Diffusion Model and Digital Twin Enhanced Deep Reinforcement Learning for Radio Resource Management in RAN Slicing

Network slicing is a key enabler for 6G mobile networks. Guaranteeing the service level agreement with the smallest amount of radio resources is a challenging problem in network slicing scenarios due to random traffic patterns and the channel environment. To this end, we propose a novel deep reinforcement learning algorithm named CGDSAC based on the conditional generative diffusion model to achieve the optimization objective while capturing the underlying environment distribution. Subsequently, we further design a digital twin (DT) enhanced version of CGDSAC named CGDSAC-DT, to address issues that CGDSAC is unsafe or has lower performance than the default strategy in the early training stages, and converges slowly. Numerical results show that our proposed method can solve the issues encountered and outperform the baseline algorithm regarding performance metrics.