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 solu tion 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) esti mation 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 convo lutional neural network and a sparse conjugate gradient algo rithm. Simulation and experimental results are presented to demonstrate the effectiveness of the proposed method in en hancing spectrum calibration and AoA estimation