A Profile-Aware Resource Allocation Framework for RAN Slicing in Cell-Free Massive MIMO Networks

Abstract

As wireless networks transition toward the 6G era, supporting strictly heterogeneous services such as eMBB, URLLC, and mMTC over a unified infrastructure becomes a fundamental challenge. Traditional Radio Access Network (RAN) slicing often relies on upper-layer logical abstractions, which fail to address physical inter-slice interference and the boundary effect of cellular architectures. This paper proposes a novel Profile-Aware Hierarchical RAN Slicing Framework for User-Centric Cell-Free Massive MIMO systems to overcome these limitations. The proposed framework comprises four hierarchical stages: utility-based profile-aware clustering, dynamic inter-slice resource partitioning for power and bandwidth, hybrid central processing unit-to-access point power budgeting, and real-time power allocation using a Bipartite Graph Convolutional Network (BiGCN). By incorporating service-specific requirements into physical layer resource management, the framework ensures strict quality-of-service isolation and global energy efficiency. Simulation results demonstrate that the proposed integrated framework achieves a high Jain’s Fairness Index, exceeding 0.95 for most profiles and provides up to a 48-fold energy efficiency improvement for battery-constrained devices compared to non-slicing baselines. Furthermore, the BiGCN-based allocation module attains near-optimal performance with millisecond-level inference latency, confirming its feasibility for mission-critical real-time applications. This comprehensive approach effectively eliminates the trade-off between aggregate throughput and individual reliability, providing a scalable solution for next-generation sliced networks.