TY - JOUR
AU - Huang, Yongming
AB - 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.
TI - Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB
JF - Computing Research Repository
DO - 10.48550/arxiv.2501.00009
DA - 2024-12-10
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/model-driven-deep-neural-network-for-enhanced-aoa-estimation-using-5g-wDLbS8iLVs
VL - 2024
IS - 2501
DP - DeepDyve
ER -