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

TL;DR

This paper introduces a model-driven deep neural network that improves angle-of-arrival estimation accuracy in 5G networks by calibrating spectrum errors, combining AI with traditional model-based methods.

Contribution

The study presents a novel MoD-DNN that reformulates AoA estimation as image reconstruction, integrating neural networks with iterative optimization for better calibration.

Findings

Enhanced spectrum calibration accuracy demonstrated in simulations.

Improved AoA estimation robustness shown in experimental results.

Effective integration of AI with model-based approaches for positioning.

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.