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arXiv 2603.04779cs.NI
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自私合作面向低空经济:集成多服务部署与具有鲁棒性的联邦强化学习

Selfish Cooperation Towards Low-Altitude Economy: Integrated Multi-Service Deployment with Resilient Federated Reinforcement Learning

University of Sydney(悉尼大学);Nanjing University of Posts and Telecommunications(南京邮电大学)

Yuxuan Yang, Bin Lyu, Abbas Jamalipour

AI总结:

本文提出一种基于鲁棒联邦强化学习的解决方案,通过保证真实性拍卖机制和博弈论分析,解决低空经济中多服务提供商之间的竞争与合作问题,提升服务性能和系统鲁棒性。

摘要:

低空经济(LAE)是一种快速兴起的范式,通过大规模可持续的无人机(UAV)服务提供构建服务导向型经济生态系统,反映了6G时代从技术进步向商业部署的转变。LAE显著市场潜力吸引了越来越多的服务提供商(SPs),导致服务部署竞争加剧。本文研究了一个现实的LAE场景,其中多个SPs动态部署无人机向用户热点提供多种服务,旨在联合优化通信和计算资源分配。为了解决SPs之间的部署竞争,设计了一个保证真实性拍卖机制,并进行博弈论分析以建立所提出资源分配问题的可解性。此外,开发了一种具有强容错能力的联邦强化学习(FRL)基于解决方案,能够有效对抗传输错误和恶意竞争,同时促进自利SPs之间的潜在合作。仿真结果表明,所提出的方法在服务性能和鲁棒性方面显著优于基线方法,为竞争性的LAE服务部署提供了实用且可扩展的解决方案。

英文摘要:

The low-altitude economy (LAE) is a rapidly emerging paradigm that builds a service-centric economic ecosystem through large-scale and sustainable uncrewed aerial vehicle (UAV)-enabled service provisioning, reflecting the transition of the 6G era from technological advancement toward commercial deployment. The significant market potential of LAE attracts an increasing number of service providers (SPs), resulting in intensified competition in service deployment. In this paper, we study a realistic LAE scenario in which multiple SPs dynamically deploy UAVs to deliver multiple services to user hotspots, aiming to jointly optimize communication and computation resource allocation. To resolve deployment competition among SPs, an authenticity-guaranteed auction mechanism is designed, and game-theoretic analysis is conducted to establish the solvability of the proposed resource allocation problem. Furthermore, a resilient federated reinforcement learning (FRL)-based solution is developed with strong fault tolerance, effectively countering transmission errors and malicious competition while facilitating potential cooperation among self-interested SPs. Simulation results demonstrate that the proposed approach significantly improves service performance and robustness compared with baseline methods, providing a practical and scalable solution for competitive LAE service deployment.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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