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

• URL: http://arxiv.org/abs/2505.07892v1
• Date: Mon, 12 May 2025 00:53:37 GMT
• Title: VoI-Driven Joint Optimization of Control and Communication in Vehicular Digital Twin Network
• Authors: Lei Lei, Kan Zheng, Jie Mei, Xuemin, Shen,
• Abstract summary: 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)<n>We first propose the architecture of VDTN, emphasizing key modules that center on functions related to the joint optimization of control and communication.<n>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.
• Score: 18.562734500767075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

Related papers

• Deep Learning Optimization of Two-State Pinching Antennas Systems [48.70043547158868]
  Pinching antennas (PAs) can dynamically control electromagnetic wave propagation through binary activation states.<n>In this work, we investigate the problem of optimally selecting a subset of fixed-position PAs to activate in a waveguide, when the aim is to maximize the communication rate at a user terminal.
  arXiv  Detail & Related papers  (2025-07-08T17:55:54Z)
• From Pixels to CSI: Distilling Latent Dynamics For Efficient Wireless Resource Management [31.080933663717257]
  We propose a novel machine learning technique to jointly model and predict the dynamics of the control system.<n>We show that our proposed approach reduces transmit power by over 50% while maintaining control performance comparable to baseline methods.
  arXiv  Detail & Related papers  (2025-06-19T11:08:20Z)
• Learning Value of Information towards Joint Communication and Control in 6G V2X [12.846064594551873]
  We propose a systematic VoI modeling framework grounded in the MDP, Reinforcement Learning (RL) and Optimal Control theories.<n>We present a structured approach to leverage the various VoI metrics for optimizing the When", What", and How" to communicate problems.
  arXiv  Detail & Related papers  (2025-05-11T13:30:35Z)
• Automatic tuning of communication protocols for vehicular ad hoc networks using metaheuristics [4.734135226897704]
  vehicular ad hoc networks (VANETs) deal with a set of communicating vehicles which are able to spontaneously interconnect.<n>It is crucial to make an optimal configuration of the communication protocols previously to the final network deployment.
  arXiv  Detail & Related papers  (2025-01-15T14:59:00Z)
• Joint Adaptive OFDM and Reinforcement Learning Design for Autonomous Vehicles: Leveraging Age of Updates [2.607046313483251]
  Millimeter wave (mmWave)-based frequency-division multiplexing (OFDM) stands out as a suitable alternative for high-resolution sensing and high-speed data transmission.<n>In this work, we consider an autonomous vehicle network where an AV utilizes its queue state information (QSI) and channel state information (CSI) in conjunction with reinforcement learning techniques to manage communication and sensing.
  arXiv  Detail & Related papers  (2024-12-24T15:32:58Z)
• DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV [33.32647734550201]
  This paper analyzes the effects of multi-priority queues and NOMA on Age of Information in the C-V2X vehicular communication system. The proposed approach demonstrates its advances in terms of energy consumption and AoI.
  arXiv  Detail & Related papers  (2024-11-20T07:59:35Z)
• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks.<n> embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption.<n> split computing - where an SNN is partitioned across two devices - is a promising solution.<n>This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications [43.443526528832145]
  This paper proposes a method that integrates Graph Neural Networks (GNN) with Deep Reinforcement Learning (DRL) to address this challenge. By constructing a dynamic graph with communication links as nodes, the model aims to ensure a high success rate for V2V communication. The proposed method retains the global feature learning capabilities of GNN and supports distributed network deployment.
  arXiv  Detail & Related papers  (2024-07-09T03:14:11Z)
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• Deep Learning-based Resource Allocation For Device-to-Device Communication [66.74874646973593]
  We propose a framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication. A deep learning (DL) framework is proposed, where the optimal resource allocation strategy for arbitrary channel conditions is approximated by deep neural network (DNN) models. Our simulation results confirm that near-optimal performance can be attained with low time, which underlines the real-time capability of the proposed scheme.
  arXiv  Detail & Related papers  (2020-11-25T14:19:23Z)
• Path Design and Resource Management for NOMA enhanced Indoor Intelligent Robots [58.980293789967575]
  A communication enabled indoor intelligent robots (IRs) service framework is proposed. Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state. The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent.
  arXiv  Detail & Related papers  (2020-11-23T21:45:01Z)
• Wireless Power Control via Counterfactual Optimization of Graph Neural Networks [124.89036526192268]
  We consider the problem of downlink power control in wireless networks, consisting of multiple transmitter-receiver pairs communicating over a single shared wireless medium. To mitigate the interference among concurrent transmissions, we leverage the network topology to create a graph neural network architecture. We then use an unsupervised primal-dual counterfactual optimization approach to learn optimal power allocation decisions.
  arXiv  Detail & Related papers  (2020-02-17T07:54:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.