无线传感器网络的信道模型

安徽大学江淮学院

本科毕业论文（设计、创作）题目：基于压缩感知的稀疏信道估计方法研究

学生姓名：卜竞斐学号：JC104255

系别：计算机科学与技术专业：通信工程

入学时间：2010年9月

导师姓名：蒋芳职称/学位：讲师/硕士

导师所在单位：安徽大学电子信息工程学院

完成时间：二〇一四年四月

基于压缩感知的稀疏信道估计方法研究

摘要

在多径无线信道的高速数据通信通常需要接收器知道状态信息，因此，如何控制信道的传播特性和参数估计是具有重要意义的数字无线通信系统的研究。就目前来说，我们所运用的导频方法是必须提前知道发射机所发送的导频信号，再与接收端所接收到的信号进行比较，经过处理后得到我们所要的信道响应。但是，我们不难发现这种处理因为插入的导频太多而占用大量的宽带，从而使频带的利用率大大降低。专家经过研究发现：真正的无线通道的结构往往是稀疏，尤其是高速数据通信系统。如此一来，如何发掘和利用信道的稀疏性从而有效的进行信道估计就是研究的重点。近年来压缩感知（CS，Compressive Sensing）被看做是一种更好的信号获取方式。压缩感知的理论指出信号在某个变化域内稀疏或近似稀疏就可以用低于奈奎斯特抽样定理的速率对稀疏信号进行采样并在收端以很高的概率重建信号。压缩感知是现在信号处理领域的研究热点也被看做是一种信号获取的有效方式。本文介绍压缩感知的理论和信道估计的相关内容，以及正交频分复用（OFDM, Orthogonal Frequency Division Multiplex系统、超宽带（UWB, Ultra Wideband）系统和多输入多输出(multiple- input multiple output, MIMO )系统中基于压缩感知的稀疏信道估计方法，重点是压缩感知的重构方法。

关键词：压缩感知；稀疏信道；正交频分复用；UWB；多输入多输出

Study on sparse channel estimation method based on compressed sensing

Abstract

In a multipath radio channel now of high-speed data communication usually requires the receiver know state information, so how to control the channel, channel propagation characteristics and the parameter estimation is of great significance to the study of digital wireless communication system. Pilot current methods is the need to accept the pilot signal is known in advance sent by the transmitter, and corresponding to the received signal phase contrast, through analyzing and processing the final channel response. But the pilot this approach into too much occupied bandwidth, reduce the bandwidth efficiency. Through the study found: the structure of real wireless channels is often sparse especially high-speed data communication system. Thus, how to excavate and thus better channel estimation using the channel sparsity. In recent years, compressed sensing (CS, Compressive Sensing) is considered as one of the effective signal acquisition.the compressed sensingtheory points out signal in a changing domain sparse or approximate sparse can use rate than the Nyquist sampling theorem for sparse signal sampling and reconstruction of signals at the receiving end with very high probability. Compressed sensing is now the research focus in the field of signal processing is also regarded as an effective signal acquisition. Introduced the relevant theory and channel compressed sensing estimation, and orthogonal frequency division multiplexing (OFDM, Orthogonal Frequency Division Multiplex system, UWB system and multiple input multiple output (multiple- input multiple output, MIMO) estimation method of sparse channel based on compressed sensing system, focuses on the reconstruction of compressed sensing.

key words ：compressed sensing；sparse channel；orthogonal frequency division

multiplexing；Ultra Wideband；multiple input multiple output