# GNN-Enhanced Approximate Message Passing for Massive/Ultra-Massive MIMO Detection

arXiv: https://arxiv.org/abs/2205.10620 · HTML: https://mlsanity.com/paper/2205.10620/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2022.

## Built on by (most cited first, 4 of 4)

- [Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems](https://mlsanity.com/paper/2302.06896/) (2023)
- [GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection](https://mlsanity.com/paper/2404.14886/) (2024)
- [Soft Graph Transformer for MIMO Detection](https://mlsanity.com/paper/2509.12694/) (2025)
- [Graph Your Own Prompt](https://mlsanity.com/paper/2509.23373/) (2025)

## Abstract (arXiv)

Efficient massive/ultra-massive multiple-input multiple-output (MIMO) detection algorithms with satisfactory performance and low complexity are critical to meet the high throughput and ultra-low latency requirements in 5G and beyond communications, given the extremely large number of antennas. In this paper, we propose a low-complexity graph neural network (GNN) enhanced approximate message passing (AMP) algorithm, AMP-GNN, for massive/ultra-massive MIMO detection. The structure of the neural network is customized by unfolding the AMP algorithm and introducing the GNN module for multiuser interference cancellation. Numerical results will show that the proposed AMP-GNN significantly improves the performance of the AMP detector and achieves comparable performance as the state-of-the-art deep learning-based MIMO detectors but with reduced computational complexity. Furthermore, it presents strong robustness to the change of the number of users.