6G中用多智能体强化学习动态管理VR切片，兼顾性能与隐私。
Privacy-Aware Agent Collaboration for Dynamic VR Slice Management in 6G SD-RAN
- 基于移动性预测和信息瓶颈编码的协作智能体框架。
- 吞吐量提升34%，资源消耗减少28%，隐私泄露降低85%。
- 适合研究6G网络切片与隐私保护的学者或工程师。
6G网络中的虚拟现实（VR）服务需要超低时延和高吞吐，这对软件定义无线接入网（SD-RAN）的动态资源管理带来巨大挑战。本文提出一种面向移动性的、隐私感知的多智能体强化学习（MARL）框架，用于VR切片管理。该框架通过协作智能体在端到端VR链路上最大化资源分配，同时保护用户数据隐私。方法融合了移动性预测和信息瓶颈编码器，以实现高效且安全的智能体协同。仿真结果表明，相较于传统方法，本方案可实现最高34%的吞吐量提升，资源消耗减少28%，隐私泄露降低85%，有效保障未来6G环境中沉浸式VR体验的可靠性。
原文摘要 · Abstract (English)
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.
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