VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Network

TL;DR

This paper proposes a VoI-driven joint optimization framework for control and communication in Vehicular Digital Twin Networks, leveraging deep reinforcement learning to enhance IoV system performance in 6G environments.

Contribution

It introduces a novel architecture and a VoI-based joint optimization framework utilizing DRL for control and communication in VDTN, addressing the dynamic interplay between these functions.

Findings

The framework improves control and communication efficiency in simulated platoon scenarios.

VoI effectively bridges control and communication for optimal decision-making.

Simulation results demonstrate enhanced system performance with the proposed method.

Abstract

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