  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.
