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Intelligent Power Controller of Wireless Body Area Networks Based on Deep Reinforcement Learning

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Bio-inspired Information and Communication Technologies (BICT 2020)

Abstract

Wireless Body Area networks allow groups of tiny sensors to communicate for purpose of medical applications. With the progress of sensor manufacture and artificial intelligence, abundant wearing devices are produced and applied with powerful intelligence functionalities. In wireless body area networks, battery energy capacity and inter-network interference are two serious threats to restrict the raise of performance. In this work, we focus on the power controlling theme in wireless body area networks. First, we introduce the primer overview of the deep-Q-Network algorithm, which is the method utilized in this work. Second, we present our communication system which is composed of two interfered WBANs. Third, we show how to design the power controller based on the deep-Q-network algorithm. The results reveal that our proposed power controller significantly decreases energy consumption by sacrificing little throughput performance.

Supported by the National Natural Science Foundation of China (Grant No. 61901070, 61871062, 61771082, 61801065), partially supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN201900611, KJQN201900604), and partially supported by Program for Innovation Team Building at Institutions of Higher Education in Chongqing (Grant No. CXTDX201601020).

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Authors and Affiliations

  1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China

    Peng He, Zhenli Liu, Lei Fu, Zhongyuan Tao, Jia Liu, Tong Tang & Zhidu Li

  2. CN Key Laboratory of Optical Communication and Networks in Chongqing, Chongqing, China

    Peng He, Zhenli Liu, Zhongyuan Tao, Jia Liu, Tong Tang & Zhidu Li

  3. Key Laboratory of Ubiquitous Sensing and Networking in Chongqing, Chongqing, China

    Peng He, Zhenli Liu, Zhongyuan Tao, Jia Liu, Tong Tang & Zhidu Li

Authors
  1. Peng He
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  2. Zhenli Liu
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  3. Lei Fu
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  4. Zhongyuan Tao
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  5. Jia Liu
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  6. Tong Tang
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  7. Zhidu Li
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Corresponding author

Correspondence to Peng He .

Editor information

Editors and Affiliations

  1. University of Electronic Science and Technology, Chengdu, China

    Yifan Chen

  2. Osaka University, Osaka, Japan

    Tadashi Nakano

  3. Tongji University, Shanghai, China

    Lin Lin

  4. Resch School of Engineering, University of Wisconsiin-Green Bay, Green Bay, WI, USA

    Mohammad Upal Mahfuz

  5. School of Engineering, Cranfield University, Cranfield, UK

    Weisi Guo

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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He, P. et al. (2020). Intelligent Power Controller of Wireless Body Area Networks Based on Deep Reinforcement Learning. In: Chen, Y., Nakano, T., Lin, L., Mahfuz, M., Guo, W. (eds) Bio-inspired Information and Communication Technologies. BICT 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-030-57115-3_21

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  • DOI: https://doi.org/10.1007/978-3-030-57115-3_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57114-6

  • Online ISBN: 978-3-030-57115-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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