Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach
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- 6 October 2017
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Vehicular Technology
- Vol. 67 (1), 44-55
- https://doi.org/10.1109/tvt.2017.2760281
Abstract
The developments of connected vehicles are heavily influenced by information and communications technologies, which have fueled a plethora of innovations in various areas, including networking, caching and computing. Nevertheless, these important enabling technologies have traditionally been studied separately in the existing works on vehicular networks. In this paper, we propose an integrated framework that can enable dynamic orchestration of networking, caching and computing resources to improve the performance of next generation vehicular networks. We formulate the resource allocation strategy in this framework as a joint optimization problem, where the gains of not only networking but also caching and computing are taken into consideration in the proposed framework. The complexity of the system is very high when we jointly consider these three technologies. Therefore, we propose a novel deep reinforcement learning approach in this paper. Simulation results with different system parameters are presented to show the effectiveness of the proposed scheme.Keywords
Funding Information
- Xinghai Scholars Program
- Fundamental Research Funds for the Central Universities (DUT17JC43)
- National Natural Science Foundation of China (61771089, 61671101)
This publication has 30 references indexed in Scilit:
- From today's VANETs to tomorrow's planning and the bets for the day afterVehicular Communications, 2015
- Cooperative Data Scheduling in Hybrid Vehicular Ad Hoc Networks: VANET as a Software Defined NetworkIEEE/ACM Transactions on Networking, 2015
- Information-centric network function virtualization over 5g mobile wireless networksIEEE Network, 2015
- Human-level control through deep reinforcement learningNature, 2015
- A Survey of Green Information-Centric Networking: Research Issues and ChallengesIEEE Communications Surveys & Tutorials, 2015
- Wireless Network Virtualization: A Survey, Some Research Issues and ChallengesIEEE Communications Surveys & Tutorials, 2014
- Finite-State Markov Modeling for Wireless Channels in Tunnel Communication-Based Train Control SystemsIEEE Transactions on Intelligent Transportation Systems, 2014
- A Survey of Information-Centric Networking ResearchIEEE Communications Surveys & Tutorials, 2013
- A new method to support UMTS/WLAN vertical handover using SCTPIEEE Wireless Communications, 2004
- Introduction: The Challenge of Reinforcement LearningPublished by Springer Science and Business Media LLC ,1992