Federated Reinforcement Learning-Based Resource Allocation in O-RAN Slicing for Metaverse

Ensuring high Quality of Experience (QoE) is crucial for seamless wireless access in immersive metaverse environments, yet this goal faces challenges due to constrained communication and computing resources. Coordinating x-applications (xAPPs) in open radio access network (O-RAN) effectively is essential for optimizing these limited resources to meet high QoE demands, thus enhancing the user experience. Federated reinforcement learning (FRL) further supports this by facilitating collaboration among distributed agents, boosting training efficiency. This paper proposes a QoE utility model based on xAPPs design for the metaverse, which incorporates energy efficiency, video quality, and communication and computation delays to optimize power consumption and bandwidth while minimizing delay. Tailored for FRL-based resource allocation within a sliced O-RAN architecture for the metaverse, the QoE utility model manages key network functions-such as power control and physical resource block allocation-via multi-agent xAPPs, while improving training efficiency. Numerical results demonstrate that the proposed algorithm significantly enhances QoE and reduces delay compared to benchmark algorithms.