Problem
Research questions and friction points this paper is trying to address.
Beamforming
mmWave
Cell-Free Massive MIMO
Channel State Information
Training Overhead
Beamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI
🤖 AI Summary
本文利用图神经网络（GNN）从Sub-6 GHz信道状态信息中学习毫米波无蜂窝大规模MIMO系统的波束成形，以降低训练开销并提高下行总速率。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Graph Neural Network
Sub-6 GHz CSI
Beamforming
mmWave Cell-Free MIMO
Message Passing
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