Hierarchical Multi-Objective Learning for Context-Aware 5G Ran Slice Resource Allocation

Abstract

Efficient coexistence of eMBB and URLLC services remains a critical challenge in AI-native Radio Access Networks (RANs). This paper proposes a two-timescale Hierarchical Reward Weighting (HRW) framework based on multiobjective reinforcement learning for context-aware O-RAN slicing under a Constrained Markov Decision Process (CMDP) formulation. The proposed architecture separates long-term policy adaptation from fast-timescale radio scheduling, mitigating the non-stationarity inherent in multiobjective RAN optimization. At the slow layer, a non-realtime RIC rApp exploits a long-term network context and a differentiable Softmax mapping to adapt slice reward preferences. These policies are propagated through the $O$ -RAN control hierarchy to guide downstream scheduling decisions. At the fast layer, decentralized scheduling agents embedded within the Open Distributed Unit (O-DU) MAC layer execute sub-millisecond Physical Resource Block (PRB) allocation and packet preemption, avoiding near-RT RIC transport latency constraints. Evaluated under a multiuser MIMO-OFDMA environment, the proposed framework improves resource utilization by up to 60.8% over static partitioning while maintaining bounded URLLC tail-latency behavior and strict Service Level Agreement (SLA) compliance. The results demonstrate the feasibility of AI-native hierarchical O-RAN control and align with the ITU-T visions for autonomous 6G RAN intelligence.