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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
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.
作者姓名:唐伦  魏延南  谭颀  唐睿  陈前斌
作者单位:1.重庆邮电大学通信与信息工程学院移动通信技术重点实验室 重庆 4000652.重庆大学光电工程学院 重庆 400044
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)
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。

关 键 词:异构云无线接入网络    网络切片    资源分配    李雅普诺夫随机优化
收稿时间:2019-06-17

Joint Congestion Control and Resource Allocation Dynamic Scheduling Strategy for Network Slices in Heterogeneous Cloud Raido Access Network
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.
Authors:Lun TANG  Yannan WEI  Qi TAN  Rui TANG  Qianbin CHEN
Affiliation:1.Key Laboratory of Mobile Communication Technology, School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China2.School of Measurement and Control Technology and Instruments, Chongqing University, Chongqing 400044, China
Abstract:For online dynamic radio resources optimization for network slices in Heterogeneous Cloud Raido Access Network (H-CRAN), by comprehensively considering traffic admission control, congestion control, resource allocation and reuse, the problem is formulated as a stochastic optimization programming which maximizes network average total throughput subject to Base Station (BS) transmit power, system stability, Quality of Service (QoS) requirements of different slices and resource allocation constraints. Then, a joint congestion control and resource allocation dynamic scheduling algorithm is proposed which will dynamically allocate resources to users in network slices with distinct performance requirements within each resource scheduling time slot. The simulation results show that the proposed algorithm can improve the network overall throughput while satisfying the QoS requirement of each slice user and maintaining network stability. Besides, it could also flexibly strike a dynamic balance between delay and throughput by simply tuning an introduced control parameter.
Keywords:
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