Enhancing NLoS RIS-Aided Localization with Optimization and Machine Learning

TL;DR

This paper presents two machine learning optimization algorithms that significantly improve indoor NLoS RIS-aided localization accuracy, achieving sub-centimeter precision in simulated environments.

Contribution

Introduction of two novel machine learning optimization algorithms specifically designed for enhancing RIS-aided localization accuracy in NLoS indoor scenarios.

Findings

Localization errors under 30 cm in 90% of cases

Achieved sub-millimeter accuracy in 85% of cases

Genetic Algorithm and Particle Swarm Optimization combination outperforms other methods

Abstract

This paper introduces two machine learning optimization algorithms to significantly enhance position estimation in Reconfigurable Intelligent Surface (RIS) aided localization for mobile user equipment in Non-Line-of-Sight conditions. Leveraging the strengths of these algorithms, we present two methods capable of achieving extremely high accuracy, reaching sub-centimeter or even sub-millimeter levels at 3.5 GHz. The simulation results highlight the potential of these approaches, showing significant improvements in indoor mobile localization. The demonstrated precision and reliability of the proposed methods offer new opportunities for practical applications in real-world scenarios, particularly in Non-Line-of-Sight indoor localization. By evaluating four optimization techniques, we determine that a combination of a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) results in localization errors under 30 cm in 90 % of the cases, and under 5 mm for close to 85 % of cases when considering a simulated room of 10 m by 10 m where two of the walls are equipped with RIS tiles.