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

Lei Lei, Kan Zheng, Jie Mei, Xuemin, Shen

TL;DR

This work tackles joint optimization of control and communication in a Vehicular Digital Twin Network (VDTN) powered by 6G, introducing Value of Information (VoI) as a principled bridge between decision-making in control and resource allocation in communication. It proposes a multitimescale DRL framework with CTDE, operating in a virtual space to robustly train control and communication policies, while accounting for non-ideal V2X links that impair observations. VoI is defined via two metrics, Expected Cumulative VoI ($E$VoI) and Immediate VoI ($IVoI$), guiding an iterative JOCC procedure where a control-aware policy is learned under a given communication policy and vice versa. A platoon case study demonstrates that JOCC maintains control performance while achieving competitive or improved data throughput on V2I links, validating the approach and highlighting future research directions in VoI estimation, MARL scalability, and deployment challenges.

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

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

TL;DR

This work tackles joint optimization of control and communication in a Vehicular Digital Twin Network (VDTN) powered by 6G, introducing Value of Information (VoI) as a principled bridge between decision-making in control and resource allocation in communication. It proposes a multitimescale DRL framework with CTDE, operating in a virtual space to robustly train control and communication policies, while accounting for non-ideal V2X links that impair observations. VoI is defined via two metrics, Expected Cumulative VoI (VoI) and Immediate VoI (), guiding an iterative JOCC procedure where a control-aware policy is learned under a given communication policy and vice versa. A platoon case study demonstrates that JOCC maintains control performance while achieving competitive or improved data throughput on V2I links, validating the approach and highlighting future research directions in VoI estimation, MARL scalability, and deployment challenges.

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
Paper Structure (16 sections, 3 equations, 4 figures, 2 tables)

This paper contains 16 sections, 3 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. System Architecture of the VDTN
  3. Data Augmentation
  4. Virtual Representation
  5. Decision Making
  6. DRL-based VoI for Decision Making
  7. Multitimescale Decision Process
  8. Interplay between Control and Communication
  9. Impact of the RRA policy on control
  10. Impact of the AVC policy on communication
  11. VoI Definition
  12. Joint Optimization of Control and Communication
  13. Learning of communication-aware control policy
  14. Learning of control-aware RRA policy:
  15. A Case Study on Vehicular Platoon Applications in the VDTN
  16. ...and 1 more sections

Figures (4)

  • Figure 1: Illustration of the system architecture of the Vehicular Digital Twin Network (VDTN).
  • Figure 2: Framework of Joint Optimization of Control and Communication in VDTN.
  • Figure 3: Simulation scenario of the vehicular platoon supported by VDTN.
  • Figure 4: Performance comparison of AVs in the platoon system supported by VDTN.