Network intelligence for aDAptive and sElf Learning MObile Networks
The success of Beyond 5G (B5G) systems will largely depend on the quality of the Network Intelligence (NI) that will fully automate network management. Artificial Intelligence (AI) models are commonly regarded as the cornerstone f...
The success of Beyond 5G (B5G) systems will largely depend on the quality of the Network Intelligence (NI) that will fully automate network management. Artificial Intelligence (AI) models are commonly regarded as the cornerstone for NI design; indeed, AI models have proven extremely successful at solving hard problems that require inferring complex relationships from entangled and massive (e.g., traffic) data. However, AI is not the best solution for every NI task; and, when it is, the dominating trend of plugging ‘vanilla’ AI into network controllers and orchestrators is not a sensible choice.
Departing from the current hype around AI, DAEMON will set forth a pragmatic approach to NI design. The project will carry out a systematic analysis of which NI tasks are appropriately solved with AI models, providing a solid set of guidelines for the use of machine learning in network functions. For those problems where AI is a suitable tool, DAEMON will design tailored AI models that respond to the specific needs of network functions, taking advantage of the most recent advances in machine learning. Building on these models, DAEMON will design an end-to-end NI-native architecture for B5G that fully coordinates NI-assisted functionalities.
The advances to NI devised by DAEMON will be applied in practical network settings to: (i) deliver extremely high performance while making an efficient use of the underlying radio and computational resources; (ii) reduce the energy footprint of mobile networks; and (iii) provide extremely high reliability beyond that of 5G systems. To achieve this, DAEMON will design practical algorithms for eight concrete NI-assisted functionalities, carefully selected to achieve the objectives above. The performance of the DAEMON algorithms will be evaluated in real-world conditions via four experimental sites, and at scale with data-driven approaches based on two nationwide traffic measurement datasets, against nine ambitious yet feasible KPI targets.ver más
Seleccionando "Aceptar todas las cookies" acepta el uso de cookies para ayudarnos a brindarle una mejor experiencia de usuario y para analizar el uso del sitio web. Al hacer clic en "Ajustar tus preferencias" puede elegir qué cookies permitir. Solo las cookies esenciales son necesarias para el correcto funcionamiento de nuestro sitio web y no se pueden rechazar.
Cookie settings
Nuestro sitio web almacena cuatro tipos de cookies. En cualquier momento puede elegir qué cookies acepta y cuáles rechaza. Puede obtener más información sobre qué son las cookies y qué tipos de cookies almacenamos en nuestra Política de cookies.
Son necesarias por razones técnicas. Sin ellas, este sitio web podría no funcionar correctamente.
Son necesarias para una funcionalidad específica en el sitio web. Sin ellos, algunas características pueden estar deshabilitadas.
Nos permite analizar el uso del sitio web y mejorar la experiencia del visitante.
Nos permite personalizar su experiencia y enviarle contenido y ofertas relevantes, en este sitio web y en otros sitios web.