DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks

arXiv:2605.23056v1 Announce Type: cross Abstract: 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 a

Source: arXiv cs.AI — read the full report at the original publisher.

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