To overcome the influence of channel estimation error on signal detection, this paper presents a model-driven deep learning method for joint channel estimation and signal detection in multiple-input multiple-output (MIMO) wireless communication systems. To improve the robustness of the method, we use the Student's t -distribution to model the environment noise, and model the joint problem probabilistically by taking the channel state information (CSI) as a latent variable, then derive a generalized expectation maximization (GEM) algorithm to fit the model. In GEM, the expectation step is used for robust CSI estimation while the maximization step detects the transmitted signal with the estimated CSI. To reduce the computational complexity of GEM, we unfold it into a deep neural network. The network contains only a few trainable parameters, which is easy to train and has low space complexity. Based on our experimental results, we find that the proposed GEM algorithm outperforms a variety of signal detection algorithms which take the CSI estimation and signal detection as independent modules. Further, we find empirically that the proposed GEM network outperforms state-of-the-art deep learning based joint channel estimation and signal detection methods and has a good generalization ability against the number of antennas, the modulation mode, the level of signal-to-noise ratio and channel correlation.

中文翻译：

---

联合 MIMO 信道估计和信号检测的深度期望最大化,联合 MIMO 信道估计和信号检测的深度期望最大化

为了克服信道估计误差对信号检测的影响，本文提出了一种模型驱动的深度学习方法，用于多输入多输出（MIMO）无线通信系统中的联合信道估计和信号检测。为了提高方法的鲁棒性，我们使用 Student'st -distribution to model the environment noise, and model the joint problem probabilistically by taking the channel state information (CSI) as a latent variable, then derive a generalized expectation maximization (GEM) algorithm to fit the model. In GEM, the expectation step is used for robust CSI estimation while the maximization step detects the transmitted signal with the estimated CSI. To reduce the computational complexity of GEM, we unfold it into a deep neural network. The network contains only a few trainable parameters, which is easy to train and has low space complexity. Based on our experimental results, we find that the proposed GEM algorithm outperforms a variety of signal detection algorithms which take the CSI estimation and signal detection as independent modules. Further, we find empirically that the proposed GEM network outperforms state-of-the-art deep learning based joint channel estimation and signal detection methods and has a good generalization ability against the number of antennas, the modulation mode, the level of signal-to-noise ratio and channel correlation.,为了克服信道估计误差对信号检测的影响，本文提出了一种模型驱动的深度学习方法，用于多输入多输出（MIMO）无线通信系统中的联合信道估计和信号检测。为了提高方法的鲁棒性，我们使用 Student'st分布对环境噪声进行建模，以信道状态信息（CSI）为潜在变量对联合问题进行概率建模，然后推导出广义期望最大化（GEM）算法来拟合模型。在 GEM 中，期望步骤用于稳健的 CSI 估计，而最大化步骤使用估计的 CSI 检测传输信号。为了降低 GEM 的计算复杂度，我们将其展开为深度神经网络。该网络仅包含少量可训练参数，易于训练且空间复杂度低。根据我们的实验结果，我们发现所提出的 GEM 算法优于各种将 CSI 估计和信号检测作为独立模块的信号检测算法。更远，