Preprint Open Access

Network intelligence in 6G: challenges and opportunities

Banchs, Albert; Fiore, Marco; Garcia-Saavedra, Andres; Gramaglia, Marco

The success of the upcoming 6G 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, as they have proven extremely successful at solving hard problems that require inferring complex relationships from entangled, massive (network traffic) data. However, the common approach of plugging ‘vanilla’ AI models into controllers and orchestrators does not fulfil the potential of the technology. Instead, AI models should be tailored to the specific network level and respond to the specific needs of network functions, eventually coordinated by an end-to-end NI-native architecture for 6G. In this paper, we discuss these challenges and provide results for a candidate NI-driven functionality that is properly integrated into the proposed architecture: network capacity forecasting.

The authors of this paper have received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101017109 (DAEMON Network intelligence for aDAptive and sElf-Learning MObile Networks). This paper is also funded by the Spanish State Research Agency (TRUE5G project, PID2019-108713RB-C52PID2019-108713RB-C52 / AEI / 10.13039/501100011033)
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