SliceWise: Traffic-Aware Multi-Agent RL with Temporal Encoding for Joint Slice Admission and Resource Orchestration in O-RAN

Xingqi Wu, Yuhui Wang, Junaid Farooq, Hakim Ghazzai, Gianluca Setti

Abstract: Ultra-reliable low-latency communication (URLLC) services in mission-critical applications such as industrial automation, remote healthcare, and autonomous systems demand stringent quality-of-service (QoS) guarantees under dynamic, resource-constrained wireless conditions. Open radio access networks (O-RAN) offer a software-defined and programmable foundation for embedding AI-driven control. This letter proposes SliceWise, a traffic-aware multi-agent deep reinforcement learning (MARL) framework for joint admission control, user-slice association, and resource orchestration in O-RAN. The control problem is modeled as a cooperative multi-agent Markov decision process, with long short-term memory (LSTM) networks capturing temporal traffic dynamics and dueling double deep Q-networks (D3QN) ensuring stable learning. Simulation results show that SliceWise substantially reduces delay violations and packet drops while improving slice-level reliability and resource efficiency, offering a scalable orchestration solution for AI-native O-RAN deployments.

External IDs:dblp:journals/ieeenl/WuWFGS25