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dc.contributor.authorCollet, Alan 
dc.contributor.authorBazco-Nogueras, Antonio 
dc.contributor.authorBanchs, Albert 
dc.contributor.authorFiore, Marco 
dc.date.accessioned2024-10-09T09:08:42Z
dc.date.available2024-10-09T09:08:42Z
dc.date.issued2024
dc.identifier.issn1558-1896es
dc.identifier.urihttps://hdl.handle.net/20.500.12761/1864
dc.description.abstractThe evolution of communication networks towards self-configuring systems requires the development of anticipatory approaches for network management to realize the envisioned concept of a zero-touch network orchestration. Current anticipatory network intelligence solutions rely on a well-defined loss function, which means that they require perfect knowledge of the relationship between the proactive management decisions and the consequent system performance. However, in anticipatory networking, there exist many tasks where characterizing such a relationship in advance is not possible. In such tasks, it is possible to measure the resulting performance of a management decision a posteriori, but we cannot know a priori the resulting performance of a certain management decision. A simple example of such tasks could be the maximization of the monetary profit when allocating resources to end users: when taking a certain resource allocation decision, it is possible to measure the profit afterwards, but it would be extremely difficult to determine a priori the resulting monetary profit. To close this gap, we present a novel two-fold learning approach, which is able to jointly learn the relationship between the prediction and the target management objective at the same time as it apprehends to anticipate the corresponding task. This method lays the foundations to the automated adaptation of network intelligence to specific complex objectives in zero-touch network management. We apply this method to different use cases of interest including monetary profit maximization.es
dc.description.sponsorshipComunidad de Madrid - Atracción de talentoes
dc.description.sponsorshipEuropean Uniones
dc.language.isoenges
dc.publisherIEEEes
dc.titleLearning to Learn How to Manage Network Resources with Loss Function Meta-Learninges
dc.typemagazinees
dc.journal.titleIEEE Communications Magazinees
dc.type.hasVersionAMes
dc.rights.accessRightsopen accesses
dc.relation.projectIDinfo:eu-repo/grantAgreement/EU/101139270es
dc.relation.projectNameORIGAMI (Optimized Resource Integration and Global Architecture for Mobile Infrastructure for 6G)es
dc.subject.keywordzero-touch networkinges
dc.subject.keywordloss learninges
dc.subject.keywordanticipatory network managementes
dc.description.refereedTRUEes
dc.description.statusinpresses


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