Towards V2I Age-Aware Fairness Access:A DQN Based Intelligent Vehicular Node Training and Test Method

查看全文 作　　者：WU [1,2]Qiong;SHI [1]Shuai;WAN [1]Ziyang;FAN [3]Qiang;FAN [4]Pingyi;ZHANG [5]Cui

机构地区：[1]School of Internet of Things Engineering,Jiangnan University,Wuxi 214122,China;[2]State Key Laboratory of Integrated Services Networks,Xidian University,Xi’an 710071,China;[3]Qualcomm,San Jose CA 95110,USA;[4]Beijing National Research Center for Information Science and Technology,Tsinghua University,Beijing 100084,China;[5]Banma Network Technology Co.,Ltd.,Shanghai 200000,China

出　　处：《Chinese Journal of Electronics》索引2023年第32卷第6期,共15页

基　　金：supported by the National Natural Science Foundation of China(61701197);the Open Research Fund of State Key Laboratory of Integrated Services Networks(ISN23-11);the National Key Research and Development Program of China(2021YFA1000500(4));the 111 Project(B12018). 摘　　要：Vehicles on the road exchange data with base station frequently through vehicle to infrastructure(V2I)communications to ensure the normal use of vehicular applications,where the IEEE 802.11 distributed coordination function is employed to allocate a minimum contention window(MCW)for channel access.Each vehicle may change its MCW to achieve more access opportunities at the expense of others,which results in unfair communication performance.Moreover,the key access parameter MCW is privacy information and each vehicle is not willing to share it with other vehicles.In this uncertain setting,age of information(AoI),which measures the freshness of data and is closely related with fairness,has become an important communication metric.On this basis,we design an intelligent vehicular node to learn the dynamic environment and predict the optimal MCW,which can make the intelligent node achieve age fairness.In order to allocate the optimal MCW for the vehicular node,we employ a learning algorithm to make a desirable decision by learning from replay history data.In particular,the algorithm is proposed by extending the traditional deep-Q-learning(DQN)training and testing method.Finally,by comparing with other methods,it is proved that the proposed DQN method can significantly improve the age fairness of the intelligent node. 关键词：VEHICLES FAIR Chennel access Age of information(AoI) Deep-Q-learning

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Towards V2I Age-Aware Fairness Access:A DQN Based Intelligent Vehicular Node Training and Test Method

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