Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Diffusion-based Auction Mechanism for Efficient Resource Management in 6G-enabled Vehicular Metaverses

Jiawen Kang, Yongju Tong, Yue Zhong, Junlong Chen, Minrui Xu, Dusit Niyato, Runrong Deng, Shiwen Mao

TL;DR

A learning-based modified second-bid (MSB) auction mechanism to optimize resource allocation between ground BSs and UAVs by accounting for VT task latency and accuracy is proposed, and a diffusion-based reinforcement learning algorithm is designed to optimize the price scaling factor.

Abstract

The rise of 6G-enable Vehicular Metaverses is transforming the automotive industry by integrating immersive, real-time vehicular services through ultra-low latency and high bandwidth connectivity. In 6G-enable Vehicular Metaverses, vehicles are represented by Vehicle Twins (VTs), which serve as digital replicas of physical vehicles to support real-time vehicular applications such as large Artificial Intelligence (AI) model-based Augmented Reality (AR) navigation, called VT tasks. VT tasks are resource-intensive and need to be offloaded to ground Base Stations (BSs) for fast processing. However, high demand for VT tasks and limited resources of ground BSs, pose significant resource allocation challenges, particularly in densely populated urban areas like intersections. As a promising solution, Unmanned Aerial Vehicles (UAVs) act as aerial edge servers to dynamically assist ground BSs in handling VT tasks, relieving resource pressure on ground BSs. However, due to high mobility of UAVs, there exists information asymmetry regarding VT task demands between UAVs and ground BSs, resulting in inefficient resource allocation of UAVs. To address these challenges, we propose a learning-based Modified Second-Bid (MSB) auction mechanism to optimize resource allocation between ground BSs and UAVs by accounting for VT task latency and accuracy. Moreover, we design a diffusion-based reinforcement learning algorithm to optimize the price scaling factor, maximizing the total surplus of resource providers and minimizing VT task latency. Finally, simulation results demonstrate that the proposed diffusion-based MSB auction outperforms traditional baselines, providing better resource distribution and enhanced service quality for vehicular users.

Diffusion-based Auction Mechanism for Efficient Resource Management in 6G-enabled Vehicular Metaverses

TL;DR

A learning-based modified second-bid (MSB) auction mechanism to optimize resource allocation between ground BSs and UAVs by accounting for VT task latency and accuracy is proposed, and a diffusion-based reinforcement learning algorithm is designed to optimize the price scaling factor.

Abstract

The rise of 6G-enable Vehicular Metaverses is transforming the automotive industry by integrating immersive, real-time vehicular services through ultra-low latency and high bandwidth connectivity. In 6G-enable Vehicular Metaverses, vehicles are represented by Vehicle Twins (VTs), which serve as digital replicas of physical vehicles to support real-time vehicular applications such as large Artificial Intelligence (AI) model-based Augmented Reality (AR) navigation, called VT tasks. VT tasks are resource-intensive and need to be offloaded to ground Base Stations (BSs) for fast processing. However, high demand for VT tasks and limited resources of ground BSs, pose significant resource allocation challenges, particularly in densely populated urban areas like intersections. As a promising solution, Unmanned Aerial Vehicles (UAVs) act as aerial edge servers to dynamically assist ground BSs in handling VT tasks, relieving resource pressure on ground BSs. However, due to high mobility of UAVs, there exists information asymmetry regarding VT task demands between UAVs and ground BSs, resulting in inefficient resource allocation of UAVs. To address these challenges, we propose a learning-based Modified Second-Bid (MSB) auction mechanism to optimize resource allocation between ground BSs and UAVs by accounting for VT task latency and accuracy. Moreover, we design a diffusion-based reinforcement learning algorithm to optimize the price scaling factor, maximizing the total surplus of resource providers and minimizing VT task latency. Finally, simulation results demonstrate that the proposed diffusion-based MSB auction outperforms traditional baselines, providing better resource distribution and enhanced service quality for vehicular users.

Paper Structure

This paper contains 21 sections, 1 theorem, 20 equations, 6 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. Vehicular Metaverses
  4. Resource Allocation in 6G-enabled Vehicular Metaverses
  5. Generative Models in Auction Mechanisms
  6. System Model
  7. Network Model
  8. Latency Model
  9. Accuracy Model
  10. Market Design And Problem Formulation
  11. Market Design
  12. Problem Formulation
  13. Diffusion-based Modified Second-bid Auction
  14. Modified Second-Bid Auction
  15. Diffusion-based Pricing Scaling Factor
  16. ...and 6 more sections

Key Result

Theorem 1

The DMSB auction, utilizing a dynamic price scaling policy governed by parameters $\rho$ and optimized through the Diffusion-based RL algorithm, maintains anonymity, fully strategy-proof, and is free from adverse selection.

Figures (6)

  • Figure 1: The system model of our proposed DMSB auction for resource allocation in 6G-enabled Vehicular Metaverses.
  • Figure 2: The architecture of the Diffusion-based RL algorithm for dynamic price scaling in the MSB auction.
  • Figure 3: Convergence of the DMSB auction scheme compared with PPO, Greedy, Random, and theoretical auction methods.
  • Figure 4: Convergence of the proposed DMSB auction under surplus compared with the different number of resource providers.
  • Figure 5: Comparison of total surplus under different DMSB auction environment settings.
  • ...and 1 more figures

Theorems & Definitions (2)

  • Theorem 1
  • proof