A Multi-Agent DRL-Based Dynamic Resource Allocation in O-RAN-Enabled TN-NTN Metaverse Services

The integration of terrestrial and non-terrestrial networks (TN-NTN) with open radio access network (O-RAN) technology presents a significant advancement for facilitating scalable and immersive Metaverse services within 6G networks. Seamless virtual experiences necessitate highly reliable, low-latency communication, effective resource management, and adaptive decision-making to satisfy the varied and rigorous requirements of Metaverse applications, including gaming, healthcare, and autonomous systems. The inherent heterogeneity, dynamic nature, and substantial resource requirements of TN-NTN present significant challenges for effective resource allocation and optimizing quality of experience (QoE). Then, we formulate a multi-objective optimization problem for joint resource allocation and spectrum sharing in O-RAN-enabled TN-NTN Metaverse environments. This problem is inherently NP-hard due to the intricate coupling between continuous action spaces and discrete decision variables. Solving such a complex problem using traditional optimization approaches is complex. To overcome this, we transform the problem into a decentralized partially observable Markov decision process (Dec-POMDP) and address it using a hierarchical multi-agent deep reinforcement learning (MADRL) approach. This study presents a hierarchical multi-agent proximal policy optimization (MAPPO) framework, a new MADRL solution for dynamic resource allocation and spectrum sharing in O-RAN-enabled TN-NTN Metaverse environments. MAPPO facilitates collaborative learning among intelligent agents to optimize resource management strategies in a decentralized manner, considering essential metrics, including energy consumption, latency, and meta-distance. The proposed framework enhances resource utilization efficiency, minimizes latency, and improves the QoE for Metaverse users through the seamless allocation and management of resources. Comprehensive simulations show that MAPPO outperforms baseline methods, such as conventional reinforcement learning and centralized optimization approaches, achieving better energy efficiency, lower latency, and improved QoE. This demonstrates its effectiveness in adapting to dynamic 6G-enabled Metaverse requirements, enabling intelligent and scalable TN-NTN networks.