计算机科学
强化学习
马尔可夫决策过程
资源配置
过程(计算)
分布式计算
转码
资源管理(计算)
蜂窝网络
马尔可夫过程
资源(消歧)
人工智能
实时计算
计算机网络
统计
数学
操作系统
作者
Tan Le,Martin Reisslein,Sachin Shetty
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-10
标识
DOI:10.1109/tits.2023.3303953
摘要
This paper studies artificial intelligence (AI) aided communication and computing resource allocation in a vehicular network that supports blockchain-enabled video streaming. Our study aims to improve the operating efficiency and to maximize the transcoding rewards for blockchain based vehicular networks. Our resource allocation policy considers the vehicular mobility, which is modelled with a highly-realistic Semi-Markov renewal process, as well as the real-time video service delay constraints. We propose a multi-timescale actor-critic-reinforcement learning framework to tackle these grand challenges. We also develop a prediction model for the vehicular mobility by using analysis and classical machine learning, which alleviates the heavy signaling and computation overheads due to the vehicular movement. A mobility-aware reward estimation for the large timescale model is then proposed to mitigate the complexity due to the large action space. Finally, numerical results are presented to illustrate the developed theoretical findings in this paper and the significant performance gains due to our proposed multi-timescale framework.
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