AI-RAN resource configuration for non-collaborative cross-domain slicing

In the evolution from 5G to 6G, network slicing faces challenges due to the coexistence of multiple operators and service providers, where privacy constraints on configuration data and parameters restrict end-to-end slice deployment. This issue is more pronounced in the 6G AI-RAN architecture, especially when cross-domain coordination between the Radio Access Network (RAN) and Core Network (CN) is insufficient, leading to inefficient slice orchestration and management. To address these challenges, we propose three solutions: (1) A non-collaborative cross-domain slicing orchestration architecture for 6G AI-RAN, enabling an adaptive orchestration framework driven by slice metric prediction; (2) a cross-domain Key Performance Indicator (KPI) prediction mechanism combining Graph Convolutional and Attention Networks (GCN-GAT) with a Transformer model to analyze the impact of cross-domain features on RAN slice performance; and (3) an intelligent RAN slice resource orchestration strategy driven by metric prediction. Experiments on real multi-domain datasets demonstrate that the proposed framework outperforms baseline methods by improving end-to-end slice service quality, while significantly enhancing system throughput and reducing both resource overhead and average load.