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arXiv · 2501.00009

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

Shengheng Liu·Xingkang Li·Zihuan Mao·Peng Liu·Yongming Huang
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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.

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Shengheng Liu, Xingkang Li, Zihuan Mao, Peng Liu, Yongming Huang. 2024-12-10. Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB. https://arxiv.org/abs/2501.00009

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