密集网络下基于Self-Backhaul感知的用户接入负载均衡算法

doi:10.13190/j.jbupt.2017.04.010

北京邮电大学学报 ›› 2017, Vol. 40 ›› Issue (4): 60-67.doi: 10.13190/j.jbupt.2017.04.010

密集网络下基于Self-Backhaul感知的用户接入负载均衡算法

唐伦, 梁荣, 陈婉, 张元宝

重庆邮电大学移动通信重点实验室, 重庆 400065

收稿日期:2017-04-17 出版日期:2017-08-28 发布日期:2017-07-10
作者简介:唐伦(1973-),男,教授,博士,E-mail:tangl@cqupt.edu.cn.
基金资助:
国家自然科学基金项目（6157073）

User Association Load Balancing Algorithm Based on Self-Backhaul Aware in Dense Networks

TANG Lun, LIANG Rong, CHEN Wan, ZHANG Yuan-bao

Key Laboratory of Mobile Communication, Chongqing University of Posts and Telecommunication, Chongqing 400065, China

Received:2017-04-17 Online:2017-08-28 Published:2017-07-10

摘要/Abstract

摘要： 针对密集异构网络自回程场景中带宽分配不合理引起的负载不均衡问题，提出一种基于self-backhaul感知的用户接入负载均衡方案.首先根据密集异构网络下各个小基站接入与回程资源的负载状态提出一种用户接入负载均衡策略；其次利用Q-Learning算法对各个小基站带内无线接入与回程带宽分配进行学习，用户在不同带宽分配因子下，根据用户接入负载均衡策略进行重新接入，得到不同接入情况下的系统效用，进而得到最优带宽分配策略，保证负载均衡性的同时实现系统效用最大化.仿真结果表明，该方案在密集异构网络自回程场景中提高了网络负载均衡性，同时提升了用户速率体验.

关键词: 密集网络, 负载均衡, 自回程, Q学习

Abstract: In order to solve the problem of load imbalance caused by irrational bandwidth allocation in dense heterogeneous networks, a self-backhaul aware user access load balancing scheme was proposed. Firstly, a user association-load balancing strategy(UA-LBS) was described based on the load state of each small base station access and backhaul resource in dense heterogeneous network. Secondly, the Q-Learning algorithm was used to allocate wireless access and backhaul bandwidth in each small base station. For different allocation factors, it can ensure user to re-access according to the UA-LBS to get different system utility, and then to get optimal bandwidth allocation strategy to ensure load balancing while achieving system utility maximization. Simulation shows that the scheme improves the network load balancing in the self-backhaul scene of dense heterogeneous network, and improves the user rate experience.

Key words: dense network, load balance, self-backhaul, Q-learning

中图分类号:

TN929.5

唐伦, 梁荣, 陈婉, 张元宝. 密集网络下基于Self-Backhaul感知的用户接入负载均衡算法[J]. 北京邮电大学学报, 2017, 40(4): 60-67.

TANG Lun, LIANG Rong, CHEN Wan, ZHANG Yuan-bao. User Association Load Balancing Algorithm Based on Self-Backhaul Aware in Dense Networks[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2017, 40(4): 60-67.

参考文献

[1] Kamel M, Hamouda W, Youssef A. Ultra-dense networks:a survey[J]. IEEE Communications Surveys &Tutorials, 2016, 18(4):2522-2545.
[2] Yu Wei, Xu H S, Zhang H L, et al. Ultra-dense networks:survey of state of the art and future directions[C]//201625th International Conference on Computer Communication and Networks (ICCCN),[S. l.]:IEEE, 2016:1-10.
[3] Liu Erwu, Jin Shan, Shen Gang, et al. Fair scheduling in wireless multi-hop self-backhaul networks[C]//International Conference on Internet and Web Applications and Services/Advanced International Conference on Telecommunications (AICT-ICIW'06). Guadelope:IEEE, 2006:96-96.
[4] 曲桦, 庄雄, 赵季红, 等. 蜂窝网络下设备到设备通信中的联合资源优化[J]. 北京邮电大学学报, 2015, 38(3):112-116. Qu Hua, Zhuang Xiong, Zhao Jihong, et al. Jointresource optimization for equipment to equipment communication in cellular networks[J]. Journal of Beijing University of Posts and Telecommunications, 2015, 38(3):112-116.
[5] Wang N, Hossain E, Bhargava V K. Downlink cell association for large-scale MIMO HetNets employing small cell wireless backhaul[C]//2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE),.[S. l.]:IEEE, 2015:1042-1047.
[6] Siddique U, Tabassum H, Hossain E. Spectrum allocation for wireless backhauling of 5G small cells[C]//2016 IEEE International Conference on Communications Workshops (ICC),. Kuala:IEEE, 2016:122-127.
[7] Chen Lei, Yu F R, Ji Hong, et al. Green full-duplex self-backhaul and energy harvesting small cell networks with Massive MIMO[J]. IEEE Journal on Selected Areas in Communications, 2016, 34(12):3709-3724.
[8] 3GPP TS 36. 814, V9. 0. 0. Evolved universal terrestrial radio access (E-UTRA), Further advancements for E-UTRA physical layer aspects (Release 9).[s. l.]:3GPP Press, 2010.
[9] Hoydis J, Hosseini K, ten Brink S, and M. Debbah, "Making smart use of excess antennas:Massive MIMO, small cells, and TDD," Bell Labs Tech. J., vol.18, no.2, pp. 5-20, Sep. 2013.
[10] Bethanabhotla D, Bursalioglu O Y, Papadopoulos H C, and G. Caire, "User association and load balancing for cellular massive MIMO," in Proc. of ITA 2014, Feb. 2014, pp. 1-10.
[11] WATKINS C, DAYAN P. Technical note:Q-Learning[J]. Machine Learning, 1992, 8(3/4):279-292.

