OFDM稀疏信道估计中导频设计算法

由于无线信道固有的稀疏性,压缩感知理论已被应用于正交频分复用(OFDM)系统的信道估计中来提高频谱利用率。文中研究OFDM系统稀疏信道中确定性导频的设计问题,针对互相关最小准则的不足,提出了一种基于测量矩阵互相关和测量矩阵列相关平方和最小化的联合算法。仿真结果表明,该算法的归一化均方误差(MSE)和误码率(SER)性能均优于基于互相关最小准则的随机搜索导频,相比于最小二乘算法,稀疏信道估计使用了更少的导频获得了更好的估计效果,提高了频谱利用率。     

基于分群子载波空时频MIMO-OFDM信道估计

针对空时频编码的MIMO-OFDM系统,根据子信道相关特性确定子载波分群,该文提出了独立子群导频法的信道估计处理策略,并将导频序列由空、频两域扩展到空、时、频域,能进一步提高估计精度。理论分析和计算机仿真表明,在频率选择性衰落信道下,基于空时频的独立子群导频法在较低运算复杂度下可以获得信道估计性能的明显改善。     

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。     

基于二进制编码多导频搜索的导频设计

针对正交频分复用(OFDM)系统中的稀疏信道估计问题,文章提出了一种基于二进制编码的多导频并行搜索导频设计算法.文章所提算法在最小互不相干性(MIP)规则下,通过最小化字典矩阵的相干性来考虑确定性导频设计问题,并要求将导频分配放在有效子载波范围内以实现更实际的目的.导频集在文章所提算法中是二进制编码的.在每次迭代中,将...     

DFT-S-GMC系统中均匀导频信道估计方法性能分析

针对我国提出的离散傅里叶(DFT)扩频广义多载波(DFT -S-GMC)系统,给出了使用均匀分布导频的信道估计方法.该方法首先采用最小二乘算法估计导频音的信道频响,然后内插或外推非导频音的信道频响,再利用信道频响DFT过采样特性,数据块特定用户数据子带信道频响可由与之对应的导频块内子载波处信道频响线性内插获得,并在时间维上线性内插出数据子带处的信道频响.最后,通过仿真比较分析了高低阶插值、内插外推方法以及导频密度对信道估计方法性能的影响.     

快速时变信道下的OFDM信道估计方法

为解决正交频分复用(OFDM)系统在快速时变信道下引入的子载波间干扰(ICI)导致性能急剧下降的问题,提出一种基于带状矩阵的数据辅助型信道估计算法。通过分析由多普勒频偏引入的子载波间干扰矩阵,运用线性模型对信道冲击响应算法进行建模,对每个子载波设计带状补偿矩阵以最大程度消除ICI。数值仿真结果表明,该信道估计算法较最小均方误差(MMSE)算法复杂度大大减小,便于实现,满足快速时变信道下的估计精度需要。     

地空通信中基于贪婪算法的SC-FDE系统稀疏信道估计

针对在地空频率选择性衰落信道中,信道冲激响应具有时域稀疏特性的情况,提出了基于贪婪算法的单载波频域均衡(SC-FDE)系统稀疏信道估计方法。Chu序列是SC-FDE中常用的导频序列,对其进行分析并证明了将Chu序列进行循环移位所构造的导频矩阵满足RIP条件,将导频矩阵作为测量矩阵,把地空信道估计问题建模为稀疏重构模型,采用贪婪算法中的OMP和Co Sa MP算法对信道进行稀疏重构,仿真验证了所得信道估计较传统最小二乘(LS)信道估计方法更加准确。在相同的训练序列长度和信道环境下,利用所得信道估计对接收信号进行最小均方误差(MMSE)均衡,蒙特卡罗仿真结果表明,所提方法与传统LS信道估计方法相比,系统误码性能提高2~3 d B。     

一种改进的OFDM/OQAM系统信道估计算法

基于块状导频的信道估计方法可以克服OFDM/OQAM(OFDM/Offset QAM)系统所固有的符号间和载波间干扰,从而成为该类系统通用的信道估计方法。该文基于块状导频结构和OFDM/OQAM的系统特点,分析了系统相邻子载波之间的相关性,并在此基础上提出一种改进的信道估计算法,通过计算相邻子载波的相关系数,在频域进行有效的加权运算来降低干扰和噪声对信道估计的影响。分析和仿真结果表明,该算法能够有效地提高传统算法的信道估计精度和系统性能。     

