基于分簇的小基站用户资源分配方案

doi:10.19678/j.issn.1000-3428.0050694

计算机工程 ›› 2019, Vol. 45 ›› Issue (5): 110-115. doi: 10.19678/j.issn.1000-3428.0050694

基于分簇的小基站用户资源分配方案

鲜永菊^a,郑健^a,徐昌彪^b

重庆邮电大学 a.通信与信息工程学院; b.光电工程学院,重庆 400065

收稿日期:2018-03-09 出版日期:2019-05-15 发布日期:2019-05-15
作者简介:鲜永菊(1973—),女,副教授、博士,主研方向为移动通信、未来网络;郑健,硕士研究生;徐昌彪,教授、博士。
基金资助:
国家自然科学基金“可靠性驱动的移动云资源管理随机模型和优化方法”(61602073)。

Clustering-based User Resource Allocation Scheme for Small Base Station

XIAN Yongju^a,ZHENG Jian^a,XU Changbiao^b

a.School of Communication and Information Engineering; b.School of Optoelectronics Engineering, Chongqing University of Posts and Telecommunications,Chongqing 400065,China

Received:2018-03-09 Online:2019-05-15 Published:2019-05-15

摘要/Abstract

摘要：

为降低密集网络系统层内干扰并提高系统容量,提出一种基于分簇的用户资源分配方案。在分簇阶段,根据小基站到簇头的最大距离方差与簇头间最小距离对初始分簇结果进行消除、合并及分裂操作,实现网络拓扑变化情况下的自适应分簇。在资源分配阶段,通过用户剩余资源有效利用及资源块微调,保障用户业务速率。仿真结果表明,与现有用户资源分配方案相比,该方案提高了系统容量及用户公平性。

关键词: 密集网络, 基站分簇, 层内干扰, 资源分配, 系统容量

Abstract:

In order to reduce the intra-layer interference in dense network systems and increase the system capacity,a clustering-based user resource allocation scheme is proposed.In the clustering stage,the initial clustering results are eliminated,merged and split according to the maximum distance variance from Small Base Station(SBS) to cluster-heads and the minimum distance between cluster-heads so as to realize adaptive clustering under the changing network topology.In the resource allocation stage,the user service rate is guaranteed by making use of user remaining resources and the fine adjustment of resource blocks.Simulation results show that compared with the existing user resource allocation scheme,the scheme improves system capacity and user fairness.

Key words: dense network, base station clustering, intra-layer interference, resource allocation, system capacity

中图分类号:

TN929.5

鲜永菊,郑健,徐昌彪. 基于分簇的小基站用户资源分配方案[J]. 计算机工程, 2019, 45(5): 110-115.

XIAN Yongju,ZHENG Jian,XU Changbiao. Clustering-based User Resource Allocation Scheme for Small Base Station[J]. Computer Engineering, 2019, 45(5): 110-115.

https://www.ecice06.com/CN/Y2019/V45/I5/110

参考文献

［1］CHANDRASEKHAR V,ANDREWS J G,GATHERER A.Femtocell networks:a survey［J］.IEEE Communications Magazine,2008,46(9):59-67.
［2］SAUNDERS S,CARLAW S,GIUSTINA A,et al.Femtocells:opportunities and challenges for business and technology［M］.New York,USA:John Wiley and Sons,2009.
［3］3GPP.Requirements for further advancements for evolved universal terrestrial radio access:ETSI TR 36.913 V8.0.1［R/OL］.［2018-02-17］.https://www.etsi.org/deliver/etsi_tr/136900_136999/136913/09.00.00_60/tr_136913v090000p.pdf.
［4］尤肖虎,潘志文,高西奇,等.5G移动通信发展趋势与若干关键技术［J］.中国科学:信息科学,2014,44(5):551-563.
［5］KAMEL M,HAMOUDA W,YOUSSEF A.Ultra-dense networks:a survey［J］.IEEE Communications Surveys and Tutorials,2017,18(4):2522-2545.
［6］ACHAR R,RAJESH A,NAKKEERAN R,et al.Heterogeneous resource allocation in LTE based femtocell networks［C］//Proceedings of International Conference on Circuit,Power and Computing Technologies.Washington D.C.,USA:IEEE Press,2015:1-6.
［7］VARGHESE A,SUDHA T.A resource allocation scheme for throughput maximization in OFDMA femtocell networks［C］//Proceedings of International Conference on Control Communication and Computing India.Washington D.C.,USA:IEEE Press,2016:407-412.
［8］SHEIKHZADEH S,MOKARI N,SAEEDI H.Radio resource allocation for interference management in OFDMA-based femtocell-macrocell deployment［C］//Proceedings of the 24th Iranian Conference on Electrical Engineering.Washington D.C.,USA:IEEE Press,2016:757-761.
［9］LIU Yuanfei,WANG Ying,ZHANG Yuan,et al.Game-theoretic hierarchical resource allocation in ultra-dense networks［C］//Proceedings of IEEE International Symposium on Personal,Indoor,and Mobile Radio Communications.Washington D.C.,USA:IEEE Press,2016:1-6.
［10］VU N H,LONG B L.Fair resource allocation for OFDMA femtocell networks with macrocell protection［J］.IEEE Transactions on Vehicular Technology,2014,63(3):1388-1401.
［11］王莹,缪中宇,张平.超高密度网络关键技术及研究现状［J］.北京邮电大学学报,2015,38(5):1-17.
［12］杨峰义,谢伟良,张建敏.5G无线网络及关键技术［J］.通信学报,2017,38(3):184-185.
［13］张海波,邹剑,刘开健,等.Femtocell网络中基于分簇的资源分配机制［J］.通信学报,2017,38(1):16-25.
［14］XU Liang,MAO Yuming,LENG Supeng.A cluster-based resource allocation strategy with energy harvesting in dense small-cell networks［C］//Proceedings of International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery.Washington D.C.,USA:IEEE Press,2017:303-310.
［15］ZHAO Caidan,XU Xiaofei,GAO Zhibin.A coloring-based cluster resource allocation for ultra dense network［C］//Proceedings of IEEE International Conference on Signal Processing,Communications and Computing.Washington D.C.,USA:IEEE Press,2016:1-5.
［16］LIN Shangjing,TIAN Hui.Clustering based interference management for QoS guarantees in OFDMA femtocell［C］//Proceedings of Wireless Communications and Networking Conference.Washington D.C.,USA:IEEE Press,2013:649-654.
［17］WEI Rong,WANG Ying,ZHANG Yuan.A two-stage cluster-based resource management scheme in ultra-dense networks［C］//Proceedings of IEEE/CIC International Conference on Communications.Washington D.C.,USA:IEEE Press,2015:738-742.
［18］贾瑞玉,宋建林.基于聚类中心优化的最佳聚类数确定方法［J］.微电子学与计算机,2016,33(5):62-66.

