Message Passing Meets Graph Neural Networks : A New Paradigm for Massive MIMO Systems

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Scopus Citations
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  • Hengtao He
  • Xianghao Yu
  • Jun Zhang
  • Shenghui Song
  • Khaled B. Letaief

Related Research Unit(s)


Original languageEnglish
Journal / PublicationIEEE Transactions on Wireless Communications
Online published10 Oct 2023
Publication statusOnline published - 10 Oct 2023


As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial multiplexing gains. When developing efficient physical layer algorithms for massive MIMO systems, message passing is one promising candidate owing to its superior performance. However, as their computational complexity increases dramatically with the problem size, the state-of-the-art message passing algorithms cannot be directly applied to future 6G systems, where an exceedingly large number of antennas are expected to be deployed. To address this issue, we propose a model-driven deep learning (DL) framework, namely the AMP-GNN for massive MIMO transceiver design, by considering the low complexity of the AMP algorithm and adaptability of GNNs. Specifically, the structure of the AMP-GNN network is customized by unfolding the approximate message passing (AMP) algorithm and introducing a graph neural network (GNN) module into it. The permutation equivariance property of AMP-GNN is proved, which enables the AMP-GNN to learn more efficiently and to adapt to different numbers of users. We also reveal the underlying reason why GNNs improve the AMP algorithm from the perspective of expectation propagation, which motivates us to amalgamate various GNNs with different message passing algorithms. In the simulation, we take the massive MIMO detection to exemplify that the proposed AMP-GNN significantly improves the performance of the AMP detector, achieves comparable performance as the state-of-the-art DL-based MIMO detectors, and presents strong robustness to various mismatches. © 2023 IEEE.

Research Area(s)

  • 6G, Approximation algorithms, Bayesian inference, Channel estimation, deep learning, Detectors, graph neural networks, Inference algorithms, massive MIMO, Message passing, model-driven, Wireless networks