基于改进广义正交匹配追踪的OFDM稀疏信道估计

在OFDM稀疏信道中,将压缩感知中的广义正交匹配追踪(GOMP)重构算法用到OFDM信道估计中.由于其信道重构的精度比较低,根据其特点做出了改进,提出了一种用于OFDM稀疏信道估计的改进广义正交匹配追踪算法.该改进算法能够在不需要预知信道稀疏度的情况下准确恢复出信号.根据实验和仿真结果可以看出,该改进算法与LS算法、OMP算法、GOMP算法相比,在同样的环境下误比特率以及均方误差相对比较低,而且运算速度比较快,具有一定的实用性.     

OFDM信道估计中基于随机导频的OMP算法研究

信道估计是正交频分复用（OFDM）系统中非常重要的一个环节,准确的信道状态信息是数据可靠接收的重要保证.文中具体采用基于随机导频的正交匹配追踪算法（OMP）对OFDM系统进行信道估计,通过与最小二乘（LS）算法性能对比,验证了OMP算法可以使用更少的导频获得较好的估计性能.并仿真分析了OMP算法对先验信道稀疏度的依赖程度,为实际应用提供了一定参考意义.     

基于快速贝叶斯匹配追踪优化的海上稀疏信道估计方法

正交频分复用(OFDM)系统中,由于频率发生选择性衰落会导致信道在数据传输中产生符号间干扰,因此接收机往往需要知道信道状态信息。而在海上通信的情况下,信道传输会受到多种外界因素的干扰,往往需要预先进行信道探测估计。为了提高估计性能,该文提出一种基于奇异值分解优化观测矩阵的快速贝叶斯匹配追踪稀疏信道估计优化算法(FBMPO),该算法不仅能够充分考虑海上通信的信道稀疏性,也能够降低信道的不确定性带来的影响。计算机仿真实验表明,与传统的信道估计算法相比,该算法能够提高信道估计的精确度。

     

基于压缩感知的MIMO NC-OFDM系统信道估计算法北大核心

多输入多输出不连续正交频分复用(MIMO NC-OFDM)系统是认知无线电(CR)系统的常用体制,由于授权用户占用而导致的载波不连续情况下的信道估计是影响该系统性能的关键技术问题。提出一种基于压缩感知(CS)的MIMO NC-OFDM系统的信道估计方法——稀疏自适应匹配追踪(SAMP)算法。SAMP算法在重构过程中先对信号稀疏度进行初始估计,然后自适应调整步长逐步逼近信号,相较于其他贪婪算法,能够在稀疏度未知的情况下准确重建稀疏信号。仿真结果表明,SAMP算法提高了重构精度,在实际应用中易于实现。     

MIMO-OFDM系统基于导频信道估计算法研究

介绍MIMO-OFDM系统,文章着重介绍分析了MIMO-OFDM系统的各种信道估计技术,通过对LS,MMSE,基于DFT的信道估计算法的仿真,得到三种算法的性能对比图。仿真结果显示LS性能虽不及其他两种估计算法,但是复杂度最低;MMSE性能较好,复杂度却高;DFT信道估计算法性能比LS好,复杂度也较低。     

MIMO-OFDM系统中的信道估计算法

本文对MIMO—OFDM系统中的信道估计技术进行了介绍,根据是否使用训练序列,信道估计可以分为导频辅助信道估计、盲信道估计及半盲信道估计,本文分别对已有的信道估计算法进行了综述,并对一种已有的基于m序列的时域信道估计方法进行了改进。     

MIMO-OFDM系统信道估计算法综述

MIMO-OFDM技术是近年来通信信号处理领域的研究热点,无线信道不仅是频率选择性的,而且是时变的,为了获得较高的系统性能,信道估计就显得尤为重要.对MIMO-OFDM系统中现有的信道估计算法进行了综述,重点介绍了基于训练序列的、导频符号的以及盲和半盲信道估计算法.比较了各种算法的优缺点,提出了未来信道估计的发展方向.     

MIMO-OFDM系统的信道估计算法

基于训练序列的信道估计方法在慢衰落信道中呈现出较好的性能，但是这种方法不适用于快衰落环境，为了能够及时跟踪信道的变化，通常采用基于导频符号的信道估计方法。将针对采用连续传输方式的MIMO—OFDM系统，讨论基于导频符号的信道估计方法并进行了计算机仿真。     

基于压缩感知的OFDM稀疏信道估计应用

压缩感知理论可通过远低于那奎斯特准则的方式进行采样数据,仍能够精确恢复出原始信号,基于CS技术的信道估计可减少OFDM系统中导频的数量,同时可获得较好的估计性能,本文通过介绍CS理论和OFDM信道估计方法,将CS理论应用到信道估计中,重点介绍通过ROMP算法估计信道冲击响应函数。     

一种基于RAMP算法的OFDM稀疏信道估计方法

针对OFDM稀疏信道估计需要信道稀疏度作先验条件的不足,将正则化自适应匹配追踪（RAMP ） 用于信道重建,可在信道稀疏度未知的情况下,自适应地调整候选集原子的个数,并利用正 则化过程实现支撑集的二次筛选,逐步扩大支撑集,准确地估计出信道的冲激响应。仿真结 果表明,该方法收敛速度快,估计效果好,有较好的应用价值。     

