期刊
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
卷 22, 期 4, 页码 2226-2238出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TITS.2020.3015210
关键词
Task analysis; Servers; Edge computing; Delays; Calculus; Upper bound; Stochastic processes; Vehicular edge computing; DSRC; C-V2X; mmWave; federated Q-learning
资金
- National Key Research and Development Program of China [2018YFE0117500]
- Science and Technology Program of Sichuan Province, China [2019YFG0520]
- EU H2020 Project COSAFE [MSCA-RISE-2018-824019]
- China Scholarship Council
- A*STAR under its RIE2020 Advanced Manufacturing and Engineering (AME) Industry Alignment Fund-Pre Positioning (IAF-PP) [A19D6a0053]
This article proposes an intelligent task offloading framework in heterogeneous vehicular networks with three V2X communication technologies and utilizes a federated Q-learning method to minimize the communication/computing budgets and the offloading failure probabilities, showing significant improvements over existing algorithms.
With the rapid development of autonomous driving technologies, it becomes difficult to reconcile the conflict between ever-increasing demands for high process rate in the intelligent automotive tasks and resource-constrained on-board processors. Fortunately, vehicular edge computing (VEC) has been proposed to meet the pressing resource demands. Due to the delay-sensitive traits of automotive tasks, only a heterogeneous vehicular network with multiple access technologies may be able to handle these demanding challenges. In this article, we propose an intelligent task offloading framework in heterogeneous vehicular networks with three Vehicle-to-Everything (V2X) communication technologies, namely Dedicated Short Range Communication (DSRC), cellular-based V2X (C-V2X) communication, and millimeter wave (mmWave) communication. Based on stochastic network calculus, this article firstly derives the delay upper bounds of different offloading technologies with certain failure probabilities. Moreover, we propose a federated Q-learning method that optimally utilizes the available resources to minimize the communication/computing budgets and the offloading failure probabilities. Simulation results indicate that our proposed algorithm can significantly outperform the existing algorithms in terms of resource cost and offloading failure probability.
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