基于差分概率的窄带信道估计方法研究

发布时间：2018-05-21 02:15

本文选题：时变信道 + 衰落　；参考：《大连工业大学》2016年硕士论文

【摘要】：陆地移动无线通信信道具有时变性和衰落特性,其统计特征可以用瑞利分布来描述。但无线信道往往容易受移动端的移动速度、传输速率、散射环境以及载波频率等因素的影响。而信道是通信系统必不可少的组成部分,信道估计又是移动通信系统的关键技术,估计的精度直接影响整个系统的性能。在无线通信技术高速发展的同时,高速铁路的发展速度也令人瞩目。高速铁路运行速度的提升是对无线通信技术的考验,尤其是对信道估计技术的挑战。当移动台移动速度增加的情况下,会产生更大的多普勒频移,随之带来更加恶劣的信道环境。如何在如此恶劣的环境中保证通信质量,成为当今无线移动通信领域亟待解决的问题,其关键在于信道估计的实现。虽然现有的信道估计方法已经相当成熟,经典的信道估计方法有非盲信道估计、盲信道估计和半盲信道估计,常用的算法包括最小二乘法、最大似然估计、最小均方误差、迫零算法、线性最小均方误差、最大比合并等。另外还有很多插值方法,如一阶线性插值,二阶多项式插值,低通插值,样条插值,时域插值。但大多估计方法都存在计算量大、复杂度高等问题。因此针对现有信道估计方法存在的问题,本文首先对已有的经典信道估计方法进行分类总结,并对常用的估计算法进行了仿真分析。其次还对无线衰落信道特性进行分析并对衰落信道进行分类总结。之后本文主要在时变条件下,研究移动端移动速度对衰落的影响,利用差分概率的思想提出基于差分概率的信道估计方法。其中重点研究了信道增益的时序关系及其时序差分量的概率统计特性,观测到信道增益差分量的分布规律、各时点的差分概率分布是存在的,并且信道增益具有时序依存性。本文还将信道增益的时序关系及其时序差分量的概率统计特性相结合进行仿真实验,实验结果表明信道估计的计算量和复杂度得到了降低。最后,还对全文做了总结,得出结论,并对文中所提法下一步可研究的内容及方向做了展望。
[Abstract]:Land mobile wireless communication channels have time-varying and fading characteristics, and their statistical characteristics can be described by Rayleigh distribution. However, wireless channels are often affected by mobile speed, transmission rate, scattering environment and carrier frequency. The channel is an essential part of the communication system, and channel estimation is the key technology of the mobile communication system. The accuracy of the estimation directly affects the performance of the whole system. With the rapid development of wireless communication technology, the development speed of high-speed railway is also remarkable. Improving the speed of high-speed railway is a challenge to wireless communication technology, especially to channel estimation technology. When the moving speed of mobile station increases, the Doppler frequency shift will be larger, and the worse channel environment will follow. How to guarantee the communication quality in such a bad environment has become an urgent problem in the field of wireless mobile communication. The key problem lies in the implementation of channel estimation. Although the existing channel estimation methods are quite mature, the classical channel estimation methods include non-blind channel estimation, blind channel estimation and semi-blind channel estimation. The commonly used algorithms include least square method, maximum likelihood estimation, minimum mean square error. Zero forcing algorithm, linear minimum mean square error, maximum ratio combination, etc. There are also many interpolation methods, such as first order linear interpolation, second order polynomial interpolation, low pass interpolation, spline interpolation, time domain interpolation. However, most of the estimation methods have many problems, such as large computation and high complexity. Therefore, aiming at the problems of existing channel estimation methods, the classical channel estimation methods are classified and summarized in this paper, and the commonly used estimation algorithms are simulated and analyzed. Secondly, the characteristics of wireless fading channel are analyzed and the fading channel is classified and summarized. Then, under the condition of time-varying, the influence of mobile speed on fading is studied, and a channel estimation method based on differential probability is proposed by using the idea of differential probability. The time-series relationship of channel gain and the probability and statistical characteristics of time-series difference components are studied. The distribution law of channel gain difference components is observed. The differential probability distribution of each time point exists and the channel gain is time-dependent. In this paper, the time-series relation of channel gain and the probability and statistical characteristics of time-series difference component are combined to carry out simulation experiments. The experimental results show that the computational complexity and computational complexity of channel estimation are reduced. Finally, the paper summarizes the full text, draws a conclusion, and looks forward to the content and direction that can be studied in the next step.
【学位授予单位】：大连工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TN929.5

【相似文献】