Enhancing NLoS RIS-Aided Localization with Optimization and Machine Learning

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2023

Autores

Aguiar, RA (Autor) Outra; Nuno Paulino (Autor) FEUP; L. M. Pessoa (Autor) FEUP

Ata de Conferência Internacional

Título: IEEE Globecom Workshops 2023, Kuala Lumpur, Malaysia, December 4-8, 2023

Páginas: 1898-1903

2023 IEEE Globecom Workshops, GC Wkshps 2023

Kuala Lumpur, 4 December 2023 through 8 December 2023

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-010-7NS

DOI: 10.1109/gcwkshps58843.2023.10464631

Abstract (EN): 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 10m where two of the walls are equipped with RIS tiles. © 2023 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 5