Forecasting before acting: Recent advances in learning-based traffic prediction and intelligent control in Open RAN

초록

"Open Radio Access Network (O-RAN) disaggregates the RAN into standardized units coordinated by Near-Real-Time (Near-RT) and Non-Real-Time (Non-RT) RAN Intelligent Controllers (RICs), enabling closed-loop optimization. Proactive control requires accurate prediction of traffic demand, user equipment (UE) mobility, and resource consumption before congestion or service level agreement violations occur. Yet machine learning (ML)-based proposals remain fragmented across granularities, interface placements, and evaluation environments, with no unified framework for comparing designs or assessing evidence quality. This paper surveys recent works on ML-based traffic and UE behavior prediction that explicitly feed O-RAN control loops. We classify each work along four axes: (i) ML tier, separating architectures with structural inductive bias from standard deep learning and classical methods; (ii) traffic target at UE, slice, and cell granularities; (iii) evaluation environment across live deployments, emulation, and simulation; and (iv) O-RAN interface placement across A1, E2, O1, and O2. Our analysis reveals four headline findings. First, a forecast-then-act pipeline, composing a predictor with a downstream deep reinforcement learning or optimization controller, dominates closed-loop design across granularities. Second, standard deep learning predominates, whereas architectures with structural inductive bias remain a minority. Third, evaluation is dominated by custom, unstandardized simulators, with no surveyed work performing live closed-loop actuation on an operational deployment. Fourth, open-source reproducibility is near-absent, with only one complete release of code, real data, and evaluation scripts. We then identify open challenges in per-UE forecasting, standardized data exposure, and open-source evaluation infrastructure, and outline a research roadmap toward 6G predictive intelligence in O-RAN."

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