Learning-Based Joint Resource Slicing and Scheduling in Space-Terrestrial Integrated Vehicular Networks
查看参考文献31篇
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文摘
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In this paper, we investigate the resource slicing and scheduling problem in the space-terrestrial integrated vehicular networks to support both delaysensitive services (DSSs)and delay-tolerant services (DTSs).Resource slicing and scheduling are to allocate spectrum resources to different slices and determine user association and bandwidth allocation for individual vehicles.To accommodate the dynamic network conditions, we first formulate a joint resource slicing and scheduling (JRSS)problem to minimize the long-term system cost, including the DSS requirement violation cost, DTS delay cost, and slice reconfiguration cost.Since resource slicing and scheduling decisions are interdependent with different timescales, we decompose the JRSS problem into a large-timescale resource slicing subproblem and a smalltimescale resource scheduling subproblem.We propose a two-layered reinforcement learning (RL)-based JRSS scheme to find the solutions to the subproblems.In the resource slicing layer, spectrum resources are pre-allocated to different slices via a proximal policy optimization-based RL algorithm.In the resource scheduling layer, spectrum resources in each slice are scheduled to individual vehicles based on dynamic network conditions and service requirements via matching-based algorithms.We conduct extensive trace-driven experiments to demonstrate that the proposed scheme can effectively reduce the system cost while satisfying service quality requirements. |
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来源
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Journal of Communications and Information Networks
,2021,6(3):208-223 【核心库】
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DOI
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10.23919/JCIN.2021.9549118
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关键词
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space-terrestrial integrated vehicular networks
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LEO satellite communication
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resource slicing and scheduling
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reinforcement learning
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matching-based optimization
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地址
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1.
Department of Electrical and Computer Engineering, University of Waterloo, Canada, Waterloo, N2L 3G1
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Department of Electrical and Computer Engineering, the University of British Columbia, Canada, Vancouver, V6T 1Z4
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Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS), and the Department of Electrical and Computer Engineering, University of Waterloo, Canada
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语种
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英文 |
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文献类型
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研究性论文 |
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ISSN
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2096-1081 |
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学科
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电子技术、通信技术 |
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基金
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Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS)
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supported in part by the Natural Sciences and Engineering Research Council (NSERC)of Canada
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文献收藏号
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CSCD:7054593
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