Conditional Diffusion Model With OOD Mitigation as High-Dimensional Offline Resource Allocation Planner in Clustered Ad Hoc Networks

In modern clustered ad hoc networks, efficient and dynamic resource allocation is crucial for ensuring Quality of Service (QoS) under dynamic and uncertain environments. However, the challenges posed by limited sample efficiency, high interaction cost, and high-dimensional action space limit the effectiveness of the widely adopted Model-Free Reinforcement Learning (MFRL) solutions. In contrast, Model-Based RL (MBRL) offers an alternative approach to boost sample efficiency and stabilize the training by explicitly leveraging a learned environment model. Nevertheless, designing accurate and stable dynamics models in noisy, real-world communication scenarios remains a key bottleneck. To address these issues, we propose a Conditional Diffusion Model Planner (CDMP) for high-dimensional offline resource allocation in clustered ad hoc networks. By leveraging the powerful generative capability of Diffusion Models (DMs), our approach enables the accurate modeling of complex environmental dynamics and utilizes an inverse dynamics model for effective policy planning. Beyond simply adopting DMs in offline RL, we further incorporate the CDMP algorithm with a theoretically guaranteed, uncertainty-aware penalty metric, which theoretically and empirically manifests itself in mitigating the Out-of-Distribution (OOD)-induced distributional shift, a common issue for offline settings with scarce training data. Extensive experiments also show that our model outperforms MFRL in average reward and QoS, while demonstrating superior performance over other MBRL algorithms. These results highlight the practicality and scalability of our model in real-world network resource allocation tasks.