arXiv:2511.06663v2 Announce Type: replace-cross Abstract: Accurate Channel State Information (CSI) is critical for Hybrid Beamforming (HBF) tasks. However, obtaining high-resolution CSI remains challenging in practical wireless communication systems. To address this issue, we propose to utilize Graph Neural Networks (GNNs) and score-based generative models to enable robust HBF under imperfect CSI conditions. Firstly, we develop the Hybrid Message Graph Attention Network (HMGAT) which updates both node and edge features through node-level and edge-level message passing. Secondly, we design a Bi
The increasing complexity of wireless communication systems and the demand for higher data rates necessitate more robust and efficient beamforming techniques, which AI can significantly enhance.
This development represents a step towards more reliable and higher-performance wireless networks by applying advanced AI methods to overcome fundamental challenges in signal processing.
The application of GNNs and score-based generative models promises more accurate and resilient Channel State Information (CSI) acquisition, leading to improved Hybrid Beamforming (HBF) in dynamic environments.
- · Telecommunication network providers
- · 5G/6G equipment manufacturers
- · AI/ML research institutions
- · Edge computing industries
- · Traditional CSI estimation methods
- · Communication systems with limited AI integration
Improved spectral efficiency and reduced interference in wireless communication systems.
Faster and more reliable data transmission will enable more sophisticated IoT and autonomous systems.
Enhanced wireless performance could accelerate the development of pervasive AI applications that rely on real-time, high-bandwidth connectivity.
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