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基于聚类算法家庭基站的节能方法 被引量:1

ENERGY SAVING METHOD BASED ON CLUSTERING ALGORITHM IN HENB
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摘要 随着移动通信系统的快速发展,整个通信行业的年耗电量急剧上升,这不仅给运营商带来了较大的运营成本负担,也给环境带来了污染。因此,针对家庭基站资源浪费的问题,提出一种适用于家庭基站网络的基于聚类算法的节能机制。在家庭基站密集部署的环境中,通过对活动家庭基站进行聚类的方法,可以将网络中不必要的家庭基站进行休眠,从而有效地实现网络节能。通过对不同因素下的家庭基站用户(HUE)的服务质量进行仿真,结果表明,这种新的节能机制不仅提高了HUE的吞吐量,而且提高了系统的容量和节省了能源消耗。 With the rapid development of mobile communication system, the power consumption in whole communications industry grows sharply, this not only brings larger operational cost burden to operators, but also brings pollution to the environment. Therefore, this paper presents a clustering algorithm-based energy saving mechanism suitable for Home eNode B (HeNB) networks aiming at its resources wasting problem. In the environment of dense HeNBs deployment, the method will turn the unnecessary active HeNBs to sleep mode through applying the clustering algorithm on HeNBs, so as to effectively implement networks energy saving. Through simulating the quality of service (QoS) of HeNB users (HUE) under different factors, the results show that the new mechanism improves the throughput of HUE, and also increases the capacity of the system and saves the energy consumption as well.
出处 《计算机应用与软件》 CSCD 2015年第11期103-106,175,共5页 Computer Applications and Software
基金 国家自然科学基金项目(41172297) 国家十一五科技支持计划项目(2006BAB01B07) 湖南省教育厅科学研究项目(14C0063)
关键词 家庭基站 聚类算法 休眠模式 节能 HeNB Clustering algorithm Sleep mode Energy saving
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参考文献12

  • 1Liu D, Wang W, Guo W. ' Green ' Cooperative Spectrum Sharing Communication [ J ]. IEEE communications letters, 2013,17 ( 3 ) : 459 - 462.
  • 2Nakamura T, Nagata S, Benjebbour A, et al. Trends in small cell en- hancements in LTE advanced [ J ]. Communications Magazine, IEEE, 2013,51 (2) :98 - 105.
  • 3Andrews J, Claussen H, Dohler M, et al. Femtocells : Past, Present, and Future [ J ]. IEEE Journal on Selected Areas in Communications ,2012, 30(3) :497 -508.
  • 4Li Y, Jia Y, Wang Y, et al. Collaborative Sleeping Scheme for Femto- cell Networks [ C ]//Green Computing and Communications (Green- Com), 2013 IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing. IEEE,2013:142 - 147.
  • 5Li Y Y, Yen L, Sousa E S. Hybrid user access control in HSDPA fem- tocells [ C ]//GLOBECOM Workshops ( GC Wkshps ), 2010 IEEE. IEEE, 2010:679 - 683.
  • 6Pan Z, Shimamoto S. Cell sizing based energy optimization in joint macro-femto deployments via sleep activation [ C ]//Wireless Commu- nications and Networking Conference (WCNC) , 2013 IEEE. IEEE, 2013:4765 - 4770.
  • 7Yoon S G, Han J, Bahk S. Low-duty mode operation of femto base sta- tions in a densely deployed network environment[ C ]//Personal Indoor and Mobile Radio Communications (PIMRC), 2012 IEEE 23rd Inter- national Symposium on. IEEE, 2012:636 - 641.
  • 8Andrews J G, Claussen H, Dohler M, et al. Femtocells: Past, pres- ent, and future [ J ]. Selected Areas in Communications, IEEE Journal on ,2012,30 (3) :497 - 508.
  • 9Zhang J, Hong P, Xue K, et al. A novel power control scheme for femtocell in heterogeneous networks[ C]//Consumer Communications and Networking Conference (CCNC) , 2012 IEEE. IEEE,2012:802- 806.
  • 10周涓,熊忠阳,张玉芳,任芳.基于最大最小距离法的多中心聚类算法[J].计算机应用,2006,26(6):1425-1427. 被引量:71

二级参考文献10

  • 1HanJ KamberM.数据挖掘概念与技术[M].北京:机械工业出版社,2002..
  • 2KUMAR M, ORLIN JB, PATEL NR. Clustering data with measurement errors[ R]. Technical Report, RRR 12 - 2005, New Jersey:RUTCOR, Rutgers Center for Operations Researeh, 2005.
  • 3SU MC. A modified version for k-means[ J]. IEEE Transactions onPattern Analysis and Machine Intelligence, 2001, 23 (6) : 674 -680.
  • 4FAYYAD U, REINA C, BRADLEY PS. Initialization of interative refinement clustering algorithms[ A]. Proceedings of Fourth International Conference on Knowledge Discovery and Data Mining[ C].Menlo Park: AAAI Press, 1998. 194 - 198.
  • 5CHAUDHURI D, CHAUDHURI BB. A novel muhiseed nonhierarchical data clustering technique[ J]. IEEE Transactions on Systems,Man and Cybernetics: PartB, 1997, 27(5) : 871 - 877.
  • 6Kurniawan A, Benech N, Tao Yufei. Towards High-dimensional Clustering [ J ]. COMP, November 1999 : 1-2.
  • 7MacQueen J. Some Methods for Classification and Analysis of Multivariate Observations [ J ]. In: Proceedings of 5th Berkeley Syrup. Math. Statist,Prob. ,1967,1:281-297.
  • 8Jolla L. Alternatives to the k-means algorithm that find better clustering [ J ]. In : Proceeding of ACM SIGMOD, 1992: 192-195.
  • 9Schrimpf. Migration of Processes, files and virtual devices in the MDX operating system[ J ]. ACM SIGOPS, 1995:70-81.
  • 10张春阳,周继恩,钱权,蔡庆生.抽样在数据挖掘中的应用研究[J].计算机科学,2004,31(2):126-128. 被引量:11

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