多小区通信系统干扰对齐技术研究

发布时间：2018-03-27 09:04

本文选题：多小区　切入点：多天线　出处：《西安电子科技大学》2014年硕士论文

【摘要】：近年来,随着多媒体业务和移动智能终端的不断发展,无线通信数据量激增。为了满足人们对于更高数据速率的需求,各种提高频谱利用率的技术得到了巨大发展。其中多输入多输出(MIMO,Multiple-Input Multiple-Output)技术由于其能够有效利用空间复用增益来提高系统的传输速率而受到了越来越多的关注。同时,为了提高频谱的利用率,普遍采用复用因子为1的方式进行组网。这样在小区的边缘用户就会不可避免地收到来自其它基站的干扰以及多天线之间的干扰,干扰问题成为了限制系统容量的主要因素。干扰对齐(IA,Interference Alignment)作为干扰协作的一种特殊形式成为了目前的研究热点之一。干扰对齐打破了传统MIMO系统中信道容量干扰受限的观点,为在频谱资源有限的情况下提高系统吞吐量提供了一个新的研究方向。与传统的资源平分不同,干扰对齐将来自不同基站的干扰在接收端进行压缩,从而使每个基站可以获得更多的发送自由度(DoF,Degree of Freedom)。本文的研究主要关注于如何在实现干扰对齐的同时进一步实现系统传输速率的提升。本文首先对现有的干扰对齐算法进行了分析。实现干扰对齐的算法形式主要有两种,闭合式干扰对齐算法和迭代式干扰对齐算法。闭合干扰对齐算法虽然设计简单,但是每个节点都需要获得全局的信道状态信息(CSI,Channel State Information)。迭代干扰对齐算法利用信道的互易性通过发送端和接收端的不断迭代实现干扰对齐,每个节点只需要了解本节点的信道状态信息,所以受到了非常广泛的研究。迭代算法中基于最小干扰泄漏的干扰对齐使每个用户接收到的来自其它基站的干扰强度最小,但是忽略了有用信号所经历的信道状态信息;基于秩受限秩最小的干扰对齐算法虽然将干扰的秩压缩到最小,并且保证每个基站获得最大的发送自由度,但是并不能保证系统速率实现最优。本文提出的优化算法,是在保证系统自由度最大的情况下,实现系统速率的进一步提高。利用系统速率优化的非凸特性,将预编码沿着吞吐量梯度方向进行搜索,使预编码沿着提高系统吞吐量上升的方向优化。在迭代过程中随着搜索步长的不断减小,算法近似退化为秩受限秩最小的干扰对齐,因此系统最终可以实现干扰对齐,并且系统吞吐量的提高。由仿真结果可以看出,相比已有的干扰对齐实现方案,本文提出的优化算法在系统传输速率方面有明显的提升。
[Abstract]:In recent years, with the continuous development of multimedia services and mobile intelligent terminals, the amount of wireless communication data has increased dramatically. Various techniques to improve spectral efficiency have been greatly developed. Among them, the multiple input multiple output (MIMOO) Multiple-Input Multiple-Output (MIMO) technology has attracted more and more attention because of its ability to effectively utilize spatial multiplexing gain to improve the transmission rate of the system. In order to improve the efficiency of the spectrum, the multiplexing factor of 1 is widely used to construct the network, so that the edge users in the cell will inevitably receive interference from other base stations and interference between multiple antennas. As a special form of interference coordination, interference alignment has become one of the research hotspots. Interference alignment breaks the view that channel capacity interference is limited in traditional MIMO systems. It provides a new research direction for improving the system throughput under the condition of limited spectrum resources. Different from the traditional resource sharing, interference alignment compresses the interference from different base stations at the receiving end. So that each base station can obtain more freedom of transmission. The research of this paper mainly focuses on how to achieve interference alignment while further improving the transmission rate of the system. Firstly, this paper focuses on the existing interference pair. The algorithm is analyzed. There are two kinds of algorithms to realize interference alignment. Closed interference alignment algorithm and iterative interference alignment algorithm. Although the design of closed interference alignment algorithm is simple, However, each node needs to obtain global channel state information / channel State information. Iterative interference alignment algorithm uses channel reciprocity to achieve interference alignment through continuous iterations at the sender and receiver. Each node only needs to know the channel state information of the node, so it has been widely studied. The interference alignment based on minimum interference leakage in the iterative algorithm makes each user receive the least interference from other base stations. However, the channel state information experienced by useful signals is ignored. Although the rank of interference alignment algorithm based on rank limited rank minimization can reduce the rank of interference to the minimum, and ensure that each base station obtains the maximum degree of freedom. But it is not guaranteed that the system speed can be optimized. The optimization algorithm proposed in this paper is to achieve further improvement of the system rate under the condition of ensuring the maximum degree of freedom of the system. The precoding is searched along the throughput gradient, and the precoding is optimized along the direction of increasing the system throughput. In the iterative process, as the search step size decreases, the algorithm is approximately reduced to the interference alignment with the minimum rank limited rank. Therefore, the system can eventually achieve interference alignment, and improve the system throughput. The simulation results show that compared with the existing interference alignment implementation scheme, the proposed optimization algorithm in the system transmission rate has significantly improved.
【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】：TN972

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