超密集组网中基于上行容量分析的增强型动态分簇算法
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清华大学,重庆邮电大学,清华大学,清华大学

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R730.58

基金项目:

国家高技术研究发展计划(863计划);国家自然科学基金项目(面上项目,重点项目,重大项目),北京市科委计划,港澳台科技合作专项


An Improved Dynamic Clustering Algorithm Based on Uplink Capacity Analysis in Ultra-Dense Network System
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Tsinghua University,Chongqing University of Posts and Telecommunications,,

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The National High Technology Research and Development Program of China (863 Program);The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan); Science and Technology Program of Beijing; Scientific and Technological Cooperation Projects

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    摘要:

    超密集组网(Ultra-Dense Network,UDN)是未来5G(5th Generation)网络的一个关键技术。UDN拥有更小的小区半径,是一个新型的网络架构。UDN的核心概念是在热点地区部署低功率基站。由于UDN小区密度的增加,UDN中的干扰问题比宏微异构网络中干扰问题更加严峻。分簇合作方法可以降低干扰。本文首先推导出系统上行容量表达式,然后提出一种新颖的动态分簇算法。在小区密集部署的网络中,此算法在系统性能和复杂度之间做出了很好的权衡,同时降低了移动台之间的干扰。仿真结果显示本文提出的方法与一些已提出来的分簇方法相比有很大的容量增益。

    Abstract:

    The Ultra-Dense Network (UDN) is a key technology in the future 5th Generation (5G) networks. UDN is a new network structure, which has a smaller cell radius. Deploying the Low Power Base Stations (LPBSs) is the core concept of UDN. Because of the increasing of cell density, the interference is more serious in UDN than in macro and micro heterogeneous scenario. It is known that clustering cooperation can decrease the interference. Firstly, the total uplink capacity expression is derived in the paper. Then a novel dynamic clustering algorithm is presented. In densely deployed small cell network, the algorithm can make a better tradeoff between the complexity and the system performance. And the inter-Mobile Station (MS) interference can be dereased. The significant capacity gain is shown in the simulation results and is better than these clustering algorithms which have been proposed.

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引用本文

曾捷,张琪,粟欣,等. 超密集组网中基于上行容量分析的增强型动态分簇算法[J]. 科学技术与工程, 2017, 17(18): .
zengjie, zhangqi,,et al. An Improved Dynamic Clustering Algorithm Based on Uplink Capacity Analysis in Ultra-Dense Network System[J]. Science Technology and Engineering,2017,17(18).

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  • 收稿日期:2016-12-10
  • 最后修改日期:2017-01-17
  • 录用日期:2017-02-20
  • 在线发布日期: 2017-06-29
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