基于匹配追踪算法阈值改进的MIMO-OFDM信道估计研究

对于MIMO-OFDM系统信道估计的研究在国内外已经取得了一定的成果,其中采用CoSaMP算法进行信道估计时支撑候选集的选择尤为重要。采用CoSaMP算法对MIMO-OFDM信道进行估计,并在此基础上引入原子弱选择标准对候选集进行更进一步的优化,使改进后的CoSaMP算法在达到自适应的同时提高运算速率,达到信道估计算法自适应的目的。实验结果表明,对CoSaMP算法的候选集选择添加阈值后进行重构信号时复杂度降低,提高了运算速率,同时均方根误差较CoSaMP算法和OMP算法都有一定的提高。     

OFDM系统中基于弱选择ROMP算法的稀疏信道估计

为了解决实际OFDM通信系统中信道稀疏度未知的不足,提出将弱选择正则化正交匹配追踪算法用于估计稀疏信道。算法在不知晓信道稀疏度的情况下,对不同迭代残差与测量矩阵中原子的相关系数进行判定后,根据原子的弱选择准则灵活地确定出表示信道冲激响应的原子候选集,进而利用正则化原则从候选集中挑选出表示信道冲激响应的最优原子组,逐步实现精确重建。仿真结果和理论分析表明：与正则化正交匹配追踪算法相比,相同条件下改进算法可以获得更低的均方误差和误比特率;另外,算法无需将信道稀疏度作为先验信息,实用性更强。     

Sparse channel estimation via matching pursuit with application toequalization

Channels with a sparse impulse response arise in a number of communication applications. Exploiting the sparsity of the channel, we show how an estimate of the channel may be obtained using a matching pursuit (MP) algorithm. This estimate is compared to thresholded variants of the least squares (LS) channel estimate. Among these sparse channel estimates, the MP estimate is computationally much simpler to implement and a shorter training sequence is required to form an accurate channel estimate leading to greater information throughput     

基于基扩展模型的改进正则化正交匹配追踪V2X快时变SC-FDMA信道估计

为了进一步提升车联万物(V2X)的通信性能,首先根据信道冲激响应的稀疏性建立了适用于高速移动场景的基扩展模型(BEM);其次,证明了BEM系数具有稀疏性,将信道估计问题转化为稀疏信号重构问题,进而提出基于BEM的改进正则化正交匹配追踪(iROMP)迭代稀疏信道估计算法(简称为BEM-iROMP算法).所提算法通过iRO...     

Multi-parameter identification of power line communication channel model based on matching pursuit

The over-complete atom dictionary is designed on the basis of typical multi-path transmission function of power line communication (PLC) channel, and matching pursuit (MP) algorithm is used for multi-parameter identification of the channel. First, the sampling data were obtained from the measured result of low-voltage PLC channel within the range from 300 kHz to 6000 kHz, which help us to develop a model for the PLC channel. Then, in order to reduce the computation complexity of MP algorithm to identify the multi-parameter of PLC, genetic algorithm (GA) is applied to effectively search for the best atom at each step of MP in the dictionary of atoms. From the multi-parameter identification result with three or four paths channel models, it demonstrates that the combined algorithm based on MP and GA can acquire more evaluation precision of PLC channel than that of GA and particle swarm optimization (PSO) algorithm respectively. Meanwhile, the more numbers of channel paths in the process of multi-parameter identification, the better will be the curve fitting result compared to the experiment. The experimental results have validated the efficiency of the combined MP–GA algorithm that proposed in this paper.     

基于匹配追踪算法的电能质量研究

为了解决电能质量信号采集数据量大的问题,提出基于匹配追踪重构算法的压缩感知方法,并首次应用于电能质量信号压缩采样研究。文中通过采用不同的稀疏基和重构算法的方法,来提高原始电能质量信号重构效果。当采样数据空间稀疏基分别选取傅里叶变换基和小波变换基,重构算法分别采用正交匹配追踪(OMP)和压缩采样匹配追踪(CoSaMP)时,仿真结果表明,压缩采样比为20%时,两种重构算法的均方误差都低于3%,重构信噪比大于30dB,为电能质量信号压缩采样研究提供了一种新的思路。     

Maximum a posteriori fast fading channel estimation based exclusively on pilot symbols

An innovations-based block-by-block maximum a posteriori fast fading channel estimation algorithm, based exclusively on the received samples of pilot symbols, is proposed. This algorithm enables the theoretical determination of the optimum positions of pilot symbols using the raw bit error rate criterion. Moreover, it can be reformulated simply using appropriate weighting of the projections of received pilot symbols samples on an extended orthonormal base. This base can be determined simply as a function of the statistical properties of the channel.     

Efficient matching pursuit image coding based on block partitioning

A novel matching pursuit (MP) image coding method is presented. A block partitioning scheme is employed to effectively exploit the spatial characteristics of MP atoms. Experimental results show that the proposed method significantly outperforms the existing MP coding methods and thus bridges the gap in coding efficiency between MPand wavelet-based approaches.     

Recursive least square–based fast sparse multipath channel estimation

In the next‐generation wireless communication systems, the broadband signal transmission over wireless channel often incurs the frequency‐selective channel fading behavior and also results in the channel sparse structure, which is supported only by few large coefficients. For the stable wireless propagation to be ensured, linear adaptive channel estimation algorithms, eg, recursive least square and least mean square, have been developed. However, these traditional algorithms are unable to exploit the channel sparsity. Actually, channel estimation performance can be further improved by taking advantage of the sparsity. In this paper, 2 recursive least square–based fast adaptive sparse channel estimation algorithm is proposed by introducing sparse constraints, L1‐norm and L0‐norm, respectively. To improve the flexibility of the proposed algorithms, this paper introduces a regularization parameter selection method to adaptively exploit the channel sparsity. Finally, Monte Carlo–based computer simulations are conducted to validate the effectiveness of the proposed algorithms.