Deep Reinforcement Learning for Resource Management in Network Slicing

Rongpeng Li, Zhifeng Zhao, Qi Sun, I. Chi-Lin, Chenyang Yang, Xianfu Chen, Minjian Zhao, Honggang Zhang

    Research output: Contribution to journalArticleScientificpeer-review

    237 Citations (Scopus)


    Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users’ activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.
    Original languageEnglish
    Article number8540003
    Pages (from-to)74429 - 74441
    JournalIEEE Access
    Publication statusPublished - 2018
    MoE publication typeA1 Journal article-refereed


    • 5G mobile communication
    • Deep Reinforcement Learning
    • Network slicing
    • Network Slicing
    • Neural networks
    • Neural Networks
    • Q-Learning
    • Quality of experience
    • Resource management
    • Resource Management


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