# Beamforming Design Via GNN in mmWave Cell-Free Massive MIMO Using Sub-6 GHz CSI

arXiv: https://arxiv.org/abs/2608.30524 · HTML: https://mlsanity.com/paper/2608.30524/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 15 references that are in the map.
- First posted on arXiv 31 Aug 2026.

## Builds on (most cited first, 10 of 10)

- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications](https://mlsanity.com/paper/1902.06435/) (2019)
- [Deep Learning for mmWave Beam and Blockage Prediction Using Sub-6GHz Channels](https://mlsanity.com/paper/1910.02900/) (2019)
- [Scalable Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/1908.03119/) (2019)
- [SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2301.03512/) (2023)
- [Sub-6GHz Assisted mmWave Hybrid Beamforming with Heterogeneous Graph Neural Network](https://mlsanity.com/paper/2404.17138/) (2024)
- [Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems](https://mlsanity.com/paper/2110.15928/) (2021)
- [CSI Transfer From Sub-6G to mmWave: Reduced-Overhead Multi-User Hybrid Beamforming](https://mlsanity.com/paper/2403.10873/) (2024)
- [Knowledge Distillation for mmWave Beam Prediction Using Sub-6 GHz Channels](https://mlsanity.com/paper/2602.04703/) (2026)

## Abstract (arXiv)

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.