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As one of the core technologies for 5G systems, massive multiple-inputmultiple-output (MIMO) introduces dramatic capacity improvements along withvery high beamforming and spatial multiplexing gains. When developing efficientphysical layer algorithms for massive MIMO systems, message passing is onepromising candidate owing to the superior performance. However, as theircomputational complexity increases dramatically with the problem size, thestate-of-the-art message passing algorithms cannot be directly applied tofuture 6G systems, where an exceedingly large number of antennas are expectedto be deployed. To address this issue, we propose a model-driven deep learning(DL) framework, namely the AMP-GNN for massive MIMO transceiver design, byconsidering the low complexity of the AMP algorithm and adaptability of GNNs.Specifically, the structure of the AMP-GNN network is customized by unfoldingthe approximate message passing (AMP) algorithm and introducing a graph neuralnetwork (GNN) module into it. The permutation equivariance property of AMP-GNNis proved, which enables the AMP-GNN to learn more efficiently and to adapt todifferent numbers of users. We also reveal the underlying reason why GNNsimprove the AMP algorithm from the perspective of expectation propagation,which motivates us to amalgamate various GNNs with different message passingalgorithms. In the simulation, we take the massive MIMO detection to exemplifythat the proposed AMP-GNN significantly improves the performance of the AMPdetector, achieves comparable performance as the state-of-the-art DL-based MIMOdetectors, and presents strong robustness to various mismatches.

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