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

Authors

  • Shengheng Liu National Mobile Communications Research Laboratory, Southeast University, Nanjing, China Purple Mountain Laboratories, Nanjing, China
  • 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 Purple Mountain Laboratories, Nanjing, China
  • Yongming Huang National Mobile Communications Research Laboratory, Southeast University, Nanjing, China Purple Mountain Laboratories, Nanjing, China

DOI:

https://doi.org/10.1609/aaai.v38i1.27773

Keywords:

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

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.

Published

2024-03-25

How to Cite

Liu, S., Li, X., Mao, Z., Liu, P., & Huang, Y. (2024). Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB. Proceedings of the AAAI Conference on Artificial Intelligence, 38(1), 214-221. https://doi.org/10.1609/aaai.v38i1.27773

Issue

Section

AAAI Technical Track on Application Domains