【作者】 张杰；

【导师】 王勇超；

【作者基本信息】 西安电子科技大学 ， 通信与信息系统， 2018， 硕士

【摘要】 近年来,随着移动互联网的高速发展,无线数据业务流量呈指数式爆炸增长,人们对于更高质量的通信服务诉求也变得愈发强烈。大规模MIMO(Multi-Input Multi-Output,MIMO)作为第五代移动通信(5G)关键技术之一,能够大幅提升频谱效率和系统容量,在未来通信系统中势必将会扮演着重要角色。相比于传统MIMO系统,大规模MIMO系统在基站端配备有大量天线,能够基于相同时频资源服务更多的用户。然而,大规模天线阵列需要连接更多的射频链路,相比于廉价的天线单元,射频链路是通信系统中价格较为昂贵的组件。因此,大规模MIMO系统具有巨大容量增益的同时,所付出的代价是射频链路数目增加带来的高昂成本开销,以及基站端高复杂度的大型天线阵列完全收发机的设计。天线选择技术则是解决上述问题的有效方法,在降低系统开销和复杂度的同时,能够保证系统性能满足通信服务需求指标。鉴于此,本文将着重探讨大规模MIMO系统中的天线选择技术,提出了一种改进型基于2-范数的天线选择方案,并从离散集合选择函数中的亚模函数这一角度研究了天线选择问题,建立了大规模MIMO天线选择亚模函数优化模型,并根据亚模性质的“边际收益递减效应”设计了具有理论性能保证的基于容量增量最大化贪心选择算法及其对偶算法,同时给出了相应的理论分析。本文所包含的主要内容及工作如下:1、对大规模MIMO系统模型做了简介,对系统容量进行讨论分析;介绍了亚模函数的理论基础,主要包括亚模函数形式化定义、研究来源以及亚模函数的应用等内容。2、建立了大规模MIMO系统上行链路天线选择问题优化模型,提出了一种改进型基于2-范数的天线选择方案。此方案能够根据不同的信道传输条件,借助于选择系数动态地调整基站端实际投入使用的天线数目。该方案能够在减少天线数目的同时以低复杂度的运算操作,保证需要的系统信道容量。3、通过对天线选择问题的深入分析,探索其离散集合选择问题的本质,并将离散集合函数中的亚模函数引入到天线选择问题当中,设计大规模MIMO天线选择方案。首先将亚模函数与天线选择问题相结合,分析了问题模型所包含的亚模性质;其次建立了带有一致拟阵约束的大规模MIMO系统天线选择亚模函数优化模型;根据“边际收益递减效应”性质提出了基于容量增量最大化的贪心选择算法近似求解该优化模型;最后给出了该优化模型的对偶问题,以及相应的对偶容量增量最大化贪心选择算法。4、对本文所研究基于亚模函数天线选择模型贪心求解算法的理论分析表明:贪心选择算法求得的次优解与原问题最优解之间的近似比具有最差情况下的理论保证。换句话说,该算法求得的次优解可看作本文所提大规模MIMO天线选择亚模函数优化模型最优解的下界。更多还原

【Abstract】 In recent years,the traffic of wireless data service is increasing exponentially,and the demand for higher quality communication service is also growing stronger.As one of the key technologies of the fifth generation(5G)mobile communication systems,massive Multi-Input Multi-Output(MIMO)can significantly increase spectrum efficiency and system capacity,which will play an important role in the future communication systems.Compared with the conventional scale MIMO systems,massive MIMO systems are equipped with large scale antennas at the base station and can serve more user terminals with the same time-frequency resources.However,large-scale antenna arrays need more radio frequency(RF)chains.Compared to the cheap antenna units,the RF chains are relatively expensive components in communication systems.Therefore,while the massive MIMO systems have a huge capacity gain,the corresponding price paid is the high cost of the RF chains,but also the high complexity of full transceiver design for a large antenna array.Antenna selection is an effective method to solve the problems above,which can reduce overhead and complexity of the systems and guarantee that the system performance meets the demand of communication service.This paper focuses on the antenna selection problem in massive MIMO systems.First,we propose an improved 2-norm based antenna selection scheme.After that,we analyze the antenna selection problem from the view of the submodular functions.According to the "Law of Diminishing Returns" under the framework of submodularity,we design a customized capacity increment maximized greedy algorithm with guaranteed theoretical performance,and give out the corresponding theoretical analysis afterwards.The main contents and work of this article are as follows:1.This paper introduces massive MIMO system model,along with the discussion and analysis of system channel capacity.The theoretical basis of submodular functions is shown,mainly including the formal definition of submodualr functions,the source of research of submodualr functions,and the application of submodualr functions.2.In this paper,the model of antenna selection in the uplink of massive MIMO systems isestablished.We design an improved 2-norm based antenna selection scheme to reduce the number of antennas while guarantee the sufficient channel capacity performance with a low complexity.Aided by a selection coefficient,this scheme dynamically adjusts the number of antennas in use,which is determined by the different channel condition.3.According to the in-depth analysis of antenna selection,this paper explore that antenna selection problem actually possesses the nature of discrete set selection problems.Then submodular functions in discrete set functions is introduced to antenna selection issues.Firstly this paper combines the submodular functions with antenna selection and explores the submodularity inner the antenna selection model.Secondly the model of antenna selection in the uplink of massive MIMO systems is formulated to constrained submodular functions optimization(SFO)under the framework of submodularity,along with a customized capacity increment maximized greedy algorithm inspired by the "Law of Diminishing Returns".Then the dual problem of proposed optimization model as well as its dual capacity increment maximized greedy algorithm is further investigated.4.The theoretical analysis of the greedy algorithm for antenna selection model based on submodular functions in this paper shows that: the approximate ratio between the suboptimal solution obtained by the greedy selection algorithm and the optimal solution of the original problem provides the theoretical guarantee in the worst case.In other words,the suboptimal solution of this algorithm can be treated as the lower bound of the optimal solution of the antenna selection problem in the uplink of massive MIMO systems.更多还原