Privacy-Aware Agent Collaboration for Dynamic VR Slice Management in 6G SD-RAN
It addresses the challenge of dynamic resource management for VR services in 6G networks while preserving user privacy.
The paper proposes a privacy-aware multi-agent reinforcement learning framework for VR slice management in 6G SD-RAN, achieving up to 34% throughput improvement, 28% fewer resources, and 85% less privacy leakage compared to traditional methods.
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 encoder to facilitate effective and secure agent collaboration. In simulations, comparisons with traditional methods are studied which show up to 34\% throughput improvement, 28\% fewer resources, and 85\% less privacy leakage, guaranteeing dependable immersive VR experiences in future 6G environments.