基于仿真实验的移动自组织网络链路连通性建模研究

发布时间：2018-05-24 16:24

本文选题：移动自组织网络 + 链路连通性　；参考：《哈尔滨工业大学》2014年硕士论文

【摘要】：移动自组织网络(Mobile Ad-hoc Networks,MANET)是一种无固定接入点、节点自我组织和管理的无线通信系统,组网灵活,应用前景广阔。在移动自组织网络中,节点间的通信路径由一系列无线链路组成,链路性能直接影响网络通信质量,而链路最基本的特性是链路连通性。通信节点的移动性使得链路连通情况频频变化,导致网络拓扑结构和通信路由等也随之变化。因此,针对动态网络的链路连通性研究对网络拓扑控制和路由协议分析等有着重要的意义。链路连通性主要受无线链路传输环境和节点移动特性影响。根据无线电磁波在空中传播的损耗特性,理论推导了信号功率的三种衰落模型:路径损耗、阴影衰落和多径衰落。利用信号功率衰落特性,引入节点有效传输范围模型。通过仿真实验比较三种衰落模型不同组合下节点有效传输环境的变化情况,分析无线链路传输环境对链路连通性的影响。同时,对比研究三种常见运动模型:随机行走模型、高斯-马尔可夫移动模型和半马尔可夫平滑移动模型。通过实验仿真单个节点运动轨迹和某时刻所有节点的空间分布,评估三种模型的优缺点。然后,选取运动规律更贴近实际情况的半马尔可夫移动模型作为后续研究的节点运动模型。在此基础上,利用图论的邻接矩阵和链路连通概率向量表示链路连通性,结合马尔可夫链理论,建立具有时变特性的一阶马尔可夫链路连通性模型。为了提高模型精度,依据高阶马尔可夫过程可以逼近任意可测过程的理论基础,将一阶马尔可夫链路连通性模型扩展到高阶马尔可夫链路连通性模型,最终建立具有时变特性和高精度的高阶马尔可夫链路连通性模型。通过蒙特卡洛仿真得到不同时刻链路连通状态,采用统计方法获得链路连通性模型参数:马尔可夫转移概率矩阵。为了验证链路连通性模型的准确性,通过与蒙特卡洛仿真、多状态一阶马尔可夫模型实验对比,评估网络特性参数:链路生命时间。并分析模型精度与马尔可夫链阶数之间的对应关系,优化模型。通过仿真实验得到以下结论:首先,高阶马尔可夫链路连通性模型能够有效地描述无线链路随时间变化的连通情况;其次,高阶马尔可夫链路连通性模型生成的链路生命时间精度随着马尔可夫链阶数增加而提升,当马尔可夫链阶数大于四时,模型精度提升不明显;最后,相比多状态一阶马尔可夫模型,四阶马尔可夫链路连通性模型的模型误差下降了68%。
[Abstract]:Mobile Ad-hoc Networks (Manet) is a wireless communication system with no fixed access point and node self-organization and management. In mobile ad hoc networks, the communication path between nodes is composed of a series of wireless links. The link performance directly affects the communication quality of the network, and the most basic characteristic of the link is link connectivity. The mobility of communication nodes causes frequent changes in link connectivity, resulting in changes in network topology and communication routing. Therefore, the research on link connectivity of dynamic networks is of great significance to network topology control and routing protocol analysis. Link connectivity is mainly affected by wireless link transmission environment and node mobility. According to the loss characteristics of wireless electromagnetic wave propagation in the air, three fading models of signal power are derived theoretically: path loss, shadow fading and multipath fading. Based on the signal power fading characteristic, the effective transmission range model is introduced. The effect of wireless link transmission environment on link connectivity is analyzed by comparing the change of node effective transmission environment under different combinations of three fading models. At the same time, three common motion models are compared: random walking model, Gauss-Markov moving model and semi-Markov smooth moving model. The advantages and disadvantages of the three models are evaluated by simulating the motion trajectory of a single node and the spatial distribution of all nodes at a certain time. Then, the semi-Markov moving model, which is closer to the actual situation, is selected as the node motion model in the following research. On this basis, using the adjacency matrix of graph theory and link connected probability vector to express link connectivity, combining with Markov chain theory, a first-order Markov link connectivity model with time-varying characteristics is established. In order to improve the accuracy of the model, the first order Markov link connectivity model is extended to the high order Markov link connectivity model according to the theoretical basis that high order Markov processes can approach any measurable process. Finally, a high order Markov link connectivity model with time varying characteristics and high accuracy is established. The link connected states at different times are obtained by Monte Carlo simulation, and the link connectivity model parameters: Markov transition probability matrix are obtained by statistical method. In order to verify the accuracy of the link connectivity model, the link life time is evaluated by comparing it with Monte Carlo simulation and multi-state first-order Markov model. The relationship between the precision of the model and the order of Markov chain is analyzed to optimize the model. The simulation results are as follows: firstly, the high order Markov link connectivity model can effectively describe the connectivity of wireless links over time; secondly, The link lifetime accuracy generated by the higher-order Markov link connectivity model increases with the increase of Markov chain order. When the Markov chain order is greater than 04:00, the model accuracy is not improved obviously. Compared with the multi-state first-order Markov model, the model error of the fourth-order Markov link connectivity model is reduced by 68 degrees.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN929.5

【共引文献】