Deep neural networks (NNs) are considered a powerful tool for balancing the performance and complexity of multiple-input multiple-output (MIMO) receivers due to their accurate feature extraction, high parallelism, and excellent inference ability. Graph NNs (GNNs) have recently demonstrated outstanding capability in learning enhanced message passing rules and have shown success in overcoming the drawback of inaccurate Gaussian approximation of expectation propagation (EP)-based MIMO detectors. However, the application of the GNN-enhanced EP detector to MIMO turbo receivers is underexplored and non-trivial due to the requirement of extrinsic information for iterative processing. This paper proposes a GNN-enhanced EP algorithm for MIMO turbo receivers, which realizes the turbo principle of generating extrinsic information from the MIMO detector through a specially designed training procedure. Additionally, an edge pruning strategy is designed to eliminate redundant connections in the original fully connected model of the GNN utilizing the correlation information inherently from the EP algorithm. Edge pruning reduces the computational cost dramatically and enables the network to focus more attention on the weights that are vital for performance. Simulation results and complexity analysis indicate that the proposed MIMO turbo receiver outperforms the EP turbo approaches by over 1 dB at the bit error rate of $10^{-5}$, exhibits performance equivalent to state-of-the-art receivers with 2.5 times shorter running time, and adapts to various scenarios.

翻译：深度神经网络（NNs）因其精确的特征提取、高度并行性和卓越的推理能力，被认为是平衡多输入多输出（MIMO）接收机性能与复杂度的有力工具。图神经网络（GNNs）最近在学习增强消息传递规则方面展现出突出能力，并成功克服了基于期望传播（EP）的MIMO检测器中不精确高斯近似的缺陷。然而，将GNN增强的EP检测器应用于MIMO涡轮接收机仍面临挑战，且因迭代处理对外部信息的需求而具有非平凡性。本文提出了一种用于MIMO涡轮接收机的GNN增强EP算法，通过专门设计的训练过程实现了从MIMO检测器生成外部信息的涡轮原理。此外，设计了一种边剪枝策略，利用EP算法固有的关联信息消除GNN原始全连接模型中的冗余连接。边剪枝可大幅降低计算成本，并使网络更专注于对性能至关重要的权重。仿真结果与复杂度分析表明，所提出的MIMO涡轮接收机在误码率为$10^{-5}$时相比EP涡轮方法性能提升超过1 dB，运行时间仅为当前最优接收机的2.5倍，且能适应多种场景。