MIMO系统下空时编码及检测算法的研究
发布时间：2019-03-18 20:15
【摘要】：在社会对通信需求与日剧增的同时,无线通信技术的应用逐渐深入到社会生活的方方面面。为了能够在复杂的通信环境中实现大容量和高速率的数据传输,使用MIMO技术可以提升系统信道容量。MIMO技术作为当下成熟应用于第四代移动通信的核心技术之一,本文对MIMO技术下的空时编码和检测算法两大关键部分进行了主要研究。空时编码技术是在MIMO技术基础上,结合时间与空间的相关性对传送信息比特进行空时编码,比SISO系统容量提升几十倍。由于传送会受到无线通信信道时变特性和多径衰落影响,因此在接收端提出有效可靠的检测算法来恢复出原始信号。本文首先从整体上对MIMO系统模型进行简述,介绍基本的分集复用、无线信道以及均衡估计,并从SISO系统信道容量推导出MIMO系统容量公式,利用MATLAB仿真证明MIMO技术对容量有巨大的提升。接着对两类空时编码展开了研究,接受端必须已知CSI的分层空时编码、空时网格编码和空时分组编码,无需CSI的差分空时编码,综合考虑每种编码方式的性能和复杂度,确定适合应用于何种场景下。最后如何准确恢复衰落后的原始信号,对检测算法研究并提出改进方案,最佳ML算法对于大型MIMO系统来说检测复杂度过高,线性ZF、MMSE算法检测性能却达不到要求,都存在很明显的弊端;在此前提下提出众多非线性算法,如干扰消除算法和QR分解算法结构简单性能也能够满足要求,并对串行干扰消除算法做出相应改进;为了追求最佳的检测效果跟较低复杂度,研究了球形检测算法,性能与ML算法相当,搜索空间却小很多,使用MATLAB软件仿真验证效果。
[Abstract]:At the same time, the application of wireless communication technology gradually goes deep into all aspects of social life. In order to realize large-capacity and high-speed data transmission in complex communication environment, the channel capacity of the system can be improved by using MIMO technology. As one of the core technologies used in the fourth generation of mobile communication, MIMO technology is one of the core technologies. In this paper, two key parts of space-time coding and detection algorithm in MIMO technology are studied. Space-time coding (STC) is a space-time coding technique for transmitting information bits, which is based on MIMO technology and combined with the correlation between time and space, which is several times higher than the capacity of SISO system. Because the transmission will be affected by the time-varying characteristics and multipath fading of the wireless communication channel, an effective and reliable detection algorithm is proposed at the receiver to recover the original signal. In this paper, the MIMO system model is introduced, and the basic diversity multiplexing, wireless channel and equalization estimation are introduced, and the MIMO system capacity formula is derived from the channel capacity of SISO system. The simulation results of MATLAB show that MIMO technology can greatly improve the capacity. Secondly, two classes of space-time coding are studied. The receiver must know the layered space-time coding of CSI, space-time trellis coding and space-time block coding, and do not need the differential space-time coding of CSI, considering the performance and complexity of each coding method. Determine which scenarios are suitable for application. Finally, how to accurately restore the original signal after fading, the detection algorithm is studied and improved, the optimal ML algorithm for large-scale MIMO system detection complexity is too high, but the linear ZF,MMSE algorithm detection performance is not up to the requirements. There are obvious disadvantages; On this premise, many non-linear algorithms, such as interference cancellation algorithm and QR decomposition algorithm, can also meet the requirements, and make corresponding improvements to the serial interference cancellation algorithm. In order to pursue the best detection effect and lower complexity, the spherical detection algorithm is studied. The performance of spherical detection algorithm is similar to that of ML algorithm, but the search space is much smaller. MATLAB software is used to simulate and verify the effect.
【学位授予单位】：北方工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN919.3
本文编号：2443202
[Abstract]:At the same time, the application of wireless communication technology gradually goes deep into all aspects of social life. In order to realize large-capacity and high-speed data transmission in complex communication environment, the channel capacity of the system can be improved by using MIMO technology. As one of the core technologies used in the fourth generation of mobile communication, MIMO technology is one of the core technologies. In this paper, two key parts of space-time coding and detection algorithm in MIMO technology are studied. Space-time coding (STC) is a space-time coding technique for transmitting information bits, which is based on MIMO technology and combined with the correlation between time and space, which is several times higher than the capacity of SISO system. Because the transmission will be affected by the time-varying characteristics and multipath fading of the wireless communication channel, an effective and reliable detection algorithm is proposed at the receiver to recover the original signal. In this paper, the MIMO system model is introduced, and the basic diversity multiplexing, wireless channel and equalization estimation are introduced, and the MIMO system capacity formula is derived from the channel capacity of SISO system. The simulation results of MATLAB show that MIMO technology can greatly improve the capacity. Secondly, two classes of space-time coding are studied. The receiver must know the layered space-time coding of CSI, space-time trellis coding and space-time block coding, and do not need the differential space-time coding of CSI, considering the performance and complexity of each coding method. Determine which scenarios are suitable for application. Finally, how to accurately restore the original signal after fading, the detection algorithm is studied and improved, the optimal ML algorithm for large-scale MIMO system detection complexity is too high, but the linear ZF,MMSE algorithm detection performance is not up to the requirements. There are obvious disadvantages; On this premise, many non-linear algorithms, such as interference cancellation algorithm and QR decomposition algorithm, can also meet the requirements, and make corresponding improvements to the serial interference cancellation algorithm. In order to pursue the best detection effect and lower complexity, the spherical detection algorithm is studied. The performance of spherical detection algorithm is similar to that of ML algorithm, but the search space is much smaller. MATLAB software is used to simulate and verify the effect.
【学位授予单位】：北方工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN919.3
【参考文献】
相关期刊论文 前4条
1 李苗;邵朝;赵国库;;不同信道条件下MIMO空时编码技术的特性分析[J];传感器与微系统;2016年06期
2 蒋涉权;王晶;彭超;;MIMO系统中空时编码性能仿真和分析[J];电信科学;2015年02期
3 赵新雪;李琼;肖维杰;高强;;低复杂度最大似然MIMO信号检测算法[J];计算机工程与设计;2014年01期
4 王峗;汪晋宽;解志斌;;一种改进的排序QR分解MIMO检测算法[J];信息与控制;2008年02期
相关硕士学位论文 前4条
1 金鑫;MIMO无线通信系统中的信号检测技术研究[D];华东理工大学;2014年
2 邹维辰;基于球形译码的MIMO系统信号检测算法的研究[D];江苏科技大学;2013年
3 徐志恒;无线通信中空时分组编码技术的研究[D];西南交通大学;2010年
4 雷霆;MIMO系统的空时编码技术研究[D];哈尔滨工程大学;2010年
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