DQN-Based Edge Caching and Resource Allocation for 6G VR Slices

A new framework for 6G O-RAN networks uses Deep Q-Network (DQN) learning to optimize edge caching and dynamic resource allocation across multiple network slices. The system targets ultra-low latency and high bandwidth for Virtual Reality (VR) services, addressing the quality-of-service demands of eMBB, URLLC, and emerging MBRLLC slices. DRL agents integrated into the network control plane enable proactive content distribution and real-time computational resource allocation. Simulations show the DQN-based approach consistently reduces latency and improves throughput compared to traditional methods, enhancing support for immersive VR applications.