Toward Native Explainable and Robust AI in 6G Networks: Current State, Challenges and Road Ahead

6G networks are expected to face the daunting task of providing support to a set of extremely diverse services, each more demanding than those of previous generation networks (e.g., holographic communications, unmanned mobility, etc.), while at the same time integrating non-terrestrial networks, inc...

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Detalles Bibliográficos
Autores: Fiandrino, Claudio, Attanasio, Giulia|||0000-0002-5489-9854, Fiore, Marco, Widmer, Joerg
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:IMDEA Networks Institute
Repositorio:IMDEA Networks Institute Digital Repository
Idioma:inglés
OAI Identifier:oai:dspace.networks.imdea.org:20.500.12761/1600
Acceso en línea:http://hdl.handle.net/20.500.12761/1600
https://dx.doi.org/https://doi.org/10.1016/j.comcom.2022.06.036
Access Level:acceso abierto
Palabra clave:6G networks
AI
Explainable AI
Robust AI
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spelling Toward Native Explainable and Robust AI in 6G Networks: Current State, Challenges and Road AheadFiandrino, ClaudioAttanasio, Giulia|||0000-0002-5489-9854Fiore, MarcoWidmer, Joerg6G networksAIExplainable AIRobust AI6G networks are expected to face the daunting task of providing support to a set of extremely diverse services, each more demanding than those of previous generation networks (e.g., holographic communications, unmanned mobility, etc.), while at the same time integrating non-terrestrial networks, incorporating new technologies, and supporting joint communication and sensing. The resulting network architecture, component interactions, and system dynamics are unprecedentedly complex, making human-only operation impossible, and thus calling for AI-based automation and configuration support. For this to happen, AI solutions need to be robust and interpretable, i.e., network engineers should trust the way AI operates and understand the logic behind its decisions. In this paper, we revise the current state of tools and methods that can make AI robust and explainable, shed light on challenges and open problems, and indicate potential future research directions.European UnionTRUEpubElsevier20222022-06-01journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articlehttp://hdl.handle.net/20.500.12761/1600https://dx.doi.org/https://doi.org/10.1016/j.comcom.2022.06.036reponame:IMDEA Networks Institute Digital Repositoryinstname:IMDEA Networks InstituteInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:dspace.networks.imdea.org:20.500.12761/16002026-06-06T12:35:51Z
dc.title.none.fl_str_mv Toward Native Explainable and Robust AI in 6G Networks: Current State, Challenges and Road Ahead
title Toward Native Explainable and Robust AI in 6G Networks: Current State, Challenges and Road Ahead
spellingShingle Toward Native Explainable and Robust AI in 6G Networks: Current State, Challenges and Road Ahead
Fiandrino, Claudio
6G networks
AI
Explainable AI
Robust AI
title_short Toward Native Explainable and Robust AI in 6G Networks: Current State, Challenges and Road Ahead
title_full Toward Native Explainable and Robust AI in 6G Networks: Current State, Challenges and Road Ahead
title_fullStr Toward Native Explainable and Robust AI in 6G Networks: Current State, Challenges and Road Ahead
title_full_unstemmed Toward Native Explainable and Robust AI in 6G Networks: Current State, Challenges and Road Ahead
title_sort Toward Native Explainable and Robust AI in 6G Networks: Current State, Challenges and Road Ahead
dc.creator.none.fl_str_mv Fiandrino, Claudio
Attanasio, Giulia|||0000-0002-5489-9854
Fiore, Marco
Widmer, Joerg
author Fiandrino, Claudio
author_facet Fiandrino, Claudio
Attanasio, Giulia|||0000-0002-5489-9854
Fiore, Marco
Widmer, Joerg
author_role author
author2 Attanasio, Giulia|||0000-0002-5489-9854
Fiore, Marco
Widmer, Joerg
author2_role author
author
author
dc.subject.none.fl_str_mv 6G networks
AI
Explainable AI
Robust AI
topic 6G networks
AI
Explainable AI
Robust AI
description 6G networks are expected to face the daunting task of providing support to a set of extremely diverse services, each more demanding than those of previous generation networks (e.g., holographic communications, unmanned mobility, etc.), while at the same time integrating non-terrestrial networks, incorporating new technologies, and supporting joint communication and sensing. The resulting network architecture, component interactions, and system dynamics are unprecedentedly complex, making human-only operation impossible, and thus calling for AI-based automation and configuration support. For this to happen, AI solutions need to be robust and interpretable, i.e., network engineers should trust the way AI operates and understand the logic behind its decisions. In this paper, we revise the current state of tools and methods that can make AI robust and explainable, shed light on challenges and open problems, and indicate potential future research directions.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-06-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12761/1600
https://dx.doi.org/https://doi.org/10.1016/j.comcom.2022.06.036
url http://hdl.handle.net/20.500.12761/1600
https://dx.doi.org/https://doi.org/10.1016/j.comcom.2022.06.036
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:IMDEA Networks Institute Digital Repository
instname:IMDEA Networks Institute
instname_str IMDEA Networks Institute
reponame_str IMDEA Networks Institute Digital Repository
collection IMDEA Networks Institute Digital Repository
repository.name.fl_str_mv
repository.mail.fl_str_mv
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