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

• URL: http://arxiv.org/abs/2501.00009v1
• Date: Tue, 10 Dec 2024 01:16:48 GMT
• Title: Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB
• Authors: Shengheng Liu, Xingkang Li, Zihuan Mao, Peng Liu, Yongming Huang,
• Abstract summary: The present wireless networks still rely on model-driven approaches to achieve positioning functionality. Integrating artificial intelligence into the positioning framework presents a promising solution to revolutionize the accuracy and robustness of location-based services. We propose a model-driven deep neural network (MoD-DNN) which can automatically calibrate the angular-dependent phase error.
• Score: 25.578334082503755
• License:
• 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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