[1]	张俊娜, 李天泽, 赵晓焱, 袁培燕. 一种基于DQN的去中心化优先级卸载策略[J]. 计算机工程, 2024, 50(9): 235-245.
[2]	曾耀平, 夏玉婷, 江伟伟, 刘月强. 加权能耗最小化的无人机辅助移动边缘计算策略研究[J]. 计算机工程, 2024, 50(2): 288-297.
[3]	班玉琦, 段利国, 温昊宇, 李爱萍, 赵菊敏. 面向移动感知的计算卸载及资源分配策略研究[J]. 计算机工程, 2023, 49(8): 163-173.
[4]	梅晶, 戴龙宝, 童钊, 邓昕, 王嘉珂. 资源约束下基于Lyapunov优化的自适应卸载算法[J]. 计算机工程, 2023, 49(7): 34-46.
[5]	李强, 仪晋辉, 杜婷婷, 王胜春. 移动边缘计算中基于A3C的依赖任务卸载与资源分配[J]. 计算机工程, 2023, 49(6): 42-52.
[6]	郑丽萍, 赵玉娟, 费选. 基于改进MOEA/D的车联网通信资源分配算法[J]. 计算机工程, 2023, 49(5): 191-197.
[7]	杨立伟, 贾博宇, 王芳, 彭祥原. 可见光通信与WiFi异构网络资源管理算法[J]. 计算机工程, 2023, 49(3): 203-210,220.
[8]	郑振康, 周金和. 面向多租户网络资源分配的博弈优化策略[J]. 计算机工程, 2022, 48(5): 170-177.
[9]	宋勇春, 王茜竹, 高正念. 基于HAGA的D2D-NOMA资源分配优化算法[J]. 计算机工程, 2022, 48(2): 275-280,290.
[10]	刘振鹏, 郭超, 王仕磊, 陈杰, 李小菲. 基于博弈论和启发式算法的超密集网络边缘计算卸载[J]. 计算机工程, 2022, 48(12): 54-61,71.
[11]	于晶, 鲁凌云, 李翔. 车联网中基于DDQN的边云协作任务卸载机制[J]. 计算机工程, 2022, 48(12): 156-164.
[12]	曾蓉晖, 林兵, 王明芬, 林凯, 卢宇. 超密集边缘计算网络中面向能耗优化的任务卸载方法[J]. 计算机工程, 2022, 48(11): 39-48.
[13]	黄煜梵, 彭诺蘅, 林艳, 范建存, 张一晋, 余妍秋. 基于SAC强化学习的车联网频谱资源动态分配[J]. 计算机工程, 2021, 47(9): 34-43.
[14]	杨天, 杨军. MEC中卸载决策与资源分配的深度强化学习方法[J]. 计算机工程, 2021, 47(8): 37-44.
[15]	郑冰原, 孙彦赞, 吴雅婷, 王涛, 方勇. 基于DQN的超密集网络能效资源管理[J]. 计算机工程, 2021, 47(5): 169-175.

选择文件类型/文献管理软件名称

选择包含的内容

基于分簇的小基站用户资源分配方案

Clustering-based User Resource Allocation Scheme for Small Base Station

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

基于分簇的小基站用户资源分配方案

Clustering-based User Resource Allocation Scheme for Small Base Station

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价