压缩感知理论在超宽带信道估计中的应用研究

发布时间：2018-05-17 05:02

  本文选题：超宽带信道估计 + 压缩感知　； 参考：《长沙理工大学》2014年硕士论文

【摘要】：超宽带是拥有许多优点的通信方式,加之近几年出现的压缩感知理论,为超宽带的发展提供了契机,压缩感知技术可以应用在对超宽带的信道估计上,这有助于提高超宽带通信系统的通信质量和性能,人们对此广泛关注。本文将该技术应用于超宽带信道估计中,具有积极的贡献和意义。本文以压缩感知理论为基础,并利用超宽带信道的稀疏特性,对信道参数进行估计,完成理论分析和实验数据仿真,实现了超宽带信道准确的估计。该研究对构造更高效、实用的算法具有突出的研究指导与现实意义。主要工作如下：1、利用混沌序列良好的随机性提出了一种基于Logistic混沌序列的超宽带信道估计方法,对贝叶斯压缩感知进行了数学建模。理论分析和计算机仿真结果表明,在相同的实验条件下,本文方法相比传统的重构算法,具有更好的抗噪声性能和重构精度,并与其它类型的测量矩阵进行比较,在低信噪比和测量次数的数值分析结果证明本算法是可行有效的,且相对于随机矩阵,本文矩阵更易于实现,信道估计值更加稳定。2、利用快速RVM来优化分布式贝叶斯算法,解决传统单任务贝叶斯压缩感知算法在多用户超宽带系统中的不足。本文通过建立基于快速RⅧ的分布式贝叶斯压缩感知模型,利用多用户信号间的统计相关性,对接收信号进行联合重构仿真结果表明,该算法能有效的减少多用户超宽带信道估计的测量次数。此外,本文对测量矩阵在基于压缩感知的超宽带信道估计系统模型中的性能分析,为今后的研究者们提供了新的思考方向。
[Abstract]:Ultra-wideband (UWB) is a communication mode with many advantages. In addition, the theory of compressed sensing, which has emerged in recent years, provides an opportunity for the development of UWB. Compression sensing technology can be applied to the channel estimation of UWB. This is helpful to improve the communication quality and performance of UWB communication system. This paper applies this technique to UWB channel estimation, which has positive contribution and significance. Based on the theory of compression sensing and using the sparse characteristic of UWB channel, the channel parameters are estimated, and the theoretical analysis and experimental data simulation are completed to realize the accurate estimation of UWB channel. This research has the outstanding research instruction and the realistic significance to the construction more efficient, the practical algorithm. The main work is as follows: 1. By using the good randomness of chaotic sequences, a novel channel estimation method based on Logistic chaotic sequences is proposed, and the mathematical model of Bayesian compression sensing is presented. Theoretical analysis and computer simulation results show that, under the same experimental conditions, the proposed method has better anti-noise performance and better reconstruction accuracy than the traditional reconstruction algorithm, and is compared with other types of measurement matrix. The numerical results of low SNR and measurement times show that the proposed algorithm is feasible and effective. Compared with the random matrix, the proposed algorithm is easier to implement and the channel estimation value is more stable. The fast RVM is used to optimize the distributed Bayesian algorithm. To solve the problem of traditional single-task Bayesian compression sensing algorithm in multi-user UWB systems. In this paper, a distributed Bayesian compression perception model based on fast R 鈪，

本文编号：1900005