计算机技术与发展2017，Vol.27Issue(5)：92-96,5.DOI:10.3969/j.issn.1673-629X.2017.05.020
自适应簇和学习算法的调度策略
Dispatching Strategy with Adaptive Clustering and Learning Algorithm
摘要
Abstract
With the development of mobile communication networks,enhancing peak rate and throughput performance for users has always been a very important but challenging issue.In a dense cellular network,a dispatching strategy which is based on user-centric adaptive clustering and a learning algorithm has been studied.A large and dense cellular network has been considered which has been modeled by a random network where the BSs' and UEs' locations are placed randomly,following Poisson Point Process (PPP) distributions.An adaptive clustering algorithm for user-centric has been described,which means generating a cell for each user,and all base stations within the district can provide services for user,it is proposed also to maximize each user normalized effective throughput.SBS are assumed to possess high storage capacity and to form a distributed caching network.Popular files are stored in local cache of SBS in its vicinity.The popularity profile of cached content is unknown and estimated using instantaneous demands from users within a specified time interval.When a user goes to communication,the effective throughput of normalization with user-centric adaptive cluster can be found.If the value is greater than the threshold,the network system will select adaptive communication;otherwise select the most use of excellent small base station to communicate with learning method.This novel network architecture can provide users with personalized network service,and enhance the peak rate and throughput performance for users.关键词
以用户为中心/自适应簇/学习算法/泊松点过程Key words
user-centric/adaptive clustering/learning algorithm/Poisson Point Processes (PPP)分类
信息技术与安全科学引用本文复制引用
孙君,谷苏文..自适应簇和学习算法的调度策略[J].计算机技术与发展,2017,27(5):92-96,5.基金项目
国家自然科学基金资助项目(61271236) （61271236）