一种基于均匀分布型的MIMO OFDM系统最优导频设计

本文提出了一种针对存在虚子载波的MIMO OFDM系统下基于均匀分布型的最优导频设计,该设计通过采用时域最小二乘信道估计(TD-LS-CE),且以数据子载波的频域信道响应的均方误差为准则,在最大化带宽效率的前提下,对各发送天线上均匀分布型的导频子载波进行功率最优化分配,并推导了其闭式解。最后,仿真结果表明该最优导频序列在MSE、BER性能方面均优于传统的移相正交导频序列和等功率分配的交错正交导频序列。     

WiMAX系统中导频和信道估计

WiMAX系统中物理层多址方案采用了正交频分多址接入(OFDMA),并根据上、下行链路的不同特点,定义了多种导频图案。当OFDMA和多输入多输出(MIMO)技术结合时,导频图案也需要变化来支持多天线。WiMAX系统中MIMO-OFDMA有五种导频模式,包括下行部分使用子信道(DL-PUSC)、下行完全使用子信道(DL-FUSC)、下行可选完全使用子信道(DL-OFUSC)、上行部分使用子信道(UL-PUSC)和上行可选部分使用子信道(UL-OPUSC)。通过分析时域LS、频域LS和基于FFT的信道估计方法下的仿真结果,可以得出每种模式下的最优信道估计方案。     

OFDM稀疏信道估计中基于树状随机搜索导频设计新方法

压缩感知（CS,Compressed Sensing）是一种以低速率对稀疏信号进行采样后在接收端重建信号的技术,基于CS的稀疏信道估计具有更小的导频开销且具有更好的信道估计性能。针对基于CS的OFDM稀疏信道估计中的导频设计问题,提出一种基于树状随机搜索算法（TSS,Tree-based Stochastic Search Algorithm）的导频位置设计新方法,该方法结合了树的结构,以分支的方式进行随机搜索从而避免陷入局部最优问题。仿真结果表明,与传统的导频设计方法相比,使用TSS算法获得的导频图案用于信道估计中能够获得更优的信道估计性能,而且TSS算法的复杂度更低。      

压缩感知在稀疏信道估计中的应用

压缩感知( CS,Compressive Sensing)理论指出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,它是目前信号处理领域的研究热点.基于CS理论的信道估计会降低导频辅助信道估计的导频数量且估计性能好,介绍了CS的基本原理和信道估计相关内容,以及正交频分复用(OFDM,Ort...     

Pilot Optimization for Structured Compressive Sensing Based Channel Estimation in Large-Scale MIMO Systems with Superimposed Pilot Pattern

Compressive sensing (CS) has attracted much attention in wireless communications due to its ability to attain acceptable channel estimates with a small number of pilots. To further reduce the pilot overhead in multi-input multi-output (MIMO) systems, CS-based channel estimation may employ superimposed pilot pattern. Previous works on superimposed pilot design generally allocate pilots randomly, which may give ill-posed measurement matrices. In this paper, we focus on deterministic pilot allocation for large-scale MIMO systems with superimposed pilot pattern to improve the performance of structured CS based channel estimation. By exploiting the spatial common sparsity and the error bound of block sparse reconstruction, a new criterion is firstly proposed to optimize the pilots in the Hadamard space. The proposed criterion makes full use of the information about the principal angles across the blocks in the measurement matrix, which can enhance the average recovery ability and exclude the worst pilots simultaneously. Secondly, a genetic algorithm is proposed to minimize the merit factor of the proposed criterion efficiently. Simulation results show that the proposed optimized pilots outperform the random pilots in terms of mean-squared error by about 3 dB. Moreover, the proposed criterion is more likely to achieve better measurement matrices than the traditional criteria.     

一种适用于NC-OFDM系统的次优导频设计

在传统的正交频分复用（OFDM）系统中,等间隔等功率分布的导频序列是最优的。然而,由于频谱的非连续性,这种最优的导频序列无法适用于非连续正交频分复用（NC-OFDM）系统。在基于归一化符号估计均方误差的准则下,提出了一种适用于NC-OFDM系统的次优导频设计方法。它先分配给每块频带的导频数目,然后通过一种简单的搜索方法求出每个导频的次优位置。仿真分析表明,该方法不仅具有较低的计算复杂度,而且能获得良好的信道估计性能和误码率性能。     

基于混合遗传算法的导频优化

OFDM系统中,基于压缩感知的稀疏信道估计能够充分利用无线信道的固有稀疏性,进而降低导频开销,提高频谱利用率。针对压缩感知信道估计的导频设计,通过最小化压缩感知理论中测量矩阵的互相关性,提出一种基于混合遗传算法的导频优化方法。该方案首先采用遗传算法获得次优初始导频序列,然后结合导频位置以及导频功率对导频序列逐位进行替换、优化,以使测量矩阵的互相关性最小。MATLAB 仿真结果表明,相比于伪随机导频设计和等间距导频设计,该算法能够保证较低的均方误差和误码率。     

