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Deep Reinforcement Learning based Multi-attribute Auction Model for Resource Allocation in Vehicular AIGC Services

Published: 03 July 2024 Publication History

Abstract

Artificial Intelligence-Generated Content (AIGC) has garnered widespread attention due to its ability to automatically generate diverse and valuable digital content. Integrating with AIGC, vehicular networks are evolving as the next-generation intelligent transportation system, i.e., vehicular Metaverses, which can significantly enrich the diverse and personalized immersive experience of vehicular applications for drivers and passengers. Nevertheless, due to the high demand for AIGC services and the limited coverage of RoadSide Units (RSUs), it is still challenging to efficiently allocate RSU resources for massive vehicular users. In this paper, we propose an attribute-aware auction-based mechanism for real-time resource allocation in vehicular AIGC services, which considers both price and non-monetary attributes. In this mechanism, we first formulate the resource-attribute matching problem as a weighted bipartite graph to determine potential market participants. Then, we train an auctioneer in a double Dutch auction using Deep Reinforcement Learning (DRL) algorithms to adjust the auction clocks efficiently during the clock adjustment process. Finally, simulation results show that the proposed DRL-based auction scheme outperforms existing baseline schemes in terms of average cumulative reward, convergence speed, social welfare, and auction information exchange costs.

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  • (2024)The Emergence of the Vehicular Metaverse: A Scoping ReviewCONAT 2024 International Congress of Automotive and Transport Engineering10.1007/978-3-031-77635-9_11(120-135)Online publication date: 20-Nov-2024

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    GAIIS '24: Proceedings of the 2024 International Conference on Generative Artificial Intelligence and Information Security
    May 2024
    439 pages
    ISBN:9798400709562
    DOI:10.1145/3665348
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 03 July 2024

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    • (2024)The Emergence of the Vehicular Metaverse: A Scoping ReviewCONAT 2024 International Congress of Automotive and Transport Engineering10.1007/978-3-031-77635-9_11(120-135)Online publication date: 20-Nov-2024

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