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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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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