标题： 基于模型驱动的深度神经网络用于5G gNB增强到达角估计 显示英文标题

标题： Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB

摘要： 高精度定位已成为智能互联设备的一项基础使能技术。然而，现有的无线网络仍然依赖于模型驱动的方法来实现定位功能，这些方法在实际场景中容易受到性能下降的影响，主要原因是硬件损伤。将人工智能集成到定位框架中为革新基于位置的服务的准确性和鲁棒性提供了一个有前景的解决方案。在这项研究中，我们通过将到达角（AoA）估计问题重新表述为空间谱图像重建来解决这一挑战。为此，我们设计了一种模型驱动的深度神经网络（MoD-DNN），它可以自动校准与角度相关的相位误差。所提出的MoD-DNN方法在卷积神经网络和稀疏共轭梯度算法之间采用迭代优化方案。模拟和实验结果展示了所提出方法在增强频谱校准和AoA估计方面的有效性。 显示英文摘要

摘要： High-accuracy positioning has become a fundamental enabler for intelligent connected devices. Nevertheless, the present wireless networks still rely on model-driven approaches to achieve positioning functionality, which are susceptible to performance degradation in practical scenarios, primarily due to hardware impairments. Integrating artificial intelligence into the positioning framework presents a promising solution to revolutionize the accuracy and robustness of location-based services. In this study, we address this challenge by reformulating the problem of angle-of-arrival (AoA) estimation into image reconstruction of spatial spectrum. To this end, we design a model-driven deep neural network (MoD-DNN), which can automatically calibrate the angular-dependent phase error. The proposed MoD-DNN approach employs an iterative optimization scheme between a convolutional neural network and a sparse conjugate gradient algorithm. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in enhancing spectrum calibration and AoA estimation.