practical AI for Time-vAryiNG network traffic fOrecasting in 6G

To fully exploit the 6G potential and ensure near-zero latency, infinite capacity, and 100% reliability and availability communications, Mobile Network Operators (MNOs) are expected to deploy Zero-touch Network and Service Management (ZSM) solutions that completely automate the resource orchestration and work at a very fast timescale, and Artificial Intelligence (AI) is regarded as the primary enabler for proactive decision-making algorithms that will underpin ZSM.

However, the robustness and trustworthiness of AI predictors are critical aspects and represent one of the major barriers presently withholding MNOs from trusting ZSM technologies.

In fact, all existing studies on mobile traffic forecasting work under assumption of stationary network, but, in the case of mobile network Key Performance Indicators (KPIs) prediction, user demands and network configurations are time-varying (non-stationary) in operational mobile networks. For example, update of antenna configurations or shifts in popularity of mobile applications can happen over time.

Thus, this project is termed ""practical AI for Time-vAryiNG network traffic fOrecasting in 6G"" (6G-AI-TANGO) and aims to: (i) assess and quantify the severity of temporal changes (due to non-stationarity) in 6G network KPI forecasting, (ii) design AI models tailored to mobile KPI forecasting that are resilient to the long-timescale temporal variations regularly encountered in real-world user demands and network configurations, and (iii) verify the robustness of proposed predictors on real-word data and in production-grade 6G mobile network during non-academic placement at Telefónica.