Massive MIMO (multiple-input multiple-output) detection is an important topic in wireless communication and various machine learning based methods have been developed recently for this task. Expectation propagation (EP) and its variants are widely used for MIMO detection and have achieved the best performance. However, EP-based solvers fail to capture the correlation between unknown variables, leading to loss of information, and in addition, they are computationally expensive. In this paper, we show that the real-valued system can be modeled as spectral signal convolution on graph, through which the correlation between unknown variables can be captured. Based on this analysis, we propose graph convolution-enhanced expectation propagation (GCEPNet), a graph convolution-enhanced EP detector. GCEPNet incorporates data-dependent attention scores into Chebyshev polynomial for powerful graph convolution with better generalization capacity. It enables a better estimation of the cavity distribution for EP and empirically achieves the state-of-the-art (SOTA) MIMO detection performance with much faster inference speed. To our knowledge, we are the first to shed light on the connection between the system model and graph convolution, and the first to design the data-dependent attention scores for graph convolution.

翻译：大规模MIMO（多输入多输出）检测是无线通信领域的重要课题，近年来已开发出多种基于机器学习的方法用于该任务。期望传播（EP）及其变体被广泛应用于MIMO检测，并取得了最优性能。然而，基于EP的求解器未能捕获未知变量之间的相关性，导致信息损失，且其计算成本高昂。在本文中，我们证明实数系统可建模为图上的频谱信号卷积，从而能够捕获未知变量之间的相关性。基于这一分析，我们提出图卷积增强期望传播（GCEPNet），一种图卷积增强的EP检测器。GCEPNet将数据依赖的注意力分数引入Chebyshev多项式，实现具有更强泛化能力的高效图卷积。它使EP的腔体分布估计更精确，并在经验上以更快的推理速度达到当前最先进的MIMO检测性能。据我们所知，这是首次揭示系统模型与图卷积之间的关联，同时首次为图卷积设计数据依赖的注意力分数。