基于分簇的干扰对齐技术研究
发布时间：2018-11-18 12:13
【摘要】：近些年来无线通信技术的学术界出现了一种新型的干扰管理技术,我们可称之为干扰对齐技术。该技术核心思想在于通过将接收端接收信号中不同方向的干扰信号对齐到同一方向或同一空间中去,以压缩干扰信号所占空间从而提高期望信号空间维度来容纳有用信号,以此来提升信道容量和通信速率。一方面,干扰对齐技术的出现使得无线干扰网络的容量产生了飞跃式的增长,之前学者对于干扰网络容量界限的认识也被大大扩展,这对于下一代通信系统中存在多用户干扰时的大容量可靠传输研究是有极大帮助的;另一方面,通过干扰对齐,自由度的概念被引入,这不仅推动了网络信息论发展,更是在研究多用户多天线干扰信道网络的容量区域界限中起到了举足轻重的作用。然而,现阶段干扰对齐距离实际通信系统的应用还有很远的距离,还存在很多尚未解决的问题。比如:干扰对齐的性能很大程度上受到天线配置,通信流数以及用户对数目的限制;现有的可以通用于不同用户对数目干扰信道的对齐方法过于复杂,且复杂度随用户数目增加呈加速上升趋势。针对以上问题,本论文以改进干扰对齐算法使其适用于实际通信环境为最终目标,研究并提出改进后的干扰对齐技术方案及算法。文章主要工作如下:1.就传统多用户干扰信道的干扰对齐中用户数目对于干扰对齐复杂度的影响进行了分析并研究了分簇思想在其中的应用。对于簇内的干扰对齐,提出了一种簇内非迭代式干扰对齐算法并将其与迭代式最大化SINR对齐法进行比较。仿真结果证明分簇式干扰对齐极大的降低了干扰对齐的复杂度及运行时间,且在少数特定的通信环境下其性能优于某些全局迭代式干扰对齐算法。2.针对将干扰对齐算法简单应用于分簇式干扰对齐时易导致簇间干扰强烈从而影响系统性能的情况,从接收端接收信号的干扰成分入手对簇间干扰进行了仿真分析并根据处理簇间干扰时接收端对非同一簇干扰源信道状态信息的利用方式提出并分析了MMSE接收机法与对角载入法这两种簇间干扰处理方法。仿真结果证明上述两种方法均在没有增加或增加很少的运算复杂度及运行时间的前提下有效的处理了簇间干扰。3.针对在实际通信系统中不同干扰链路的能量大小不一致的情况,挖掘了干扰信道的统计特性并根据这种特性研究了基于接收端干扰的分簇算法。然后根据干扰链路的统计信息和系统和速率表达式对链路权重做了定义,并根据权重概念分别从“未分簇用户挑选簇”和“所分簇挑选未分簇用户”两个角度提出并分析了两种分簇策略。仿真结果证明了这三种分簇策略下的分簇式干扰对齐方法均极大的降低了系统的运算复杂度提升了运行效率,且相对于随机分簇的方法性能均有明显的提升。
[Abstract]:In recent years, a new interference management technology has emerged in the academic field of wireless communication technology, which can be called interference alignment technology. The core idea of this technique is to compress the space occupied by the interference signal in the same direction or in the same space, so as to increase the dimension of the desired signal space to accommodate the useful signal. In this way, the channel capacity and communication rate are increased. On the one hand, interference alignment technology has made the capacity of wireless interference network grow by leaps and bounds, and previous scholars' understanding of the limits of interference network capacity has also been greatly expanded. This is of great help to the research of large capacity and reliable transmission in the next generation communication system with multi-user interference. On the other hand, the concept of degree of freedom is introduced through interference alignment, which not only promotes the development of network information theory, but also plays an important role in the study of the capacity limits of multi-user and multi-antenna interference channel networks. However, there is still a long way to go in the application of interference alignment to the actual communication system, and there are still many unsolved problems. For example, the performance of interference alignment is greatly limited by the antenna configuration, the number of traffic and the number of users. The existing alignment methods which can be used for different users are too complex and the complexity increases with the increase of the number of users. Aiming at the above problems, this paper aims at improving the interference alignment algorithm so that it can be applied to the actual communication environment, and proposes an improved interference alignment scheme and algorithm. The main work of this paper is as follows: 1. In this paper, the influence of the number of users on the complexity of interference alignment in traditional multi-user interference channels is analyzed, and the application of clustering theory in interference alignment is studied. For intra-cluster interference alignment, a non-iterative intra-cluster interference alignment algorithm is proposed and compared with iterative maximization SINR alignment method. Simulation results show that clustering interference alignment greatly reduces the complexity and running time of interference alignment, and its performance is superior to that of some global iterative interference alignment algorithms in a few specific communication environments. 2. When the interference alignment algorithm is simply applied to cluster interference alignment, it can easily lead to strong inter-cluster interference and affect the performance of the system. The intercluster interference is simulated and analyzed from the interference components of the received signal at the receiving end, and the MMSE receiver method is proposed and analyzed according to the way in which the receiver uses the channel state information of the non-same cluster interference source when dealing with the inter-cluster interference. And diagonal loading method, which are two kinds of intercluster interference processing methods. The simulation results show that the two methods can effectively deal with the inter-cluster interference without increasing or increasing the computational complexity and running time. The statistical characteristics of the interference channel are excavated and the clustering algorithm based on the interference from the receiver is studied according to the fact that the energy of different interference links is not consistent in the actual communication system. Then the link weight is defined according to the statistical information of the interference link and the system and rate expression. According to the concept of weight, two clustering strategies are proposed and analyzed from the two angles of "selecting the user without clustering" and "selecting the user without clustering by the cluster". The simulation results show that these three clustering interference alignment methods greatly reduce the computational complexity of the system and improve the efficiency of the system, and compared with the random clustering method, the performance of the proposed method is obviously improved.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN972
