arXiv:2606.26123v1 Announce Type: cross Abstract: Ultra-low latency and high throughput are required for Virtual Reality (VR) services in 6G networks, which presents critical challenges for Software-Defined Radio Access Networks (SD-RANs) dynamic resource management. This work propose a mobility-driven, privacy-aware Multi-Agent Reinforcement Learning (MARL) framework for VR slice management, in which cooperative agents maximize resource distribution over end-to-end VR links while protecting the privacy of user data. Our approach incorporates mobility prediction and an information bottleneck e
The increasing demands of VR services necessitate advanced network management solutions as 6G research and development mature.
Sophisticated AI agent coordination for dynamic resource management is critical for enabling next-generation communication networks and immersive technologies.
Networking infrastructure will increasingly rely on autonomous, privacy-aware AI agents to manage complex dynamic resource allocation for demanding applications like VR.
- · Telecommunication companies
- · AI developers
- · VR/AR industry
- · Cloud infrastructure providers
- · Traditional network management systems
- · Companies without strong AI integration
Improved performance and reliability of VR services on mobile networks.
Accelerated adoption and development of immersive virtual and augmented reality applications due to enhanced foundational network support.
New privacy-preserving AI agent architectures become standard for distributed computing and critical infrastructure management.
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