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
The accelerating demand for high-performance, low-latency services like VR in 6G networks necessitates intelligent resource optimization, making DRL a key enabler for future network architectures.
This paper highlights how deep reinforcement learning will be integral to managing the complexity and performance requirements of future telecommunications infrastructure, particularly for advanced applications like VR.
Network resource allocation and edge caching will transition from static or heuristic methods to adaptive, AI-driven systems capable of dynamic optimization across multi-slice 6G networks.
- · Telecommunication companies
- · AI/ML providers
- · Edge computing providers
- · Virtual Reality (VR) platforms
- · Traditional network equipment vendors (without DRL integration)
- · Legacy network management systems
- · Companies reliant on high-latency networks
Improved performance and reliability of advanced services like VR over 6G networks.
Increased adoption and monetization opportunities for bandwidth-intensive, low-latency applications due to enhanced network capabilities.
New competitive landscape in telecommunications, favoring operators and vendors adept at integrating AI-driven network management.
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Read at arXiv cs.AI