研究人员用GNN从sub-6 GHz CSI学习毫米波波束成形，比传统方法性能更好，训练开销更低。
该研究提出使用图神经网络(GNN)从sub-6 GHz信道状态信息(CSI)中学习毫米波无蜂窝大规模多输入多输出(CFmMIMO)系统的波束成形方法。研究将CFmMIMO系统表示为无线图，GNN训练基于可用sub-6 GHz CSI最大化下行链路和速率的波束成形器。仿真结果表明，该方法在多种网络拓扑下实现了与依赖完整毫米波CSI的经典基线相当或更优的和速率性能。
Beamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI
Beamforming methods in millimeter-wave (mmWave) cell-free massive multiple-input multiple-output (CFmMIMO) systems require accurate channel state information (CSI), whose acquisition entails significant training overhead. This paper shows that fully digital cell-free mmWave beamforming can be effectively learned from sub-6 GHz CSI using a graph neural network (GNN). Specifically, we represent a CFmMIMO system as a wireless graph, and the GNN is trained to approximate beamformers that maximize the downlink sum-rate based on the available sub-6 GHz CSI. A message-passing mechanism is proposed to capture inter-user interference and inter-base-station cooperation across different network topologies. Simulation results demonstrate that the proposed sub-6 GHz-assisted GNN-based beamformer achieves competitive and often superior sum-rate performance compared to classical baselines that rely on full mmWave CSI.