arXiv:2611.08274v1 Announce Type: new Abstract: We solve long-horizon wireless link scheduling with per-link minimum transmission constraints. Fair distribution of resources across links and time steps requires stochastic scheduling, motivating a diffusion framework. Via imitation learning, we train a diffusion model to sample scheduling decisions, conditioning generation on the network topology and constraint levels. The parameterization is achieved through a U-Graph Neural Network (U-GNN) to leverage the relationship between graphs and wireless networks for learning. Our approach achieves near-optimal performance, maximizing sum rate while distributing allocations across different links over time horizons. The U-GNN-based diffusion model generalizes to requirements and network sizes unseen during training, demonstrating the robustness of the approach.