Beyond Forecasting: World Models for Coupled Radio Network Control

Model-based RAN control requires predicting how future resource allocations reshape network dynamics, including effects that persist after an intervention has passed. We propose TRACTOR+, an action-conditioned world model that separates recurrent network-state evolution from persistent action-response dynamics. TRACTOR+ combines multi-scale recurrent states, an instantaneous action-conditioned predictor, and adaptive fast/medium/slow response memories for free-running multi-horizon rollout. Experiments on three-slice RAN traces show that TRACTOR+ achieves the lowest standardized RMSE across all evaluated natural and allocation-event horizons with only 0.193M parameters. Removing future-action inputs degrades event prediction, while mechanism ablations show that persistent-response memory strengthens action-path discrimination. The learned dynamics also support short-horizon offline planning within the evaluated Near-RT budget. All alternative-action results are interpreted as predictive diagnostics under observational support rather than causal or closed-loop effects.