Behavior-Driven Slice-Aware Traffic and KPI Prediction for 6G Radio Access Networks

Abstract

Accurate prediction of traffic demand and slice-level key performance indicators (KPIs) is essential for enabling proactive resource management in next-generation radio access networks. However, most existing studies focus on aggregate traffic modeling and provide limited insight into slice-level dynamics under varying mobility and traffic conditions. This paper proposes a slice-aware deep learning framework for the joint prediction of traffic demand and multiple KPIs within a simulation-driven environment. An ns-3-generated multivariate time-series dataset is constructed to capture traffic demand, throughput, goodput, latency, and packet-level statistics across eMBB, URLLC, and mMTC slices under heterogeneous mobility patterns, stochastic UE populations, varying traffic loads, and adaptive radio configurations. This enables a controllable and reproducible evaluation of slice-level traffic and KPI dynamics under diverse service conditions. LSTM, GRU, CNN, and a traditional linear regression baseline are comparatively evaluated under a unified preprocessing and time-series validation framework. Experimental results demonstrate that CNN consistently achieves lower prediction errors across most KPIs, while recurrent models provide competitive performance for smoother traffic patterns. The results further show that deep learning models more effectively capture nonlinear slice-level dynamics compared with traditional forecasting approaches. Latency prediction remains the most challenging task due to its sensitivity to rapid traffic fluctuations and varying network conditions. Beyond prediction, the proposed framework provides a foundation for learning slice-level network dynamics and supporting future digital twin–oriented network representations and closed-loop optimization in 6G-ready wireless systems.