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To mitigate severe beam squinting in wideband 6G systems without costly true-time-delay hardware, this work reformulates multicarrier hybrid beamforming optimization over bipartite Graph Neural Network (GNN) architectures. By mapping shared analog and subcarrier-specific digital beamformers onto graph nodes and edges with specialized message-passing schemes, the models learn to solve the non-convex optimization problem efficiently. The resulting GNNs outperform both iterative numerical solvers and prior learning-based baselines, maintaining superior robustness to imperfect channel state information over fully digital beamforming and generalizing zero-shot to unseen subcarrier and user counts.
Domain-structured bipartite GNNs beat fully digital beamforming under noisy channel conditions while solving severe wideband beam squinting without expensive hardware modifications.
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.