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

Understand

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 a 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.