DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks
This work addresses the need for ultra-low latency and high bandwidth in 6G VR services by optimizing multi-slice resource allocation, but the gains are incremental over existing DRL approaches.
The paper proposes a DQN-based resource allocation and edge caching framework for 6G O-RAN networks to support VR services, achieving reduced latency and improved throughput compared to traditional methods.
Virtual Reality (VR) services delivered over 6G networks demand ultra-low latency and high bandwidth to ensure seamless user experiences. This paper presents an intelligent resource allocation and edge caching framework for 6G O-RAN networks, leveraging Deep Q-Network (DQN) learning for optimizing edge caching and dynamic resource provisioning across multiple network slices within an O-RAN-compliant architecture. By incorporating DRL agents into the network control plane, the proposed system enables proactive and adaptive content distribution as well as real-time computational resource allocation that meets the quality-of-service demands of eMBB, URLLC, and especially the emerging MBRLLC slices essential for VR. Simulation results demonstrate that the DQN-based framework consistently outperforms traditional methods in reducing latency and improving throughput, leading to more reliable and responsive support for immersive VR applications in 6G environments.