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

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.

Authors

• Shengheng Liu Southeast University Purple Mountain Laboratories
• Xingkang Li National Mobile Communications Research Laboratory, Southeast University, Nanjing, China
• Zihuan Mao National Mobile Communications Research Laboratory, Southeast University, Nanjing, China Purple Mountain Laboratories, Nanjing, China
• Peng Liu
• Yongming Huang Southeast University Purple Mountain Laboratories

Keywords

• APP: Internet of Things, Sensor Networks & Smart Cities
• ML: Applications