Usage of network simulators in machine-learning-assisted 5G/6G networks

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• dc.contributor.author Wilhelmi Roca, Francesc
• dc.contributor.author Carrascosa Zamacois, Marc
• dc.contributor.author Cano, Cristina
• dc.contributor.author Jonsson, Anders, 1973-
• dc.contributor.author Ram, Vishnu
• dc.contributor.author Bellalta, Boris
• dc.date.accessioned 2023-03-09T07:24:56Z
• dc.date.available 2023-03-09T07:24:56Z
• dc.date.issued 2021
• dc.description.abstract Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding trustworthiness and reliability. In this article, we devise the role of network simulators for bridging the gap between ML and communications systems. In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. Moreover, we provide insights into the main challenges resulting from this integration, and then give hints discussing how they can be overcome. Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential WiFi network.
• dc.description.sponsorship This work has been partially supported by grants MDM-2015-0502, WINDMAL PGC2018-099959- B-I00 (MCIU/AEI/FEDER,UE), 2017-SGR-11888, and by SPOTS project (RTI2018-095438-A-I00) funded by the Spanish Ministry of Science, Innovation and Universities.
• dc.format.mimetype application/pdf
• dc.identifier.citation Wilhelmi F, Carrascosa M, Cano C, Jonsson A, Ram V, Bellalta B. Usage of network simulators in machine-learning-assisted 5G/6G networks. IEEE Wirel Commun. 2021;28(1):160-6. DOI: 10.1109/MWC.001.2000206
• dc.identifier.doi http://dx.doi.org/10.1109/MWC.001.2000206
• dc.identifier.issn 1536-1284
• dc.identifier.uri http://hdl.handle.net/10230/56118
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof IEEE Wireless Communications. 2021;28(1):160-6.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/MDM-2015-0502
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PGC2018-099959-B-I00
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/RTI2018-095438-A-I00
• dc.rights © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/MWC.001.2000206
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Training data
• dc.subject.keyword Communication systems
• dc.subject.keyword Machine learning
• dc.subject.keyword Stakeholders
• dc.subject.keyword Reliability
• dc.subject.keyword Wireless fidelity
• dc.subject.keyword 5G mobile ommunication
• dc.subject.keyword 6G mobile communication
• dc.title Usage of network simulators in machine-learning-assisted 5G/6G networks
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/acceptedVersion