LTE系统中高能效的资源联合分配算法研究

发布时间：2018-02-05 01:10

本文关键词： 无线资源管理绿色效率分布搜索混合模型粒子群算法多维资源联合优化　出处：《西安电子科技大学》2014年硕士论文　论文类型：学位论文

【摘要】：由于可再生能源的逐渐减少和全球变暖等问题日渐严重,节能减排成了世界性课题。据统计,当前的ICT行业能源消耗占世界总能源消耗的2%-6%左右,而且这一比例正在迅速提高。通信行业的高能耗问题由来已久,特别是蜂窝移动通信的快速发展使得这一问题更加突出。针对通信行业高能耗的问题,“绿色通信”历史性的成了通信行业的新任务。绿色通信不是单纯的以减少能源的消耗为首要目标,而是在保证用户的业务要求的基础上在有限的通信资源下尽量减少能源的消耗,从而减少对环境的污染和温室气体的排放。对于LTE系统中的高能耗问题,以绿色效率为目标的资源管理方法成为了解决这一问题重要手段。LTE系统的业务种类和QoS需求更加多样化,但是不能无限制地满足各类不同业务的需求或者为了满足苛刻业务的需求而浪费大量的资源。在满足不同用户需求的条件下如何高效合理的分配无线资源已成为LTE系统研究的热点问题。无线资源管理既要保证系统的吞吐量和能量效率、用户的公平性等性能,同时对算法的复杂度和收敛性也有相应的要求。本文从效率的原始内涵出发,分析无线通信效率的内涵、外延与优化准则,把问题通过数学语言转换成精确描述、公式化定义的严谨理论模型。针对LTE网络,本文构造基于绿色效率的具有边际效应递减的效用函数,并以此来实现多维资源联合优化。多维资源联合优化算法的计算复杂度较高,为了保证算法的实时性,本文通过分步搜索、迭代优化的方式,在多项式时间内逼近纳什议价解。基本粒子群算法是一种启发式进化算法,其基本思想源于对自然界中鸟群、鱼类等生物群体觅食行为的仿真研究。基本粒子群算法参数设置简单、收敛速度快但是算法的达优率较低,容易陷入局部最优,不能较好的获得目标函数的全局最优解。对于基本粒子群算法中的缺点,本文通过引入混合模型粒子群优化算法自适应的调整算法的全局寻优能力和局部寻优能力,可以在保证分步搜索的收敛速度的前提下避免陷入局部最优陷阱,提高寻优能力。最后通过Matlab软件来进行平台的搭建和算法的仿真,并与传统的资源管理算法进行对比,分析并验证了多维资源联合优化算法的优点。
[Abstract]:The renewable energy gradually reduced and increasingly serious problems such as global warming, energy saving and emission reduction has become a worldwide issue. According to statistics, the current ICT industry energy consumption accounted for 2%-6% of the total energy consumption around the world, and this proportion is increasing rapidly. Long-standing high energy consumption problems in the communications industry, especially the rapid development of mobile communication the problem is more prominent. According to the communications industry, the problem of high energy consumption, "green communication" has become a new historic task in the communications industry. Green communication is not simply to reduce energy consumption is the primary goal, but to ensure that the user's business requirements based on limited communication resources to minimize energy consumption, thereby reducing the environmental pollution and greenhouse gas emissions. For the problem of high energy consumption in the LTE system, resource management method based on green efficiency as the goal of the In order to solve this problem is an important means of.LTE system and QoS needs more diverse types of business, but can not indefinitely meet the different needs of the business or to meet the demanding needs of the business and a great waste of resources. To meet the needs of different users under the condition of how reasonable and efficient allocation of radio resources has become a hot issue in LTE system research on radio resource management. To ensure the throughput and energy efficiency of the system, the fairness performance of the user, but the complexity of algorithm convergence and also have the corresponding requirements. The efficiency of the original meaning of the content analysis of wireless communication efficiency, extension and optimization criterion, the problem through the language of mathematics into precise description, define rigorous theoretical model. In LTE network, this paper has constructed based on the utility function, the marginal effect of diminishing the number of green efficiency, In order to achieve optimal multidimensional resources. Calculation algorithm of joint optimization of multidimensional resources with high complexity, in order to ensure the real-time algorithm, through step-by-step search, iterative optimization, approximation of Nash bargaining solution in polynomial time. Basic particle swarm algorithm is a heuristic evolutionary algorithm, the basic idea stems from the bird group nature, Simulation Research on the foraging behavior of fish and other biological groups. Parameters of basic particle swarm optimization algorithm is simple and fast convergence but the algorithm success rate is low, easy to fall into local optimal solution can obtain the global optimal objective function better. For basic particle swarm algorithm, by introducing the mixed particle model swarm optimization algorithm of adaptive adjustment algorithm's searching ability and local optimization ability and convergence speed can avoid the premise to ensure the search step by step Finally, we use Matlab software to build platform and algorithm simulation, and compare with the traditional resource management algorithm, analyze and verify the advantages of multi-dimensional resource optimization algorithm.

【学位授予单位】：西安电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN929.5

【共引文献】