Transformer-Driven Dynamic Resource Reconfiguration in Cellular Networks

This work introduces the integration of PatchTST, a transformer-based time-series forecasting model, in the near-real-time RAN Intelligent Controller for predictive base station reconfiguration in a 5G/6G network. Additionally, an in-domain transfer learning approach is proposed, achieving 16.87% improvement in forecasting performance by leveraging only 0.15% of the dataset, while identifying a saturation point in model performance relative to the number of base stations used for transferring knowledge. Results demonstrate that PatchTST outperforms traditional machine learning baselines by 73.44%, providing highly accurate predictions. This enables effective dynamic reconfiguration of base stations and enhancing network efficiency in next-generation cellular systems.