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作 者:WANG Chaowei WANG Ziye XU Lexi YU Xiaofei ZHANG Zhi WANG Weidong
机构地区:[1]School of Electronic Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China [2]Shaanxi Key Laboratory of Information Communication Network and Security,Xi’an University of Posts and Telecommunications,Xi’an 710121,China [3]Research Institute,China United Network Communications Corporation,Beijing 100048,China [4]School of Information and Communication Engineering,Beijing University of Posts and Telecommunications,Beijing 100876,China
出 处:《Chinese Journal of Electronics》2023年第6期1218-1229,共12页电子学报(英文版)
基 金:supported by the National Key R&D Program of China(2020YFB1807204).
摘 要:The 6G mobile communications demand lower content delivery latency and higher quality of service for vehicular edge network.With the popularity of content-centric networks,mobile users are paying more and more attention to the delay and reliability of fetching cached content.For reducing communication costs,increasing network capacity and improving the content delivery,we propose a collaborative caching scheme based on deep reinforcement learning for vehicular edge network assisted by cell-free massive multiple-input multipleoutput(MIMO)system,in which the macro base station is considered as the central processor unit,and the roadside units are treated as roadside access points(RSAPs).The proposed scheme can effectively cache contents in edge nodes,i.e.,RSAPs and vehicles with caching capability.We jointly consider the mobility of vehicles and the content request preferences of users,then we use deep Qnetworks algorithm to optimize the caching decisions.Simulation results show that the proposed scheme can significantly reduce the content delivery average latency and increase the content cache hit ratio.
关 键 词:Vehicular edge network Cell-free massive MIMO Collaborative caching Content delivery latency Deep reinforcement learning Cache hit ratio
分 类 号:TN929.5[电子电信—通信与信息系统] U463.6[电子电信—信息与通信工程]
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