Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB
Shengheng Liu, Xingkang Li, Zihuan Mao, Peng Liu, Yongming Huang
Abstract
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.
BibTeX
@article{Liu_Li_Mao_Liu_Huang_2024, title={Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/27773}, DOI={10.1609/aaai.v38i1.27773}, abstractNote={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.}, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Liu, Shengheng and Li, Xingkang and Mao, Zihuan and Liu, Peng and Huang, Yongming}, year={2024}, month={Mar.}, pages={214-221} }