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.

基于图卷积的预期传播增强 MIMO 检测器 GCEPNet 提供更好的泛化能力和更快的推断速度，从而实现了最先进的 MIMO 检测性能。

GCEPNet: 基于图卷积增强的期望传播在大规模 MIMO 检测中的应用

GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive  MIMO Detection

In this paper, we innovately use graph neural networks (GNNs) to learn a
message-passing solution for the inference task of massive multiple
multiple-input multiple-output (MIMO) detection in wireless communication. We
adopt a graphical model based on the Markov random field (MRF) where belief
propagation (BP) yields poor results when it assumes a uniform prior over the
transmitted symbols. Numerical simulations show that, under the uniform prior
assumption, our GNN-based MIMO detection solution outperforms the minimum
mean-squared error (MMSE) baseline detector, in contrast to BP. Furthermore,
experiments demonstrate that the performance of the algorithm slightly improves
by incorporating MMSE information into the prior.

本文使用图神经网络（GNNs）学习图模型和马尔可夫链的随机场（MRF）来解决无线通信中的大规模 MIMO 检测问题，实验表现出比基于置信传播（BP）的 MMSE（minimum mean-squared error）基准检测器更好的性能。

大规模 MIMO 检测的图神经网络

Graph Neural Networks for Massive MIMO Detection

In this work, we consider the use of model-driven deep learning techniques
for massive multiple-input multiple-output (MIMO) detection. Compared with
conventional MIMO systems, massive MIMO promises improved spectral efficiency,
coverage and range. Unfortunately, these benefits are coming at the cost of
significantly increased computational complexity. To reduce the complexity of
signal detection and guarantee the performance, we present a learned conjugate
gradient descent network (LcgNet), which is constructed by unfolding the
iterative conjugate gradient descent (CG) detector. In the proposed network,
instead of calculating the exact values of the scalar step-sizes, we explicitly
learn their universal values. Also, we can enhance the proposed network by
augmenting the dimensions of these step-sizes. Furthermore, in order to reduce
the memory costs, a novel quantized LcgNet is proposed, where a low-resolution
nonuniform quantizer is integrated into the LcgNet to smartly quantize the
aforementioned step-sizes. The quantizer is based on a specially designed soft
staircase function with learnable parameters to adjust its shape. Meanwhile,
due to fact that the number of learnable parameters is limited, the proposed
networks are easy and fast to train. Numerical results demonstrate that the
proposed network can achieve promising performance with much lower complexity.

本研究提出了一种基于模型驱动深度学习技术的大规模 MIMO 检测器，通过构建一个学习的共轭梯度下降网络（LcgNet），明确学习它们的通用值和增强网络通过增加这些步长的维度，并通过低分辨率非均匀量化器智能量化步幅，有效地减少了复杂性和内存成本。