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
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzyOverlapping authors or contributors · 62%open-webui/open-webui →
“Shared author/contributor keys: nguyen”
- LinkedLinked via arxiv author · 85%Sina Tavakolian →
“Beamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI”
- LinkedLinked via arxiv author · 85%Abolfazl Zakeri →
“Beamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI”
- LinkedLinked via arxiv author · 85%Ahmed Alkhateeb →
“Beamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI”
- LinkedLinked via arxiv author · 85%Markku Juntti →
“Beamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI”
- LinkedLinked via arxiv author · 85%Nhan Thanh Nguyen →
“Beamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI”