This paper considers a data detection problem in multiple-input multiple-output (MIMO) communication systems with hardware impairments. To address challenges posed by nonlinear and unknown distortion in received signals, two learning-based detection methods, referred to as model-driven and data-driven, are presented. The model-driven method employs a generalized Gaussian distortion model to approximate the conditional distribution of the distorted received signal. By using the outputs of coarse data detection as noisy training data, the model-driven method avoids the need for additional training overhead beyond traditional pilot overhead for channel estimation. An expectation-maximization algorithm is devised to accurately learn the parameters of the distortion model from noisy training data. To resolve a model mismatch problem in the model-driven method, the data-driven method employs a deep neural network (DNN) for approximating a-posteriori probabilities for each received signal. This method uses the outputs of the model-driven method as noisy labels and therefore does not require extra training overhead. To avoid the overfitting problem caused by noisy labels, a robust DNN training algorithm is devised, which involves a warm-up period, sample selection, and loss correction. Simulation results demonstrate that the two proposed methods outperform existing solutions with the same overhead under various hardware impairment scenarios.

翻译：本文研究了多输入多输出（MIMO）通信系统中存在硬件损伤时的数据检测问题。为应对接收信号中非线性与未知畸变所带来的挑战，提出了两种基于学习的检测方法，即模型驱动法和数据驱动法。模型驱动法采用广义高斯畸变模型来近似畸变接收信号的条件分布。通过将粗检测输出的结果作为含噪训练数据，该方法无需在传统导频开销用于信道估计之外增加额外训练开销。本文设计了一种期望最大化算法，从含噪训练数据中精确学习畸变模型参数。为解决模型驱动法中的模型失配问题，数据驱动法采用深度神经网络（DNN）来近似每个接收信号的后验概率。该方法以模型驱动法的输出作为含噪标签，因此无需额外训练开销。为避免含噪标签导致的过拟合问题，设计了一种鲁棒的DNN训练算法，包含预热阶段、样本选择与损失修正。仿真结果表明，在多种硬件损伤场景下，所提两种方法在相同开销条件下均优于现有解决方案。