论文标题
GNN增强的近似消息传递，以传递大规模/超质量MIMO检测
GNN-Enhanced Approximate Message Passing for Massive/Ultra-Massive MIMO Detection
论文作者
论文摘要
有效的大规模/超质量多输入多输出（MIMO）检测算法具有令人满意的性能和较低的复杂性对于满足5G和超越通信的高吞吐量和超低潜伏期需求至关重要，鉴于大量的天线。在本文中，我们提出了一个低复杂性图神经网络（GNN）增强了近似消息传递（AMP）算法AMP-GNN，以进行大规模/超质量的MIMO检测。神经网络的结构是通过展开AMP算法并引入多源干扰取消的GNN模块来定制的。数值结果将表明，所提出的AMP-GNN显着提高了AMP检测器的性能，并获得了可比的性能，作为最先进的基于深度学习的MIMO探测器，但计算复杂性降低。此外，它对用户数量的变化具有强大的鲁棒性。
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.
