用低频信道信息训练图神经网络，实现毫米波基站无小区波束成形。
Beamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI
- 用图神经网络从低频信道信息推导毫米波波束成形方案。
- 仿真显示性能媲美甚至超过依赖全频段信道信息的经典方法。
- 适合低开销、高动态场景下的大规模无线系统设计。
毫米波（mmWave）基站无小区大规模多输入多输出（CFmMIMO）系统的波束成形需要精确的信道状态信息（CSI），而其获取带来显著的训练开销。本文表明，可通过图神经网络（GNN）从子6吉赫兹（sub-6 GHz）CSI有效学习全数字的基站无小区毫米波波束成形。具体地，将CFmMIMO系统建模为无线图结构，训练GNN基于可用的sub-6 GHz CSI逼近最大化下行链路总速率的波束成形器。提出一种消息传递机制，以捕捉不同网络拓扑下的用户间干扰与基站间协作。仿真结果表明，所提出的基于sub-6 GHz辅助的GNN波束成形器，在总速率性能上优于或媲美依赖完整mmWave CSI的经典基线方法。
原文摘要 · Abstract (English)
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.
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