A deep reinforcement learning based approach for cost- and energy-aware multi-flow mobile data offloading

Cheng Zhang, Zhi Liu, Bo Gu, Kyoko Yamori, Yoshiaki Tanaka

    研究成果: Article

    16 引用 (Scopus)

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    With the rapid increase in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying wireless local area network (LAN) hotspots on to which they can offload their mobile traffic. However, these network-centric methods usually do not fulfill the interests of mobile users (MUs). Taking into consideration many issues such as different applications' deadlines, monetary cost and energy consumption, how the MU decides whether to offload their traffic to a complementary wireless LAN is an important issue. Previous studies assume the MU's mobility pattern is known in advance, which is not always true. In this paper, we study the MU's policy to minimize his monetary cost and energy consumption without known MU mobility pattern. We propose to use a kind of reinforcement learning technique called deep Q-network (DQN) for MU to learn the optimal offloading policy from past experiences. In the proposed DQN based offloading algorithm, MU's mobility pattern is no longer needed. Furthermore, MU's state of remaining data is directly fed into the convolution neural network in DQN without discretization. Therefore, not only does the discretization error present in previous work disappear, but also it makes the proposed algorithm has the ability to generalize the past experiences, which is especially effective when the number of states is large. Extensive simulations are conducted to validate our proposed offloading algorithms.

    元の言語English
    ページ(範囲)1625-1634
    ページ数10
    ジャーナルIEICE Transactions on Communications
    E101B
    発行部数7
    DOI
    出版物ステータスPublished - 2018 7 1

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    ASJC Scopus subject areas

    • Software
    • Computer Networks and Communications
    • Electrical and Electronic Engineering

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