高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

H-CRAN网络下联合拥塞控制和资源分配的网络切片动态资源调度策略

唐伦 魏延南 谭颀 唐睿 陈前斌

唐伦, 魏延南, 谭颀, 唐睿, 陈前斌. H-CRAN网络下联合拥塞控制和资源分配的网络切片动态资源调度策略[J]. 电子与信息学报, 2020, 42(5): 1244-1252. doi: 10.11999/JEIT190439
引用本文: 唐伦, 魏延南, 谭颀, 唐睿, 陈前斌. H-CRAN网络下联合拥塞控制和资源分配的网络切片动态资源调度策略[J]. 电子与信息学报, 2020, 42(5): 1244-1252. doi: 10.11999/JEIT190439
Lun TANG, Yannan WEI, Qi TAN, Rui TANG, Qianbin CHEN. Joint Congestion Control and Resource Allocation Dynamic Scheduling Strategy for Network Slices in Heterogeneous Cloud Raido Access Network[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1244-1252. doi: 10.11999/JEIT190439
Citation: Lun TANG, Yannan WEI, Qi TAN, Rui TANG, Qianbin CHEN. Joint Congestion Control and Resource Allocation Dynamic Scheduling Strategy for Network Slices in Heterogeneous Cloud Raido Access Network[J]. Journal of Electronics & Information Technology, 2020, 42(5): 1244-1252. doi: 10.11999/JEIT190439

H-CRAN网络下联合拥塞控制和资源分配的网络切片动态资源调度策略

doi: 10.11999/JEIT190439
基金项目: 国家自然科学基金(61571073),重庆市教委科学技术研究项目(KJZD-M201800601)
详细信息
    作者简介:

    唐伦:男,1973年生,教授,研究方向为下一代无线通信网络、异构蜂窝网络、软件定义无线网络等

    魏延南:男,1995年生,硕士生,研究方向为5G网络切片、虚拟资源分配、随机优化理论

    谭颀:女,1995年生,硕士生,研究方向为5G网络切片、资源分配、随机优化理论

    陈前斌:男,1967年生,教授,博士生导师,研究方向为个人通信、多媒体信息处理与传输、异构蜂窝网络等

    通讯作者:

    魏延南 weiyannan_cqupt@163.com

  • 中图分类号: TN929.5

Joint Congestion Control and Resource Allocation Dynamic Scheduling Strategy for Network Slices in Heterogeneous Cloud Raido Access Network

Funds: The National Natural Science Foundation of China (61571073), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201800601)
  • 摘要:

    针对异构云无线接入网络(H-CRAN)网络下基于网络切片的在线无线资源动态优化问题,该文通过综合考虑业务接入控制、拥塞控制、资源分配和复用,建立一个以最大化网络平均和吞吐量为目标,受限于基站(BS)发射功率、系统稳定性、不同切片的服务质量(QoS)需求和资源分配等约束的随机优化模型,并进而提出了一种联合拥塞控制和资源分配的网络切片动态资源调度算法。该算法会在每个资源调度时隙内动态地为性能需求各异的网络切片中的用户分配资源。仿真结果表明,该文算法能在满足各切片用户QoS需求和维持网络稳定的基础上,提升网络整体吞吐量,并且还可通过调整控制参量的取值实现时延和吞吐量间的动态平衡。

