论文标题
MU-MIMO系统的图神经网络辅助期望传播检测器
Graph Neural Network Aided Expectation Propagation Detector for MU-MIMO Systems
论文作者
论文摘要
多源大量多输入多输出（MU-MIMO）系统可用于满足5G和超越网络的高吞吐量要求。在上行链路系统中，基站为大量用户提供服务，导致多用户干扰（MUI）。在强大的MUI存在下设计高性能检测器是一个具有挑战性的问题。这项工作根据期望传播（EP）和图神经网络的概念提出了一种新颖的检测器，称为GEPNET检测器，以解决EP中独立高斯近似值的限制。模拟结果表明，提出的GEPNET检测器在强的MUI场景中的最新MU-MIMO探测器的表现显着胜过，具有相等数量的发射和接收天线。
Multiuser massive multiple-input multiple-output (MU-MIMO) systems can be used to meet high throughput requirements of 5G and beyond networks. In an uplink MUMIMO system, a base station is serving a large number of users, leading to a strong multi-user interference (MUI). Designing a high performance detector in the presence of a strong MUI is a challenging problem. This work proposes a novel detector based on the concepts of expectation propagation (EP) and graph neural network, referred to as the GEPNet detector, addressing the limitation of the independent Gaussian approximation in EP. The simulation results show that the proposed GEPNet detector significantly outperforms the state-of-the-art MU-MIMO detectors in strong MUI scenarios with equal number of transmit and receive antennas.
