超密集无线网络资源分配若干算法研究

发布时间：2018-06-06 07:10

本文选题：超密集无线网络 + 非协作博弈　；参考：《南京邮电大学》2017年硕士论文

【摘要】：随着移动互联网兴起和大数据时代的来临,移动数据传输的需求在极速增加。另一方面,绿色通信、低碳生活已经成为可持续发展的必然要求,这给无线通信网络运营和管理带来了越来越大的挑战。为了满足终端用户对高质量、高速率的需求,超密集无线网络是应对此挑战的重要解决方案。在超密集无线网络环境下,传统的资源分配算法已经无法适用于未来网络的发展。如何解决超密集无线网络环境下的资源分配问题,成为未来网络发展的严峻考验。本文在介绍已有的资源分配技术的基础上,主要研究了三个方面的工作:(1)提出一种基于非协作博弈的资源分配算法。该算法将非协作博弈理论应用到超密集无线网络,通过在效用收益中引入非线性惩罚函数来缓解干扰与提高用户公平性,然后证明所提算法的纳什均衡存在与唯一性,并给出了具体详细的非协作博弈分布式算法。仿真结果表明所提算法在提高系统吞吐量、降低发射功率、提高算法收敛概率等方面具有卓越的性能。(2)提出了一种基于干扰协调的能效资源分配算法。该算法研究分簇后的超密集无线网络,借助图论着色原理对簇间与簇内干扰协调,减少网络信令交流。簇内资源由簇头进行分配,在干扰协调的基础上,分别采用最大最小算法和引入阻尼震动与适应度变异改进的粒子群算法对子信道和功率资源进行分配。仿真结果表明该算法可以有效减少能量消耗、提高能量效率及降低网络干扰。(3)设计并实现了超密集无线网络资源管理验证平台,搭建了一个超密集无线网络验证系统。系统由客户端(终端用户)、eNodeB(Evolved Node B)小基站和资源管理平台服务器、数据库组成。该平台针对超密集无线网络中干扰协调、无线资源分配和能量消耗问题,平台实现了全局资源实时监控、eNodeB小基站部署图的展示以及基于干扰协调的动态频谱分配策略和绿色节能休眠策略研发。测试结果表明,资源管理平台能够降低网络干扰、减少系统能耗、有效的进行频谱分配。最后,对本文研究进行了归纳总结,并对未来工作的深入方向和思路进行了展望。
[Abstract]:With the rise of mobile Internet and the advent of big data era, the demand for mobile data transmission is increasing rapidly. On the other hand, green communication, low-carbon life has become the inevitable requirement of sustainable development, which brings more and more challenges to the operation and management of wireless communication network. In order to meet the demand of end users for high quality and high speed, ultra dense wireless network is an important solution to meet this challenge. In the super dense wireless network environment, the traditional resource allocation algorithm can not be applied to the future development of the network. How to solve the problem of resource allocation in ultra-dense wireless network environment will be a severe test of network development in the future. Based on the introduction of existing resource allocation techniques, this paper mainly studies three aspects of work: 1) and proposes a resource allocation algorithm based on non-cooperative game. In this algorithm, the non-cooperative game theory is applied to the super-dense wireless networks, and the nonlinear penalty function is introduced to mitigate the interference and improve the fairness of the users. Then, the existence and uniqueness of the Nash equilibrium of the proposed algorithm are proved. A detailed distributed non-cooperative game algorithm is presented. Simulation results show that the proposed algorithm has excellent performance in improving system throughput, reducing transmission power and improving convergence probability of the algorithm. (2) an energy efficiency resource allocation algorithm based on interference coordination is proposed. Based on the graph theory coloring principle, the algorithm is used to coordinate the inter-cluster interference and intra-cluster interference, so as to reduce the network signaling exchange. The resources in the cluster are allocated by cluster heads. On the basis of interference coordination, the maximum and minimum algorithm and particle swarm optimization algorithm with improved damping vibration and fitness mutation are used to allocate subchannels and power resources, respectively. Simulation results show that the algorithm can effectively reduce energy consumption, improve energy efficiency and reduce network interference. The system consists of client (end user) small base station and resource management platform server, database. The platform aims at interference coordination, wireless resource allocation and energy consumption in ultra-dense wireless networks. The platform realizes the display of the deployment diagram of global resource real-time monitoring and monitoring of small base station, the dynamic spectrum allocation strategy based on interference coordination and the research and development of green energy saving dormancy strategy. The test results show that the resource management platform can reduce the network interference, reduce the energy consumption of the system, and effectively allocate the spectrum. Finally, the research is summarized, and the future work is prospected.
【学位授予单位】：南京邮电大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN929.5

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