  • 图  1  基于网络切片的H-CRAN下行传输场景

    图  2  平均和吞吐量与控制参量V

    图  3  平均队列时延与控制参量V

    图  4  平均和速率与平均业务到达率$\lambda $

    图  5  平均队列时延与平均业务到达率$\lambda $

    表  1  H-CRAN网络下联合拥塞控制和资源分配的网络切片动态资源调度算法

     (1) 初始化控制参量$V > 0$、各用户的初始队列长度${Q_u}(0),\forall u \in {\cal{U}}$和最大时隙数${T^{\max }}$初始化最大迭代次数$T_0^{\max }$和允许误差$\delta $
     (2) for $t = 0,1, ··· ,{T^{\max } } - 1$
     (3) 根据式(24)分别计算各用户当前时隙最优的流量接入控制策略
     (4) Repeat:
     (5) 令迭代索引$n = 1$,初始化拉格朗日乘子${{\lambda}} $, ${{\eta }}$和${{\mu}} $
     (6) for $s \in {\cal{S}}$
     (7)  计算子载波$s$当前时隙(近似)最优的子载波复用、分配和功率分配策略${\alpha _s}^*$, ${{\beta}} _s^*$和${{{P}}_s}^*$,进而更新各用户剩余的排队队列长度
     (8)  若某用户$u \in {\cal{U}}$已经获得了足够的子载波(即其队列长度为0),则将其从接下来的子载波分配过程中排除。
     (9)  若所有用户均分配到足够的子载波,则break
     (10) end for
     (11) 根据得到的(近似)最优子载波复用、分配和功率分配策略${\alpha ^*}$, ${\beta ^*}$和${P^*}$计算拉格朗日函数${\cal{L}}{\left( {\alpha ,\beta ,P,\lambda ,\eta ,\mu } \right)^{(n)}}$
     (12) Until$\left| { {\cal{L} }{ {\left( {\alpha ,\beta ,P,\lambda ,\eta ,\mu } \right)}^{(n)} } - {\cal{L} }{ {\left( {\alpha ,\beta ,P,\lambda ,\eta ,\mu } \right)}^{(n - 1)} } } \right| \le \delta $ or $n > T_0^{\max }$, then stop Otherwise, 利用次梯度法更新拉格朗日乘子$\lambda $,
    $\eta $和$\mu $,令$n = n + 1$并返回第6步
     (13) 根据式(17)更新各用户在下一时隙的业务队列长度
     (14) end for
     (15) 输出:(近似)最优流量接入控制、子载波复用和分配以及功率分配策略$r$, $\alpha $, $\beta $和$P$,${Q_u}(t),\forall u \in {\cal{U}},t$。
    下载: 导出CSV
  • Cisco System. Cisco visual networking index: Global mobile data traffic forecast update, 2017–2022 White Paper[R/OL]. https://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/white-paper-c11-738429.html, 2019.
    LI Xin, SAMAKA M, CHAN H A, et al. Network slicing for 5G: challenges and opportunities[J]. IEEE Internet Computing, 2017, 21(5): 20–27. doi: 10.1109/MIC.2017.3481355
    ITU-R. IMT vision-framework and overall objectives of the future development of IMT for 2020 and beyond[EB/OL]. http://www.itu.int/pub/R-REC/en. 2020.
    LI Jian, PENG Mugen, YU Yuling, et al. Energy-efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks[J]. IEEE Transactions on Vehicular Technology, 2016, 65(12): 9873–9887. doi: 10.1109/TVT.2016.2531184
    ZHANG Haijun, WANG Baobao, JIANG Chunxiao, et al. Energy efficient dynamic resource optimization in NOMA system[J]. IEEE Transactions on Wireless Communications, 2018, 17(9): 5671–5683. doi: 10.1109/TWC.2018.2844359
    DANG Tian and PENG Mugen. Delay-aware radio resource allocation optimization for network slicing in fog radio access networks[C]. 2018 IEEE International Conference on Communications Workshops, Kansas City, USA, 2018: 1–6. doi: 10.1109/ICCW.2018.8403717.
    AMANI N, PEDRAM H, TAHERI H, et al. Energy-efficient resource allocation in heterogeneous cloud radio access networks via BBU offloading[J]. IEEE Transactions on Vehicular Technology, 2019, 68(2): 1365–1377. doi: 10.1109/TVT.2018.2882466
    KIM T and CHANG J M. Profitable and energy-efficient resource optimization for heterogeneous cloud-based radio access networks[J]. IEEE Access, 2019, 7: 34719–34737. doi: 10.1109/ACCESS.2019.2904766
    ZHANG Biling, MAO Xingwang, YU J L, et al. Resource allocation for 5G heterogeneous cloud radio access networks with D2D communication: a matching and coalition approach[J]. IEEE Transactions on Vehicular Technology, 2018, 67(7): 5883–5894. doi: 10.1109/TVT.2018.2802900
    唐伦, 魏延南, 马润琳, 等. 虚拟化云无线接入网络下基于在线学习的网络切片虚拟资源分配算法[J]. 电子与信息学报, 2019, 41(7): 1533–1539. doi: 10.11999/JEIT180771

    TANG Lun, WEI Yannan, MA Runlin, et al. Online learning-based virtual resource allocation for network slicing in virtualized cloud radio access network[J]. Journal of Electronics &Information Technology, 2019, 41(7): 1533–1539. doi: 10.11999/JEIT180771
    MEI Jie, ZHENG Kan, ZHAO Long, et al. A latency and reliability guaranteed resource allocation scheme for LTE V2V communication systems[J]. IEEE Transactions on Wireless Communications, 2018, 17(6): 3850–3860. doi: 10.1109/TWC.2018.2816942
    NEELY M J. Stochastic network optimization with application to communication and queueing systems[J]. Synthesis Lectures on Communication Networks, 2010, 3(1): 15–62. doi: 10.2200/S00271ED1V01Y201006CNT007
    MOKDAD A, AZMI P, MOKARI N, et al. Cross-layer energy efficient resource allocation in PD-NOMA based H-CRANs: implementation via GPU[J]. IEEE Transactions on Mobile Computing, 2019, 18(6): 1246–1259. doi: 10.1109/TMC.2018.2860985
    TANG Liya, ZHANG Xian, XIANG Hongyu, et al. Joint resource allocation and caching placement for network slicing in fog radio access networks[C]. The 18th International Workshop on Signal Processing Advances in Wireless Communications, Sapporo, Japan, 2017: 1–6. doi: 10.1109/SPAWC.2017.8227791.
    LEE Y L, LOO J, CHUAH T C, et al. Dynamic network slicing for multitenant heterogeneous cloud radio access networks[J]. IEEE Transactions on Wireless Communications, 2018, 17(4): 2146–2161. doi: 10.1109/TWC.2017.2789294
    TANG Lun, YANG Xixi, WU Xiaolin, et al. Queue stability-based virtual resource allocation for virtualized wireless networks with self-backhauls[J]. IEEE Access, 2018, 6: 13604–13616. doi: 10.1109/ACCESS.2018.2797088
  • 加载中
图(5) / 表(1)
计量
  • 文章访问数:  2412
  • HTML全文浏览量:  684
  • PDF下载量:  78
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-06-17
  • 修回日期:  2020-01-03
  • 网络出版日期:  2020-01-11
  • 刊出日期:  2020-06-04

目录

    /

    返回文章
    返回