AI-Assisted NLOS Sensing for RIS-Based Indoor Localization in Smart Factories

TL;DR

This paper presents an AI-assisted framework using a CNN to accurately classify LOS/NLOS conditions in RIS-based indoor localization, significantly improving reliability in smart factory environments.

Contribution

The study introduces a customized CNN model that outperforms standard models in NLOS detection for RIS-based localization, addressing key challenges in smart factory automation.

Findings

cCNN achieves 95-99% accuracy in NLOS classification

Outperforms VGG-16 with 85.5-88% accuracy

Validated across three different environments

Abstract

In the era of Industry 4.0, precise indoor localization is vital for automation and efficiency in smart factories. Reconfigurable Intelligent Surfaces (RIS) are emerging as key enablers in 6G networks for joint sensing and communication. However, RIS faces significant challenges in Non-Line-of-Sight (NLOS) and multipath propagation, particularly in localization scenarios, where detecting NLOS conditions is crucial for ensuring not only reliable results and increased connectivity but also the safety of smart factory personnel. This study introduces an AI-assisted framework employing a Convolutional Neural Network (CNN) customized for accurate Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) classification to enhance RIS-based localization using measured, synthetic, mixed-measured, and mixed-synthetic experimental data, that is, original, augmented, slightly noisy, and highly noisy data, respectively. Validated through such data from three different environments, the proposed customized-CNN (cCNN) model achieves {95.0\%-99.0\%} accuracy, outperforming standard pre-trained models like Visual Geometry Group 16 (VGG-16) with an accuracy of {85.5\%-88.0\%}. By addressing RIS limitations in NLOS scenarios, this framework offers scalable and high-precision localization solutions for 6G-enabled smart factories.