本文编号：2340013
[Abstract]:In recent years, a new interference management technology has emerged in the academic field of wireless communication technology, which can be called interference alignment technology. The core idea of this technique is to compress the space occupied by the interference signal in the same direction or in the same space, so as to increase the dimension of the desired signal space to accommodate the useful signal. In this way, the channel capacity and communication rate are increased. On the one hand, interference alignment technology has made the capacity of wireless interference network grow by leaps and bounds, and previous scholars' understanding of the limits of interference network capacity has also been greatly expanded. This is of great help to the research of large capacity and reliable transmission in the next generation communication system with multi-user interference. On the other hand, the concept of degree of freedom is introduced through interference alignment, which not only promotes the development of network information theory, but also plays an important role in the study of the capacity limits of multi-user and multi-antenna interference channel networks. However, there is still a long way to go in the application of interference alignment to the actual communication system, and there are still many unsolved problems. For example, the performance of interference alignment is greatly limited by the antenna configuration, the number of traffic and the number of users. The existing alignment methods which can be used for different users are too complex and the complexity increases with the increase of the number of users. Aiming at the above problems, this paper aims at improving the interference alignment algorithm so that it can be applied to the actual communication environment, and proposes an improved interference alignment scheme and algorithm. The main work of this paper is as follows: 1. In this paper, the influence of the number of users on the complexity of interference alignment in traditional multi-user interference channels is analyzed, and the application of clustering theory in interference alignment is studied. For intra-cluster interference alignment, a non-iterative intra-cluster interference alignment algorithm is proposed and compared with iterative maximization SINR alignment method. Simulation results show that clustering interference alignment greatly reduces the complexity and running time of interference alignment, and its performance is superior to that of some global iterative interference alignment algorithms in a few specific communication environments. 2. When the interference alignment algorithm is simply applied to cluster interference alignment, it can easily lead to strong inter-cluster interference and affect the performance of the system. The intercluster interference is simulated and analyzed from the interference components of the received signal at the receiving end, and the MMSE receiver method is proposed and analyzed according to the way in which the receiver uses the channel state information of the non-same cluster interference source when dealing with the inter-cluster interference. And diagonal loading method, which are two kinds of intercluster interference processing methods. The simulation results show that the two methods can effectively deal with the inter-cluster interference without increasing or increasing the computational complexity and running time. The statistical characteristics of the interference channel are excavated and the clustering algorithm based on the interference from the receiver is studied according to the fact that the energy of different interference links is not consistent in the actual communication system. Then the link weight is defined according to the statistical information of the interference link and the system and rate expression. According to the concept of weight, two clustering strategies are proposed and analyzed from the two angles of "selecting the user without clustering" and "selecting the user without clustering by the cluster". The simulation results show that these three clustering interference alignment methods greatly reduce the computational complexity of the system and improve the efficiency of the system, and compared with the random clustering method, the performance of the proposed method is obviously improved.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN972
【共引文献】
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1 姜建;异构无线网络中的选择策略研究[D];西安电子科技大学;2014年
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3 马骁;异构网络多网并发传输技术研究[D];西安电子科技大学;2014年
4 闫继垒;分布式天线系统中的资源优化[D];西安电子科技大学;2014年
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2 王磊;高架桥环境下高铁无线通信信道建模与仿真[D];西南交通大学;2014年
3 许文建;基于SNMP的数字基站监控系统设计与实现[D];华南理工大学;2015年
，本文编号：2340013
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