多天线无线通信系统的先进检测算法研究

摘要MIMO（Multiple input multiple output，MIMO）系统能不占用额外带宽的同时显著提高频谱效率和信息传输可靠性，在未来的无线通信领域具有广阔的应用前景，MIMO 系统的各种检测算法也是现今的研究热点。但是 MIMO 系统接收机存在严重的多天线干扰，如何有效地消除这些干扰是挖掘 MIMO 空间复用增益和分集增益关键所在。本文主要研究内容如下：
（1）研究并比较了迫零（Zero forcing，ZF）、最小均方误差（Minimum mean square error，MMSE）、串行干扰抵消（Successive interference cancellation，SIC）、格基规约（Lattice Reduction，LR）和最大似然（Maximum likelihood，ML）等多种传统算法。通过仿真对比可以发现，传统检测算法性能优劣排序如下：ML，SIC，MMSE 和ZF。引入格基规约之后，传统检测算法性能均有显著提高。（2）研究了球形译码（Sphere decoding，SD）和半正定松弛（Semi-definite relaxation，SDR）检测算法。仿真结果表明：球形译码的性能逼近最大似然的同时，在中高信噪比复杂度远低于最大似然。半正定松弛算法以多项式计算复杂度获得逼近最大似然的检测性能。 22273
毕业论文关键词：多输入多输出，检测算法，最大似然，格基规约，球形译码，半正定松弛
Title     Advanced    Detectors    for    Muti-antenna    Wireless Communication    System
Abstract
MIMO (Multiple input multiple output, MIMO) system can significantly improve
spectral efficiency and reliability of information transmission     without occupying
extra bandwidth of system. It has broad applications in the future wireless
communications. MIMO detectors also become the hotspots for the research on
MIMO system. But there is a serious interference between antennas of receiver in
MIMO system, and how to effectively eliminate such interference is the key of
exploiting spatial multiplexing and persity gains of MIMO. The main contents are
as follows:
(1) We study and compare the zero forcing (ZF), minimum mean square error
(MMSE), successive interference cancellation (SIC), lattice reduction (LR),
maximum likelihood (ML) and other traditional algorithms. From the simulation
results, these traditional detectors have decreasing order in performance as follows:
ML,SIC , MMSE and ZF. Combining lattice reduction with traditional detection
algorithms can significantly improve the performance of traditional detection.
(2) Sphere decoding (SD) and semi-definite relaxation (SDR) detectors are
investigated. Simulation results show that: the performance of SD is approximately
identical to that of ML, but its complexity is far lower than that of ML in the low and
medium SNR region. SDR can provide near-optimum BLER performance with
polynomial time complexity.
Keywords: multiple input multiple output, detection algorithm, the maximum
likelihood algorithm, lattice reduction, sphere decoding,    semi-definite relaxation
目   录
1 绪论. 1
1．1   选课背景及意义 1
1．2 MIMO 系统的发展历史及研究现状    1
1．3   本文主要工作和结构安排.. 2
2 MIMO 系统基本特性    4
2．1 MIMO 系统基本模型    4 多天线无线通信系统的先进检测算法研究:http://www.youerw.com/tongxin/lunwen_14833.html