QTPC: A Q-Learning Transmission Power Control Mechanism for Edge-Cloud Wireless Body Area Networks - Details

Author：

Su, Haoru (Su, Haoru.) | Yuan, Xiaoming (Yuan, Xiaoming.) | Sun, Enchang (Sun, Enchang.) | Si, Pengbo (Si, Pengbo.) | Chen, Huamin (Chen, Huamin.)

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EI Scopus

Abstract：

Wireless　body　area　networks　collect　biological　signals　from　human　body　and　sensors　connect　wirelessly　for　various　nonmedical　and　medical　applications.　Energy　efficiency　is　one　of　the　most　essential　problems　in　wireless　body　area　networks　since　the　limited　battery　capacity.　In　this　paper,　a　Q-learning　transmission　power　control　(QTPC)　mechanism　for　edge-cloud　wireless　body　area　networks　is　proposed.　Simulation　results　show　that　the　proposed　scheme　improves　the　network　performance　in　the　metrics　of　energy　efficiency　and　system　throughput.　©　Published　under　licence　by　IOP　Publishing　Ltd.

Keyword：

Energy efficiency Medical applications Reinforcement learning Power control Learning algorithms

Author Community：

[ 1 ] [Su, Haoru]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Yuan, Xiaoming]Qinhuangdao Branch Campus, Northeastern University, Shenyang, Liaoning; 110819, China
[ 3 ] [Sun, Enchang]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 4 ] [Si, Pengbo]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 5 ] [Chen, Huamin]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China

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Source ：

ISSN： 1742-6588

Year： 2020

Issue： 1

Volume： 1682

Language： English

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ESI Highly Cited Papers on the List： 0 Unfold All

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30 Days PV： 1

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