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

• URL: http://arxiv.org/abs/2411.04139v1
• Date: Fri, 01 Nov 2024 04:34:54 GMT
• Title: Diffusion-based Auction Mechanism for Efficient Resource Management in 6G-enabled Vehicular Metaverses
• Authors: Jiawen Kang, Yongju Tong, Yue Zhong, Junlong Chen, Minrui Xu, Dusit Niyato, Runrong Deng, Shiwen Mao,
• Abstract summary: In 6G-enable Vehicular Metaverses, vehicles are represented by Vehicle Twins (VTs), which serve as digital replicas of physical vehicles. VT tasks are resource-intensive and need to be offloaded to ground Base Stations (BSs) for fast processing. We propose a learning-based Modified Second-Bid (MSB) auction mechanism to optimize resource allocation between ground BSs and UAVs.
• Score: 57.010829427434516
• License:
• 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.

Related papers

• 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)
• Hybrid-Generative Diffusion Models for Attack-Oriented Twin Migration in Vehicular Metaverses [58.264499654343226]
  Vehicle Twins (VTs) are digital twins that provide immersive virtual services for Vehicular Metaverse Users (VMUs) High mobility of vehicles, uneven deployment of edge servers, and potential security threats pose challenges to achieving efficient and reliable VT migrations. We propose a secure and reliable VT migration framework in vehicular metaverses.
  arXiv  Detail & Related papers  (2024-07-05T11:11:33Z)
• Diffusion-based Reinforcement Learning for Dynamic UAV-assisted Vehicle Twins Migration in Vehicular Metaverses [11.608114188345692]
  Vehicle Twins (VTs) are the digital twins of physical vehicles to enable vehicular Metaverses services. Air-ground integrated networks can relieve communication pressure on ground transportation networks and provide 6G-enabled vehicular Metaverses services offloading. We propose a dynamic Unmanned Aerial Vehicle (UAV)-assisted VT migration framework in air-ground integrated networks.
  arXiv  Detail & Related papers  (2024-06-08T09:53:56Z)
• Multi-attribute Auction-based Resource Allocation for Twins Migration in Vehicular Metaverses: A GPT-based DRL Approach [85.65587846913793]
  Vehicular Metaverses are developed to enhance the modern automotive industry with an immersive and safe experience among connected vehicles. We propose an attribute-aware auction-based mechanism to optimize resource allocation during VTs migration. We train a DDA auctioneer using a generative pre-trained transformer (GPT)-based deep reinforcement learning (DRL) algorithm to adjust the auction clocks efficiently.
  arXiv  Detail & Related papers  (2024-06-08T09:41:38Z)
• Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs [21.195346908715972]
  Unmanned aerial vehicles present an alternative means to offload data traffic from terrestrial BSs. This paper presents a novel approach to efficiently serve multiple UAVs for data offloading from terrestrial BSs.
  arXiv  Detail & Related papers  (2024-02-05T12:36:08Z)
• Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach [57.15309977293297]
  The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses. We propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses.
  arXiv  Detail & Related papers  (2024-01-18T02:14:13Z)
• Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays [55.736718475856726]
  Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT) Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points. We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
  arXiv  Detail & Related papers  (2023-08-03T02:49:50Z)
• LLHR: Low Latency and High Reliability CNN Distributed Inference for Resource-Constrained UAV Swarms [2.320417845168326]
  Unmanned Aerial Vehicles (UAVs) have shown impressive performance in many critical applications, such as surveillance, search and rescue operations, environmental monitoring, etc. One promising solution is to divide the inference requests into subtasks that can be distributed among UAVs in a swarm based on the available resources. Our system model deals with real-time requests, aiming to find the optimal transmission power that guarantees higher reliability and low latency.
  arXiv  Detail & Related papers  (2023-05-25T08:47:16Z)
• Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach [88.45509934702913]
  We design a navigation policy for multiple unmanned aerial vehicles (UAVs) where mobile base stations (BSs) are deployed. We incorporate different contextual information such as energy and age of information (AoI) constraints to ensure the data freshness at the ground BS. By applying the proposed trained model, an effective real-time trajectory policy for the UAV-BSs captures the observable network states over time.
  arXiv  Detail & Related papers  (2020-02-21T07:29:15Z)

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.