A Deep Learning Approach in RIS-based Indoor Localization

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

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

Ata de Conferência Internacional

Título: 2024 JOINT EUROPEAN CONFERENCE ON NETWORKS AND COMMUNICATIONS & 6G SUMMIT, EUCNC/6G SUMMIT 2024

Páginas: 523-528

Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit)

Antwerp, BELGIUM, JUN 03-06, 2024

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-012-NA4

DOI: 10.1109/eucnc/6gsummit60053.2024.10597098

Abstract (EN): In the domain of RIS-based indoor localization, our work introduces two distinct approaches to address real-world challenges. The first method is based on deep learning, employing a Long Short-Term Memory (LSTM) network. The second, a novel LSTM-PSO hybrid, strategically takes advantage of deep learning and optimization techniques. Our simulations encompass practical scenarios, including variations in RIS placement and the intricate dynamics of multipath effects, all in Non-Line-of-Sight conditions. Our methods can achieve very high reliability, obtaining centimeter-level accuracy for the 98th percentile (worst case) in a different set of conditions, including the presence of the multipath effect. Furthermore, our hybrid approach showcases remarkable resolution, achieving submillimeter-level accuracy in numerous scenarios.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 6