魏延南,,
马润琳,
贺小雨,
陈前斌
1.重庆邮电大学通信与信息工程学院? ?重庆? ?400065
2.重庆邮电大学移动通信技术重点实验室? ?重庆? ?400065
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，主要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
马润琳：女，1993年生，硕士生，研究方向为5G网络切片、网络功能虚拟化、无线资源分配
贺小雨：女，1995年生，硕士生，研究方向为5G网络切片、无线网络虚拟化、智能优化理论
陈前斌：男，1967年生，教授，博士生导师，主要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计量
文章访问数:1986
HTML全文浏览量:844
PDF下载量:105
被引次数:0
出版历程
收稿日期:2018-08-03
修回日期:2019-02-20
网络出版日期:2019-03-19
刊出日期:2019-07-01
Online Learning-based Virtual Resource Allocation for Network Slicing in Virtualized Cloud Radio Access Network
Lun TANG,Yannan WEI,,
Runlin MA,
Xiaoyu HE,
Qianbin CHEN
1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2. Key Laboratory of Mobile Communication Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Funds:The National Natural Science Foundation of China (61571073), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201800601)
摘要
摘要:针对现有研究中缺乏云无线接入网络(C-RAN)场景下对网络切片高效的动态资源分配方案的问题，该文提出一种虚拟化C-RAN网络下的网络切片虚拟资源分配算法。首先基于受限马尔可夫决策过程(CMDP)理论建立了一个虚拟化C-RAN场景下的随机优化模型，该模型以最大化平均切片和速率为目标，同时受限于各切片平均时延约束以及网络平均回传链路带宽消耗约束。其次，为了克服CMDP优化问题中难以准确掌握系统状态转移概率的问题，引入决策后状态(PDS)的概念，将其作为一种“中间状态”描述系统在已知动态发生后，但在未知动态发生前所处的状态，其包含了所有与系统状态转移有关的已知信息。最后，提出一种基于在线学习的网络切片虚拟资源分配算法，其在每个离散的资源调度时隙内会根据当前系统状态为每个网络切片分配合适的资源块数量以及缓存资源。仿真结果表明，该算法能有效地满足各切片的服务质量(QoS)需求，降低网络回传链路带宽消耗的压力并同时提升系统吞吐量。
关键词:5G网络切片/
云无线接入网络/
资源分配/
马尔可夫决策过程
Abstract:To solve the problem of lacking efficient and dynamic resource allocation schemes for 5G Network Slicing (NS) in Cloud Radio Access Network (C-RAN) scenario in the existing researches, a virtual resource allocation algorithm for NS in virtualized C-RAN is proposed. Firstly, a stochastic optimization model in virtualized C-RAN network is established based on the Constrained Markov Decision Process (CMDP) theory, which maximizes the average sum rates of all slices as its objective, and is subject to the average delay constraint for each slice as well as the average network backhaul link bandwidth consumption constraint in the meantime. Secondly, in order to overcome the issue of having difficulties in acquiring the accurate transition probabilities of the system states in the proposed CMDP optimization problem, the concept of Post-Decision State (PDS) as an " intermediate state” is introduced, which is used to describe the state of the system after the known dynamics, but before the unknown dynamics occur, and it incorporates all of the known information about the system state transition. Finally, an online learning based virtual resource allocation algorithm is presented for NS in virtualized C-RAN, where in each discrete resource scheduling slot, it will allocate appropriate Resource Blocks (RBs) and caching resource for each network slice according to the observed current system state. The simulation results reveal that the proposed algorithm can effectively satisfy the Quality of Service (QoS) demand of each individual network slice, reduce the pressure of backhaul link on bandwidth consumption and improve the system throughput.
Key words:5G Network Slicing (NS)/
Cloud Radio Access Network (C-RAN)/
Resource allocation/
Markov Decision Process (MDP)
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