认知无线Mesh网络中面向流媒体传输的组播技术研究

发布时间：2018-05-24 22:32

本文选题：认知无线Mesh网络 + 组播路由　；参考：《东北大学》2014年硕士论文

【摘要】：随着无线通信技术的快速发展,频谱资源稀缺和授权频谱利用率低的问题日益凸显。认知无线Mesh网络将认知无线电技术和无线Mesh网络相结合,使网络具备认知、可重配置、自组织等能力,成为构建新一代宽带移动互联网络的潜在核心技术。然而,受多径衰落、码间串扰和信道干扰等因素的影响,无线网络中流媒体的高效传输面临严峻挑战。为此,本文针对认知无线Mesh网络中面向流媒体传输的组播技术进行深入研究。针对流媒体对延迟和延迟抖动的较高要求,通过综合考虑通信功率、信道干扰等对链路容量的影响,提出了基于协议干扰模型的流媒体组播路由跨层优化模型。建立了基于组播树延迟的用户满意度评判标准,提出一种以最大化用户满意度为目标的流媒体组播路由跨层优化算法。利用遗传算法进行信道分配和功率控制,利用蚁群算法进行组播树构建,通过遗传算法与蚁群算法的嵌套优化方式,实现了信道分配、功率控制和组播树构建的联合跨层优化。并分别设计了混合编码方式与整数编码方式,以加速算法收敛。一系列仿真实验验证了算法的有效性。由于组播传输速率对流媒体性能有较大影响,且物理干扰模型能更好的表征接收节点的信干噪比,进一步,以最大化组播传输速率为目标,提出了基于物理干扰模型的流媒体组播速率跨层优化模型。在该模型中,物理链路层和网络层之间受容量约束耦合,具有清晰的分层结构。通过引入对偶分解理论,将原始问题分解为物理链路层的资源分配子问题和网络层的组播速率优化子问题,提出了基于对偶分解的流媒体组播速率跨层优化算法。采用遗传算法求解物理链路层子问题,采用凸优化方法求解网络层子问题,两个子问题通过对偶变量相互协调,最终实现功率控制和组播速率的联合优化。由于信道干扰严重影响通信质量,进一步提出了基于对偶分解的联合信道分配和功率控制的流媒体组播速率跨层优化算法,以降低节点间干扰,提高传输速率。最后通过大量仿真实验验证了算法的有效性。
[Abstract]:With the rapid development of wireless communication technology, the scarcity of spectrum resources and the low efficiency of authorized spectrum become more and more serious. Cognitive wireless Mesh network combines cognitive radio technology with wireless Mesh network, which makes the network have the ability of cognition, reconfiguration, self-organization and so on. It becomes the potential core technology to construct the new generation broadband mobile Internet. However, due to multipath fading, inter-symbol crosstalk and channel interference, the efficient transmission of streaming media in wireless networks faces severe challenges. Therefore, the multicast technology for streaming media transmission in cognitive wireless Mesh networks is studied in this paper. Aiming at the high requirement of streaming media to delay and delay jitter, a cross-layer optimization model of streaming media multicast routing based on protocol interference model is proposed by considering the influence of communication power and channel interference on link capacity. In this paper, the criteria of user satisfaction evaluation based on multicast tree delay are established, and a cross-layer optimization algorithm for streaming media multicast routing is proposed, which aims at maximizing user satisfaction. Genetic algorithm (GA) is used for channel allocation and power control, ant colony algorithm (ACA) is used to construct multicast tree. Through nested optimization of genetic algorithm and ant colony algorithm, the joint cross-layer optimization of channel allocation, power control and multicast tree construction is realized. The hybrid coding and integer coding are designed to accelerate the convergence of the algorithm. A series of simulation experiments verify the effectiveness of the algorithm. Because the multicast transmission rate has great influence on the streaming media performance, and the physical interference model can better represent the signal-to-noise ratio of the receiving node, and further, the goal is to maximize the multicast transmission rate. A cross-layer optimization model of streaming media multicast rate based on physical interference model is proposed. In this model, the physical link layer and the network layer are coupled by capacity constraints and have a clear hierarchical structure. By introducing the dual decomposition theory, the original problem is decomposed into the sub-problem of resource allocation in the physical link layer and the multicast rate optimization problem in the network layer, and a cross-layer optimization algorithm for streaming media multicast rate based on duality decomposition is proposed. The genetic algorithm is used to solve the physical link layer subproblem and the convex optimization method is used to solve the network layer subproblem. The two subproblems are coordinated with each other by dual variables to achieve the joint optimization of power control and multicast rate. Because the channel interference seriously affects the communication quality, a cross-layer optimization algorithm of streaming media multicast rate based on dual decomposition is proposed to reduce the inter-node interference and improve the transmission rate. Finally, the effectiveness of the algorithm is verified by a large number of simulation experiments.
【学位授予单位】：东北大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN929.5

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