  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.
