DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks
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 ...
| Authors | Khaled M. Naguib, Soumaya Cherkaoui, Mahmoud M. Elmessalawy et al. |
|---|---|
| Primary category | cs.NI |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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