As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial multiplexing gains. When developing efficient physical layer algorithms for massive MIMO systems, message passing is one promising candidate owing to the superior performance. However, as their computational complexity increases dramatically with the problem size, the state-of-the-art message passing algorithms cannot be directly applied to future 6G systems, where an exceedingly large number of antennas are expected to be deployed. To address this issue, we propose a model-driven deep learning (DL) framework, namely the AMP-GNN for massive MIMO transceiver design, by considering the low complexity of the AMP algorithm and adaptability of GNNs. Specifically, the structure of the AMP-GNN network is customized by unfolding the approximate message passing (AMP) algorithm and introducing a graph neural network (GNN) module into it. The permutation equivariance property of AMP-GNN is proved, which enables the AMP-GNN to learn more efficiently and to adapt to different numbers of users. We also reveal the underlying reason why GNNs improve the AMP algorithm from the perspective of expectation propagation, which motivates us to amalgamate various GNNs with different message passing algorithms. In the simulation, we take the massive MIMO detection to exemplify that the proposed AMP-GNN significantly improves the performance of the AMP detector, achieves comparable performance as the state-of-the-art DL-based MIMO detectors, and presents strong robustness to various mismatches.

翻译：作为5G系统的核心技术之一，大规模多输入多输出（MIMO）在实现极高波束赋形和空间复用增益的同时，带来了显著的容量提升。在开发面向大规模MIMO系统的高效物理层算法时，消息传递因其优越性能而成为极具前景的候选方案。然而，随着问题规模增大，其计算复杂度急剧上升，当前最先进的的消息传递算法无法直接应用于预计将部署超大规模天线阵列的未来6G系统。针对这一问题，我们提出了一种模型驱动的深度学习框架，即AMP-GNN，用于大规模MIMO收发机设计，该框架兼顾了AMP算法的低复杂度特性和GNN的自适应性。具体而言，AMP-GNN网络的结构通过展开近似消息传递（AMP）算法并引入图神经网络（GNN）模块进行定制化设计。我们证明了AMP-GNN的置换等变性，这使得AMP-GNN能够更高效地学习并适应不同数量的用户。我们还从期望传播的角度揭示了GNN改进AMP算法的根本原因，这促使我们将各种GNN与不同的消息传递算法进行融合。在仿真中，我们以大规模MIMO检测为例，证明所提出的AMP-GNN显著提升了AMP检测器的性能，达到了与最先进的基于深度学习的MIMO检测器相当的性能，并展现出对各种失配情况的强鲁棒性。