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學術報告(8月11日):能量收集系統中基于增強學習的多接入控制和電池預測

發布者:李芳菲發布時間:2020-08-10瀏覽次數:10

報告題目:能量收集系統中基于增強學習的多接入控制和電池預測

  

報告人:楚曼 博士

時間:20208月11日上午10:00-11:00

地址:  Zoom 會議, ID:665 6126 2304,密碼:123456

會議鏈接https://cuhksz.zoom.com.cn/j/66561262304?pwd=NG4xN2lDRnh6czVNUENRcXZCSk41UT09

報告人單位:香港中文大學

主辦單位:信控學院

報告摘要:

Energy harvesting (EH) is a promising technique to fulfill the long-termand self-sustainable operations for Internet of things (IoT) systems.In this paper, we study the joint access control and battery predictionproblems in a small-cell IoT system including multiple EHuser equipments (UEs) and one base station (BS) with limited uplink accesschannels.Each UE has a rechargeable battery with finite capacity.The system control is modeled as a Markov decision process without complete priorknowledge assumed at the BS, which also deals with large sizes in both state andaction spaces.First, to handle the access control problem assuming causal battery and channelstate information,we propose a scheduling algorithm that maximizes the uplink transmissionsum rate based on reinforcement learning (RL) withdeep Q-network (DQN) enhancement.Second, for the battery prediction problem,with a fixed round-robin access control policy adopted,we develop a RL based algorithm to minimize the prediction loss(error) without any model knowledge about the energy source and energy arrivalprocess.Finally, the joint access control and battery prediction problem isinvestigated, where we propose a two-layer RLnetwork to simultaneously deal with maximizing the sum rate and minimizingthe prediction loss: the first layer is for battery prediction,the second layer generates the access policy based on the output from the firstlayer.Experiment results show that the three proposed RL algorithmscan achieve better performances compared with existing benchmarks.

報告人簡介:楚曼博士,分別于2011年和2014年獲得西安交通大學學士和碩士學位。2015年-2016年在香港中文大學Professor Vincent Lau團隊進行訪問上海十一选五开奖结果|官网首页上海十一选五开奖结果|官网首页;2016-2018年在加州大學戴維斯分校Professor Shuguang Cui團隊進行訪問; 2019年獲得西安交通大學博士學位。目前是香港中文大學(深圳)理工學院的博士后/助理研究員。

研究領域: Deep Learning, Federated Learning, 能量收集,無線資源優化等上海十一选五开奖结果|官网首页。

 

版權所有:中國礦業大學信息與控制工程學院  地址:江蘇省徐州市大學路1號中國礦業大學南湖校區信息與控制工程學院

上海十一选五开奖结果|官网首页
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