Graph Neural Networks for Massive MIMO Detection

• URL: http://arxiv.org/abs/2007.05703v1
• Date: Sat, 11 Jul 2020 07:34:56 GMT
• Title: Graph Neural Networks for Massive MIMO Detection
• Authors: Andrea Scotti, Nima N. Moghadam, Dong Liu, Karl Gafvert, Jinliang Huang
• Abstract summary: We learn a message-passing solution for the inference task of massive 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.
• Score: 8.516590865173407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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