World Model Aided Parameter Adjustment Decision and Evaluation System for Radio Access Network

In the rapidly evolving landscape of telecommunications, Radio Access Network (RAN) optimization is critical for maintaining high network performance and adapting to diverse service requirements. Traditionally, RAN optimization has relied heavily on manual adjustments by human experts, lacking in intelligent decision-making model. However, applying decision-making model to RAN optimization is challenged by high interaction costs and feedback delays. To address these challenges, we introduce a World Model aided Parameter Adjustment Decision and Evaluation System (WMDE), utilizing a World Model framework with offline reinforcement learning to adjust RAN parameters. WMDE, integrating the Transformer-Informed Adjustment Decision Net (TADNet) and the Causal Adjustment Effect Evaluation Net (CAENet). The WMDE system sidesteps real-time network interaction in model training with CAENet's causal estimation, cutting interaction costs. Meanwhile, TADNet employs its Transformer structure and data processing to provide a long-term, global perspective on adjustment effects, reducing feedback delay issues. Utilizing real-world operational RAN parameter adjustment data, our experiments validate the effectiveness of WMDE in decision-making for RAN parameter adjustment.