Converge: towards an efficient multi-modal sensing research infrastructure for next-generation 6 G networks

• Tipo: Artigo em Revista Científica Internacional
• Data: 2025

Autores

Teixeira, FB (Autor) Outra; Manuel Ricardo (Autor) FEUP; Coelho, A (Autor) Outra; Oliveira, HP (Autor) FCUP; Viana, P (Autor) Outra; Nuno Paulino (Autor) Outra; Fontes, H (Autor) Outra; Marques, P (Autor) Outra; Campos, R (Autor) FEUP; L. M. Pessoa (Autor) FEUP

Revista

Título: Eurasip Journal on Wireless Communications and Networking

Vol. 2026

ISSN: 1687-1472

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 0 Citações

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-01A-RQS

DOI: 10.1186/s13638-025-02544-y

Abstract (EN): Telecommunications and computer vision solutions have evolved significantly in recent years, allowing a huge advance in the functionalities and applications offered. However, these two fields have been making their way as separate areas, not exploring the potential benefits of merging the innovations brought from each of them. In challenging environments, for example, combining radio sensing and computer vision can strongly contribute to solving problems such as those introduced by obstructions or limited lighting. Machine learning algorithms, able to fuse heterogeneous and multi-modal data, are also a key element for understanding and inferring additional knowledge from raw and low-level data, able to create a new abstracting level that can significantly enhance many applications. This paper introduces the CONVERGE vision-radio concept, a new paradigm that explores the benefits of integrating two fields of knowledge towards the vision of View-to-Communicate, Communicate-to-View. The main concepts behind this vision, including supporting use cases and the proposed architecture, are presented. CONVERGE introduces a set of tools integrating wireless communications and computer vision to create a novel experimental infrastructure that will provide open datasets to the scientific community of both experimental and simulated data, enabling new research addressing various 6 G verticals, including telecommunications, automotive, manufacturing, media, and health.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 20