Journal
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
Volume 10, Issue 6, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3317572
Keywords
-
Funding
- National Natural Science Foundation of China [61572106, 61971084]
- China Postdoctoral Science Foundation [2018T110210]
- State Key Laboratory of Integrated Services Networks, Xidian University [ISN20-01]
- State Key Laboratory for Novel Software Technology, Nanjing University [KFKT2018B04]
- Dalian Science and Technology Innovation Fund [2018J12GX048]
- National Natural Science Foundation of Chongqing [cstc2019jcyj-msxmX0208]
- FCT-Fundacao para a Ciencia e a Tecnologia [UID/EEA/50008/2019]
- MCTIC under the Centro de Referencia em Radiocomunicacoes-CRR project of the Instituto Nacional de Telecomunicacoes (Inatel), Brazil [01250.075413/2018-04]
- Brazilian National Council for Research and Development (CNPq) [309335/2017-5]
- RNP
Ask authors/readers for more resources
The development of smart vehicles brings drivers and passengers a comfortable and safe environment. Various emerging applications are promising to enrich users' traveling experiences and daily life. However, how to execute computing-intensive applications on resource-constrained vehicles still faces huge challenges. In this article, we construct an intelligent offloading system for vehicular edge computing by leveraging deep reinforcement learning. First, both the communication and computation states are modelled by finite Markov chains. Moreover, the task scheduling and resource allocation strategy is formulated as a joint optimization problem to maximize users' Quality of Experience (QoE). Due to its complexity, the original problem is further divided into two sub-optimization problems. A two-sided matching scheme and a deep reinforcement learning approach are developed to schedule offloading requests and allocate network resources, respectively. Performance evaluations illustrate the effectiveness and superiority of our constructed system.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available