基于压缩感知的信道估计算法研究

发布时间：2018-03-23 01:34

  本文选题：正交频分复用　切入点：信道估计　出处：《西南交通大学》2017年硕士论文　论文类型：学位论文

【摘要】：在正交频分复用(OFDM)系统中,准确、实时的信道估计是接收信息成功解调的重要保证。高速移动和多径传播环境使得基站和移动台之间的信号受到多普勒效应和多径效应的影响。为获得准确的信道响应,传统的信道估计算法利用大量的导频获取信道状态信息CSI(Channel State Information),使得导频开销难以降低,造成系统频谱利用率下降。因此,在信道估计性能和导频开销之间取得良好的折中,一直是信道估计亟需解决的问题。本文主要研究基于压缩感知理论的信道估计算法,主要研究内容如下:1.研究了无线通信传播环境的特性和无线信道建模的相关知识,建立信道的CE-BEM模型,研究表明,信道基扩展模型的基系数表现出稀疏性,为压缩感知信道估计提供了理论保证;搭建SISO-OFDM链路级仿真平台,利用搭建的仿真平台,对传统信道估计算法和压缩感知信道估计算法进行仿真分析。2.从理论上分析了压缩感知理论应用于信道估计的可行性,建立了压缩感知理论和OFDM系统信道估计算法的联系;恢复算法是压缩感知领域研究的重点之一,本文对多种压缩感知恢复算法进行理论分析,针对算法的缺陷,设计算法的优化方案;在链路级系统仿真平台上,对多种压缩感知信道估计算法进行了仿真验证。3.论文针对正交匹配追踪算法具有信道估计效率低、需要信道稀疏度作为先验信息等缺点,进行算法优化。首先,针对需要稀疏度作为先验信息的缺点,采用稀疏度自适应匹配追踪(CE-SAMP)算法进行信道估计,实验结果表明,CE-SAMP信道估计算法不需要确定的稀疏度作为先验信息,而且算法效率高于正交匹配追踪算法;针对SAMP算法不能准确逼近真实稀疏度的缺陷,设计了可变步长稀疏度自适应匹配追踪(CE-AsSAMP)估计算法;论文结合分段匹配追踪和共轭梯度方向更新的思想,设计了分段共轭匹配追踪(CE-StCGP)估计算法,并进行了仿真分析,实验结果表明,在实现相同信道估计均方误差性能的情况下,分段共轭匹配追踪算法的恢复速度最快。
[Abstract]:In OFDM (orthogonal Frequency Division Multiplexing) system, Real-time channel estimation is an important guarantee for successful demodulation of received information. In order to obtain accurate channel response, the signal between base station and mobile station is affected by Doppler effect and multipath effect in high speed mobile and multipath transmission environment. The traditional channel estimation algorithm uses a large number of pilots to obtain channel state information CSI(Channel State information, which makes the pilot cost difficult to reduce and the system spectrum efficiency drop. Therefore, a good compromise between channel estimation performance and pilot overhead is achieved. Channel estimation has always been a problem that needs to be solved. This paper mainly studies the channel estimation algorithm based on compressed sensing theory. The main research contents are as follows: 1. The characteristics of wireless communication environment and the related knowledge of wireless channel modeling are studied. The CE-BEM model of the channel is established. The research shows that the base coefficient of the channel expansion model is sparse, which provides a theoretical guarantee for the compressed perceptual channel estimation. The SISO-OFDM link-level simulation platform is built and the simulation platform is built. The traditional channel estimation algorithm and compressed perceptual channel estimation algorithm are simulated. 2. The feasibility of applying compressed sensing theory to channel estimation is analyzed theoretically, and the relationship between compressed sensing theory and channel estimation algorithm in OFDM system is established. The restoration algorithm is one of the focal points in the field of compression perception. In this paper, a variety of compression sensing recovery algorithms are theoretically analyzed, and the optimization scheme of the algorithm is designed in view of the shortcomings of the algorithm; on the link-level system simulation platform, In this paper, several compressed perceptual channel estimation algorithms are simulated. 3. Aiming at the shortcomings of orthogonal matching tracking algorithm, such as low channel estimation efficiency and the need of channel sparsity as prior information, the algorithm is optimized. Aiming at the shortcoming of the need for sparse degree as prior information, a sparse adaptive matching tracking CE-SAMP algorithm is used for channel estimation. The experimental results show that the sparse degree is not required as prior information in CE-SAMP channel estimation algorithm. Moreover, the efficiency of the algorithm is higher than that of orthogonal matching tracking algorithm. Aiming at the defect that SAMP algorithm can not approach the true sparsity accurately, a variable step size adaptive matching tracking CE-AsSAMP-based estimation algorithm is designed. Combined with the idea of piecewise matching tracing and conjugate gradient direction updating, a piecewise conjugate matching tracking CE-StCGP-based estimation algorithm is designed and simulated. The experimental results show that the performance of mean square error of the same channel estimation is realized. Piecewise conjugate matching tracking algorithm has the fastest recovery speed.
【学位授予单位】：西南交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TN929.53

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