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Distributed Power Controller of Massive Wireless Body Area Networks based on Deep Reinforcement Learning


Wireless body area network (WBAN) is encountering a tough challenge in terms of energy efficiency due to multiple realistic factors like increasing scale of network environment, emerging demand of healthcare applications and limited manufacturing technique of sensors. In this work, we address the energy saving issue of WBAN. We consider a layered network framework and hybrid channels with multiple in vivo medium. A distributed power controller is developed based on deep Q-learning algorithm to mitigate the affection of inter-network interference. The proposed power controller utilizes distributed coordinators to learn from WBAN environment and optimize the transmitting power of sensors in the communication. Simulation results demonstrate that our power controller achieves higher performance of energy efficiency compared with two baseline power controllers. Simulation results also demonstrate that proper configuration of proposed power controller of coordinators can significantly achieve the performance gain with the increase of network scale.

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This work was 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, KJQN201900609), and partially supported by Program for Innovation Team Building at Institutions of Higher Education in Chongqing (Grant No. CXTDX201601020).

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Correspondence to Peng He.

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He, P., Liu, M., Lan, C. et al. Distributed Power Controller of Massive Wireless Body Area Networks based on Deep Reinforcement Learning. Mobile Netw Appl 26, 1347–1358 (2021).

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  • Wireless body area networks
  • Power control
  • Energy efficiency
  • Deep Q-network