基于Markov预测的灵活网络动态切换技术研究(硕士)(论文30000字)
摘要
为满足更高容量与更好覆盖、极高通信可靠性与极低时延以及大规模机器类通信等技术需求，异构网络、超密集网络和软件定义网络等新型架构正在成为新一代无线网络的候选架构。超密集部署的异构蜂窝网络通过增加低功率节点数量的方式提升了系统容量，在很大程度上缓解了由无线通信设备数量以及所需通信流量的爆炸式增长带来的需求压力，但小小区的密集部署导致频繁切换和乒乓效应，造成了信道资源的浪费和用户体验质量的下降。因此，针对超密集蜂窝网络中大规模机器类通信所涉及的通信与计算问题，本文进行了一种基于Markov预测的灵活网络动态切换技术研究。本论文的主要研究内容以及创新点如下：
1.本文所考虑的超密集部署的异构网络是一种部分集成SDN中心控制器实体的接入网，并将无线接入点虚拟化为虚拟节点，采用半结构化分布。利用SDN控制器的全局化视图实时获取网络和终端参数，网络信息处理模块提取有关无线接入技术的网络参数以及状态信息，并对其进行加工处理，最终执行切换预测算法，为终端用户选择合适的网络并执行切换决策。
2.为提升大规模机器类通信的切换性能，本文把预测和小区切换相结合，提出了基于Markov预测的切换方案。首先建立Markov模型，利用使用非齐次的离散时间马尔科夫链得到用户在小区间的转移概率，得到预测目标小区。用户可以提前向预测小区发送认证，请求并转发数据，当用户接收到当前小区的信号强度减弱时，直接向预测小区发送连接请求。仿真结果表明，在低速率（2m/s）的移动场合，基于Markov预测的切换设计在保证切换性能的基础上便于有效预测用户下一个接入网络。
3.上下文感知计算主要用于提高系统性能及用户体验。本文提出了基于上下文感知的Markov预测切换系统模型，进一步优化算法。考虑上下文信息，即用户周围每个小区负载状况，目的是进一步提高切换算法的精确度和智能化。仿真结果表明，优化后的算法能够权衡考虑信号质量和各个小区的传输负载，为用户动态选择最佳切换小区。
关键词: 超密集蜂窝网络，大规模机器类通信，SDN控制器，Markov模型，上下文感知，切换
Abstract
To meet the requirements of higher capacity and better coverage, extremely high communication reliability and very low latency, and massive machine-type communication, new architectures such as Heterogeneous Network (HetNet), Ultra Dense Networks (UDN) and Software Defined Networks (SDN) are becoming candidate architectures for the next generation of wireless networks. Ultra-dense deployment of heterogeneous cellular networks improves system capacity by increasing the number of low power nodes, largely easing the demand pressure from the explosive growth of the number of wireless communications devices and the traffic needed, but the dense deployment of small small cell leads to frequent switching and ping-pong effects, It causes the waste of channel resources and the decline of Quality of Experience (QoE). Therefore, in order to solve the problem of the communication and computational problems of massive machine-type communication in an ultra-dense cellular network, a flexible network dynamic switching technology based on Markov prediction is proposed. The main research contents and innovation of this thesis are as follows:
1. In this paper, the ultra-dense deployment of heterogeneous networks is a part of the integrated central SDN controller entity of the access network, and the wireless access point is virtualized into virtual nodes, using a semi-structured distribution. The global view of SDN controller is used to obtain the network parameters and terminal parameters in real time, and the Network Information Processing module extracts the network parameters and state information ofRadio Access Technologies (RAT), and processes it, finally executes the handover prediction algorithm, selects the appropriate network for the end user and performs the handover decision.
2. In order to improve the handover performance of massive machine-type communication, this paper combines the prediction with the handover and proposes a handover scheme based on Markov prediction. Firstly, a Markov model is established to obtain the transfer probability between the cells by using the nonhomogeneous discrete time Markov chain, and forecaste the target cell. Users can send certification in advance to the prediction cell, request and forward data, when the user received the signal strength of the current cell weakened, can send connection requests to the prediction cell directly . The simulation results show that the handover design based on Markov prediction is easy to predict the next access network of the users on the basis of ensuring the handover performance in the low speed (2m/s) mobile situation.
3. Context-aware computing is mainly used to improve system performance and user experience. This paper presents a Markov predictive handover system model based on context-aware to further optimizes the algorithm. Considering the context information, that is, the load status of each cell around the user, and the aim is to further improve the precision and intelligence of the handoff algorithm. The simulation results show that the optimized algorithm can weigh the signal quality and the transmission load of each cell, and dynamically choose the best handover cell for the user.
Key words: ultra-dense cellular network, massive machine-type communicationa, SDN controller, Markov model, context-aware, handover
目录
第一章绪论 6
1.1 移动通信概述 6
1.2 移动网络切换概述 8
1.3 研究背景及意义 9
1.4 国内外研究现状 10
1.5 论文组织架构 12
第二章相关背景知识介绍 13
2.1 新型网络架构 13
2.1.1 异构网络 13
2.1.2 超密集网络 14
2.1.3 软件定义网络 16
2.2 第五代移动通信系统（5G） 19
2.2.1 5G简介 19
2.2.2 机器类通信（MTC） 21
2.2.3 高密度小小区的部署 23
2.2.4 物联网 24
2.3 移动网络切换分析 25
2.3.1 LTE切换概述 25
2.3.2 LTE标准切换 27
2.3.3 异构无线网络切换算法 28
2.4 本章小结 30
第三章基于Markov的预测切换方案研究 32
3.1 移动性管理 32
3.2 系统模型 33
3.2.1 SDN HetNet架构 33
3.2.2 半结构化的异构网络和网络拓扑图 34
3.3 基于预测的切换设计 35
3.3.1 Markov过程简述 36
3.3.2 Markov预测切换设计 36
3.3.3 预测切换算法流程 39
3.4 仿真结果及实现 40
3.4.1 切换算法性能评估 40
3.4.2 仿真分析 40
3.5 本章小结 43
第四章上下文感知下的动态切换优化 44
4.1 上下文感知 44
4.1.1 上下文感知概述 44
4.1.2 上下文感知应用 45
4.2 基于上下文感知的算法优化 46
4.2.1 预测算法与上下文信息的关联计算 47
4.2.2 基于上下文感知的预测切换流程 48
4.3 仿真分析 49
4.4 本章小结 51
第五章总结与展望 52
5.1 全文总结 52
5.2 后续工作与展望 53
参考文献 54 |