MIMO-OFDM快衰落信道的压缩感知估计方法

在快衰落多输入多输出(MIMO)-正交频分复用(OFDM)系统中,为了避免传统的信道估计方法中存在大量系数需要估计的问题,利用快衰落信道在角时延多普勒域可稀疏的特性,提出了基于压缩感知的MIMO-OFDM系统快衰落信道估计方法。根据压缩感知的受限等距特性(RIP),推导了一种少量导频随机结构测量矩阵,用于测量快衰落信道在角时延多普勒域稀疏系数。接收端可从这些少量的测量数据中以高概率重构出快衰落信道。理论分析与仿真结果都表明:该方法与传统的信道估计方法相比,所得到的系统数据传输效率及估计性能都有了明显提高。     

Pilot design for sparse MIMO-OFDM channel estimation with generalized shift invariance property

Compressed sensing (CS) based channel estimation is greatly bound by the measurement matrix according to CS theory. We design pilot patterns by minimizing the mutual coherence of the measurement matrix with the generalized shift invariance property (GSIP). GSIP and a corollary are firstly proposed. Then two pilot pattern design schemes termed pilot design with GSIP (PDGSIP) and tradeoff pilot design with GSIP (TPDGSIP) are put forward to design orthogonal pilot patterns based on GSIP for a multiple-input multiple-output orthogonal frequency division multiplexing system. In PDGSIP, a collection of pilot patterns are firstly obtained and then pilot patterns having large mutual coherence are replaced with new ones generated with optimal pilot patterns. TPDGSIP directly produces new pilot patterns based on GSIP to fully exploit the pilot distance of the obtained pilot pattern as soon as one pilot pattern is obtained. Simulation results have shown that, the proposed pilot pattern design schemes are able to obtain the best pilot patterns in comparison to existing methods from the perspective of mutual coherence. Channel estimation performance using pilot patterns designed by proposed schemes precedes that using pilot patterns designed by existing schemes in terms of normalized mean square error and bit error rate.     

认知NC-OFDM系统一种改进的信道估计

介绍了认知无线电NC-OFDM系统中的信道估计技术,着重分析了随机插入导频模式和迭代IDFT变换域降噪方法。通过引入转换分解算法,对迭代IDFT变换域降噪方法进行改进,改进的信道估计算法与迭代IDFT变换域降噪方法有相同的估计性能,但改进的信道估计算法运算量少,估计器复杂度低,提高了硬件资源的利用,具有实用性。     

基于压缩感知的室内场景无线定位算法

The sparse nature of location finding in the spatial domain makes it possible to exploit the Compressive Sensing (CS) theory for wireless location. CS-based location algorithm can largely reduce the number of online measurements while achieving a high level of localization accuracy, which makes the CS-based solution very attractive for indoor positioning. However, CS theory offers exact deterministic recovery of the sparse or compressible signals under two basic restriction conditions of sparsity and incoherence. In order to achieve a good recovery performance of sparse signals, CS-based solution needs to construct an efficient CS model. The model must satisfy the practical application requirements as well as following theoretical restrictions. In this paper, we propose two novel CS-based location solutions based on two different points of view: the CS-based algorithm with raising-dimension pre-processing and the CS-based algorithm with Minor Component Analysis (MCA). Analytical studies and simulations indicate that the proposed novel schemes achieve much higher localization accuracy.     

A low complexity DFT-matrix based pilot allocation algorithm for sparse channel estimation in OFDM systems

The accuracy of channel estimation is very important for Orthogonal Frequency Division Multiplexing (OFDM) systems. In a high speed wideband wireless communication, the channel can be modeled as a sparse one. Therefore, the Compressed Sensing (CS) technique can be used for the estimation of the channel. In this paper, the problem of deterministic pilot allocation in OFDM systems is considered and a new criterion which is based on minimizing the summation of the correlations between the columns of the Discrete Fourier Transform (DFT) sub-matrix is proposed. It will be shown that the proposed criterion is a simple version of the well-known but complex criterion, Restricted Isometry Property (RIP). In addition, the pilot pattern design, using our proposed scheme, indicates better recovery performance than other proposed coherence based criteria in terms of the reconstruction mean square error (MSE) and successful channel recovery percentage. Simulation results confirm our analysis.