摘要
To support dramatically increased traffic loads,communication networks become ultra-dense.Traditional cell association(CA)schemes are timeconsuming,forcing researchers to seek fast schemes.This paper proposes a deep Q-learning based scheme,whose main idea is to train a deep neural network(DNN)to calculate the Q values of all the state-action pairs and the cell holding the maximum Q value is associated.In the training stage,the intelligent agent continuously generates samples through the trial-anderror method to train the DNN until convergence.In the application stage,state vectors of all the users are inputted to the trained DNN to quickly obtain a satisfied CA result of a scenario with the same BS locations and user distribution.Simulations demonstrate that the proposed scheme provides satisfied CA results in a computational time several orders of magnitudes shorter than traditional schemes.Meanwhile,performance metrics,such as capacity and fairness,can be guaranteed.
基金
This work was supported by the Fundamental Research Funds for the Central Universities of China under grant no.PA2019GDQT0012
by National Natural Science Foundation of China(Grant No.61971176)
by the Applied Basic Research Program ofWuhan City,China,under grand 2017010201010117.
作者简介
Jinhua Pan was born in Chizhou,Anhui Province,China,in 1996.He received the B.E.degree in Communications Engineering from Yangzhou University(YZU),Yangzhou,China,in 2018.He is a master student in Communications Engineering Department at Hefei University of Technology(HFUT),Hefei,China.His research interests are resource management in hyper-dense and heterogeneous networks;corresponding author:Lusheng Wang received his B.Sc.in Communications Engineering in 2004 from Beijing University of Posts and Telecommunications(BUPT),China and his Ph.D.in 2010 in Computer Science and Networks from Telecom ParisTech(ENST),France.Currently,he is a research professor and the vice-dean of Communications Engineering Department at Hefei University of Technology(HFUT),China.His research interests are resource and interference management in hyper-dense and heterogeneous networks.email:lushengwang@hfut.edu.cn;Hai Lin received the B.S.degree from the Department of Thermal Engineering,Huazhong Sciences and Technologies University,Wuhan,China,in 1999,the M.S.degree in com-puter science from the University of Pierre and Marie Curie,Paris,France,in 2005,and the Ph.D.degree from the Institute Telecom–Telecom ParisTech,Paris,in 2008.He held Postdoctoral Research with France Telecom.He was a Researcher with ZTE Europe.Since 2012,he has been with Wuhan University,Wuhan,where he is currently an Associate Professor with the School of Cyber Science and Engineering.His research interests include the Internet of Things,edge computing,sensor networks,and future networks;Zhiheng Zha was born in Tongling,Anhui Province,China,in 1994.He received the B.E.degree in communication engineering from Qilu University of Technology(QLUT),Jinan,China,in 2017.He received the M.S.degree in Communications Engineering from Hefei University of Technology(HFUT),Hefei,China.His research interests are resource management in hyper-dense and heterogeneous networks;Caihong Kai received the B.S.degree from Hefei University of Technology,Hefei,China,in 2003,the M.S.degree in Electronic Engineering and Computer Science from University of Science and Technology of China,Hefei,China,in 2006,and the Ph.D.degree in Information Engineering from the Chinese University of Hong Kong,Hong Kong,China,in 2010,respectively.She is now a Professor of the School of Computer Science and Information Engineering,Hefei University of Technology.Her research interests are in wireless communication and networking,network protocols and performance evaluation.
