用深度学习校准5G波达方向误差，提升定位精度
Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB
- 将波达方向估计转为频谱图像重建，结合神经网络与优化算法
- 在模拟和实验中显著改善频谱校准效果，提升角度估计准确率
- 适合5G基站定位、智能物联网等需要高精度测向的场景
高精度定位已成为智能互联设备的基础能力。然而，现有无线网络仍依赖模型驱动方法实现定位功能，易受硬件损伤影响，在实际场景中性能下降。将人工智能融入定位框架，有望彻底提升位置服务的精度与鲁棒性。本文将波达方向（AoA）估计重构为空间谱图像重建问题，设计了一种模型驱动的深度神经网络（MoD-DNN），可自动校准角度相关的相位误差。所提方法通过卷积神经网络与稀疏共轭梯度算法之间的迭代优化实现。仿真与实验结果表明，该方法能有效提升频谱校准与AoA估计性能。
原文摘要 · Abstract (English)
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.
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