# Graph Neural Networks for Massive MIMO Detection

arXiv: https://arxiv.org/abs/2007.05703 · HTML: https://mlsanity.com/paper/2007.05703/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2020.

## Built on by (most cited first, 9 of 9)

- [Graph Neural Networks for Wireless Communications: From Theory to Practice](https://mlsanity.com/paper/2203.10800/) (2022)
- [Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems](https://mlsanity.com/paper/2302.06896/) (2023)
- [Privacy-Preserving Decentralized Inference with Graph Neural Networks in Wireless Networks](https://mlsanity.com/paper/2208.06963/) (2022)
- [GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection](https://mlsanity.com/paper/2404.14886/) (2024)
- [Learn to Communicate with Neural Calibration: Scalability and Generalization](https://mlsanity.com/paper/2110.00272/) (2021)
- [Graph Neural Network Aided MU-MIMO Detectors](https://mlsanity.com/paper/2206.09381/) (2022)
- [Edge Graph Neural Networks for Massive MIMO Detection](https://mlsanity.com/paper/2206.06979/) (2022)
- [A Transformer Inspired AI-based MIMO receiver](https://mlsanity.com/paper/2510.20363/) (2025)
- [GD4: Graph-based Discrete Denoising Diffusion for MIMO Detection](https://mlsanity.com/paper/2605.00423/) (2026)

## Abstract (arXiv)

In this paper, we innovately use graph neural networks (GNNs) to learn a message-passing solution for the inference task of massive multiple multiple-input multiple-output (MIMO) detection in wireless communication. We adopt a graphical model based on the Markov random field (MRF) where belief propagation (BP) yields poor results when it assumes a uniform prior over the transmitted symbols. Numerical simulations show that, under the uniform prior assumption, our GNN-based MIMO detection solution outperforms the minimum mean-squared error (MMSE) baseline detector, in contrast to BP. Furthermore, experiments demonstrate that the performance of the algorithm slightly improves by incorporating MMSE information into the prior.