization methods and existing ML-based beamforming solutions; fully digital beamforming; existing hybrid beamforming designs..
Proposed GNN structures outperform traditional optimization methods and existing ML-based solutions in simulation GNNs demonstrate resilience to beam squinting and robustness against imperfect CSI compared to fully digital beamforming Trained models generalize across diverse multicarrier and multi-user settings without retraining
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This is a preprint presenting a novel computational method for wireless beamforming optimization using graph neural networks, with no peer review, clinical validation, or deployment evidence reported.
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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 with distinct digital beamformer representations (i) at the subcarrier nodes, (ii) at the edges, or (iii) integrating traditional singular-value decomposition solutions. By designing an efficient message-passing mechanism, these structures offer insights into the impact of different GNN designs on communication system performance and computational complexity. Extensive analysis and ablation studies show that our proposed GNN structures outperform traditional optimization methods and existing ML-based solutions. Furthermore, the proposed GNNs exhibit strong resiliency to beam squinting and better robustness against imperfect CSI than even fully digital beamforming and all existing hybrid designs. These GNNs can also be extended to multi-user scenarios and demonstrate excellent generalization capabilities, allowing trained models to adapt to diverse multicarrier and multi-user settings without retraining.
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