计算机科学 ›› 2018, Vol. 45 ›› Issue (6A): 279-282.

• 网络与通信 • 上一篇    下一篇

超密集网络中子信道和功率分配研究

谭博文,王纲,姚稳   

  1. 重庆邮电大学通信与信息工程学院 重庆400065
  • 出版日期:2018-06-20 发布日期:2018-08-03
  • 作者简介:谭博文(1993-),男,硕士生,主要研究方向为超密集网络资源分配,E-mail:296159082@qq.com(通信作者)。
  • 基金资助:
    国家科技重大专项基金(2016ZX03002010-003)资助

Study of Sub-channel and Power Allocation in Ultra-dense Networks

TAN Bo-wen,WANG Gang,YAO Wen   

  1. Department of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Online:2018-06-20 Published:2018-08-03

摘要: 超密集网络中,严重的小区间干扰制约了终端用户的数据速率,针对该问题,提出一种基于簇优先级的资源分配方案。该方案分为3个步骤:首先,采用基于图论的染色算法为毫微微接入点(Femtocell Access Points,FAPs)分簇;然后,以簇内每个毫微微用户(Femtocell User Equipments,FUEs)的待发送数据量、排队等待时延以及受干扰强度等作为优先级,计算每个簇的优先级,高优先级的簇可最先获得信道增益好的子信道;最后,利用卡罗需-库恩-塔克(Karush-Kuhn-Tucker,KKT)条件和注水算法为FUEs分配功率。仿真实验表明该方案能够有效地减小Femtocell间的干扰,并能够极大地满足用户的需求,同时提升系统的吞吐量和频谱效率。

关键词: 超密集网络, 分簇, 毫微微小区, 资源分配

Abstract: In ultra-dense network,the serious inter-cell interference has restricted the data rate of users.In view of this problem,a new priority-based clustering resource allocation scheme was proposed in this paper.The scheme is divided into three steps.In the first step,it uses graph-based coloring algorithm for femtocell access points (FAPs).In the se-cond step,it uses number of data to be transmitted,the queuing delay and the interference intensity of each femtocell user equipment (FUEs) in the cluster as the priority,and then calculates the priority of each cluster,for examples,clusters with high priority can receive sub-channel of good channel gain first.At last,it allocates power for FUEs by Karush-Kuhn-Tucker(KKT) and Water-Filling fashion.Simulation results show that the proposed scheme can effectively reduce the interference between femtocell,and can greatly satisfy the needs of users,while improving the throughput and spectrum efficiency.

Key words: Clustering, Femtocell, Resource allocation, Ultra-dense network

中图分类号: 

  • TN929.5
[1]WANG C X,HAIDER F,GAO X Q,et al.Cellular architectureand key technologies for 5G wireless communication networks[J].IEEE Communication Magazine,2014,52(2):122-130.
[2]HAO P,YAN X,RUYUE Y N,et al.Ultra dense Network: Challenges,Enabling Technologies and New Trends[J].China Communications,2016,13(2):30-40.
[3]KAMEL M,HAMOUDA W,YOUSSEF A.Ultra-dense Net- works:A Survey[J].IEEE Communications Surveys & Tuto-rials,2016,18(4):2522-2545.
[4]WANG Z,ZHU X R,BAO X,et al.A Novel Resource Allocation Method in Ultra-dense Network Based on Noncooperation Game Theory[J].China Communications,2016,13(10):169-180.
[5]LIU L,GARCIA V,TIAN L,et al.Joint clustering and inter-cell resource allocation for CoMP in ultra dense cellular networks[C]∥International Conference on Communications.IEEE,2015:2560-2564.
[6]HUA C,LUO Y,LIU H.Wireless Backhaul Resource Alloca- tion and User-centric Clustering in Ultra-dense Wireless Networks[J].IET Communications,2016,10(15):1858-1864.
[7]ZHANG Q,ZHU X,WU L,et al.A coloring-based resource allocation for OFDMA femtocell networks[C]∥Wireless Communications and Networking Conference.IEEE,2013:673-678.
[8]LIU T,YANG C,YANG L L.A low-complexity subcarrier- power allocation scheme for frequency-division multiple-access systems[J].IEEE Transactions on Wireless Communications,2010,9(5):1564-1570.
[9]LOPEZ-PEREZ D,CHU X,VASILAKOS A V,et al.Power Minimization Based Resource Allocation for Interference Mitigation in OFDMA Femtocell Networks[J].IEEE Journal on Selected Areas in Communications,2014,32(2):333-344.
[10]UYGUNGELEN S,AUER G,BHARUCHA Z.Graph-Based Dynamic Frequency Reuse in Femtocell Networks[C]∥73rd Vehicular Technology Conference (VTC Spring).IEEE,2011:1-6.
[11]LUAN Z R.User-Oriented Graph Based Frequency Allocation Algorithm for Densely Deployed Femtocell Network[J].China Communications,2013,10(12):57-65.
[12]ZHAO C D,XU X F,GAO Z B,et al.A Coloring-based Cluster Resource Allocation for Ultra Dense Network[C]∥Internatio-nal Conference on Signal Processing,Communications and Computing (ICSPCC).IEEE,2016:1-5.
[13]解可新,韩健,林友联.最优化方法[M].天津:天津大学出版社,1997:125-135.
[14]HATOUM A,LANGAR R,AITSAADI N,et al.Cluster-Based Resource Management in OFDMA Femtocell Networks With QoSGuarantees[J].IEEE Transactions on Vehicular Technology,2014,63(5):2378-2391.
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