[1]	苏扬, 魏连锁, 郭媛. 多目标融合势博弈无线自组网络拓扑控制算法[J]. 北京邮电大学学报, 2022, 45(4): 91-97.
[2]	苏扬魏连锁郭媛. 多目标融合的序数势博弈无线自组网络拓扑控制算法[J]. 北京邮电大学学报, 2022, 45(4): 105-111.
[3]	康玲, 王翊, 胡艳军, 蒋芳, 李莉萍. 超密集网络中基于判别函数的聚类算法[J]. 北京邮电大学学报, 2022, 45(2): 104-109.
[4]	阎逸飞, 王智立, 邱雪松, 王嘉潞. Spark环境下基于数据倾斜模型的Shuffle分区优化方案[J]. 北京邮电大学学报, 2020, 43(2): 116-121.
[5]	刘保菊, 喻鹏, 丰雷, 邱雪松, 江昊. 电力SDN通信网中面向负载均衡的路由重构[J]. 北京邮电大学学报, 2020, 43(2): 16-22.
[6]	马庆刘, 喻鹏, 吴佳慧, 熊翱, 颜拥. 基于深度强化学习的综合能源业务通道优化机制[J]. 北京邮电大学学报, 2020, 43(2): 87-93.
[7]	王璇, 侯蓉晖, 徐伟琳. 低轨卫星网络动态路径切换技术[J]. 北京邮电大学学报, 2020, 43(2): 80-86,109.
[8]	王孟哲, 滕颖蕾, 宋梅, 韩丹涛, 张勇. 基于平均场博弈的超密集网络边缘缓存和删除分配研究[J]. 北京邮电大学学报, 2020, 43(2): 29-39.
[9]	田心记, 张浩天, 李晓静, 张长森. UDN中基于JR-CoMP的分簇和资源分配方案[J]. 北京邮电大学学报, 2019, 42(3): 72-78.
[10]	许焱平, 贾文杰, 李晓静, 张长森, 田心记. 超密集网络中一种改进的分簇及资源分配方案[J]. 北京邮电大学学报, 2019, 42(2): 77-82.
[11]	王亚鑫, 杨大成. 一种基于C-RAN的BBU负载均衡优化算法[J]. 北京邮电大学学报, 2018, 41(5): 115-119.
[12]	张海波, 李虎, 陈善学, 邹剑. 超密集网络中混合接入方式下基于分组的资源分配[J]. 北京邮电大学学报, 2018, 41(3): 69-74.
[13]	朱斌, 孙斌. 结合分类回归树和K近邻的负载均衡预测算法[J]. 北京邮电大学学报, 2017, 40(s1): 93-97.
[14]	王英杰, 刘晓宁, 罗桓桓, 周桂平, 王艳茹. TWDM-PON中用户流量预测的波长分配方案[J]. 北京邮电大学学报, 2017, 40(6): 74-79.
[15]	曲桦, 赵季红, 樊斌, 王密, 郭涯. 软件定义网络中应用蚁群优化的负载均衡算法[J]. 北京邮电大学学报, 2017, 40(3): 51-55.

密集网络下基于Self-Backhaul感知的用户接入负载均衡算法

User Association Load Balancing Algorithm Based on Self-Backhaul Aware in Dense Networks

PDF

可视化

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