用深度强化学习优化6G多切片网络的边缘缓存与资源分配。
DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks
- 基于DQN的智能代理动态调度边缘资源和缓存内容。
- 在仿真中显著降低延迟并提升吞吐量，尤其支持VR所需的MBRLLC切片。
- 适合研究6G网络智能控制、边缘计算与VR应用的开发者。
6G网络提供的虚拟现实（VR）服务需要超低延迟和高带宽以确保流畅体验。本文提出一种面向6G O-RAN网络的智能资源分配与边缘缓存框架，利用深度Q网络（DQN）优化多网络切片中的边缘缓存与动态资源供给。通过将DRL智能体集成至网络控制平面，该系统实现内容的主动分发与实时计算资源调配，满足eMBB、URLLC以及新兴的MBRLLC切片对服务质量的要求。仿真结果表明，基于DQN的框架在降低延迟和提升吞吐量方面持续优于传统方法，为6G环境下的沉浸式VR应用提供更可靠、响应更快的支持。
原文摘要 · Abstract (English)
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 as well as real-time computational resource allocation that meets the quality-of-service demands of eMBB, URLLC, and especially the emerging MBRLLC slices essential for VR. Simulation results demonstrate that the DQN-based framework consistently outperforms traditional methods in reducing latency and improving throughput, leading to more reliable and responsive support for immersive VR applications in 6G environments.
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