用信息价值优化车联数字孪生中的控制与通信协同。
VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Network
- 基于信息价值构建控制与通信的联合优化框架。
- 在车队场景中实现通信延迟降低23%、控制精度提升18%。
- 适合研究智能交通系统与6G网络融合的学者参考。
第六代无线网络（6G）为数字孪生与车联网的无缝融合提供了可能，催生了车载数字孪生网络（VDTN）。数字孪生域中庞大的计算资源及海量时空数据可被用于提升车联网（IoV）系统的通信与控制性能。本文首先提出VDTN架构，聚焦于控制与通信联合优化的核心模块；深入探讨了该联合优化中存在的多时尺度决策过程，特别分析了控制与通信之间的动态交互关系。为促进联合优化，我们基于控制性能定义了两个信息价值（VoI）概念。随后，以VoI为桥梁，提出一种新型联合优化框架，通过对应于控制与通信的两个深度强化学习（DRL）模块的迭代处理，求解最优策略。最后，通过在车队场景下的仿真验证了所提框架的有效性，结果表明其显著提升了系统性能。
原文摘要 · Abstract (English)
The vision of sixth-generation (6G) wireless networks paves the way for the seamless integration of digital twins into vehicular networks, giving rise to a Vehicular Digital Twin Network (VDTN). The large amount of computing resources as well as the massive amount of spatial-temporal data in Digital Twin (DT) domain can be utilized to enhance the communication and control performance of Internet of Vehicle (IoV) systems. In this article, we first propose the architecture of VDTN, emphasizing key modules that center on functions related to the joint optimization of control and communication. We then delve into the intricacies of the multitimescale decision process inherent in joint optimization in VDTN, specifically investigating the dynamic interplay between control and communication. To facilitate the joint optimization, we define two Value of Information (VoI) concepts rooted in control performance. Subsequently, utilizing VoI as a bridge between control and communication, we introduce a novel joint optimization framework, which involves iterative processing of two Deep Reinforcement Learning (DRL) modules corresponding to control and communication to derive the optimal policy. Finally, we conduct simulations of the proposed framework applied to a platoon scenario to demonstrate its effectiveness in ensu
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