基于压缩感知的OFDM系统信道估计方法

针对无线信道的时域稀疏性以及稀疏度未知的问题,文章将压缩感知技术应用到正交频分复用(OFDM)系统信道估计中,提出了一种稀疏度自适应正交匹配追踪信道估计算法。算法利用离散傅里叶变换(DFT)信道估计算法对循环前缀内和外的噪声进行处理,估计得到的信道频率响应作为正交匹配追踪(OMP)算法稀疏迭代终止的判断条件,实现稀疏度自适应信号重建。同时在原子预选阶段,采用Dice系数准则代替内积准则作为相关性度量准则,可达到更优的估计性能。仿真结果表明,该算法相比于传统的压缩感知信道估计算法具有较好的性能,可以提高系统的归一化均方误差(NMSE)和误码率(BER)性能。     

一种稀疏增强的压缩感知MIMO-OFDM信道估计算法

 基于压缩感知(Compressed Sensing, CS)的信道估计可以达到减少导频的目的,但在频-时域信道矩阵到时延-多普勒域的稀疏变换中存在谱泄漏现象,影响了信道矩阵的稀疏性和估计的均方误差(MSE)性能。为此该文对信道的稀疏性进行研究,提出一种时域加窗的稀疏优化CS信道估计算法。通过对时域加窗,所提算法抑制了由离散截断导致的多普勒域泄漏,再据此设计出观测矩阵,以此方式增强信道在时延-多普勒域的稀疏性,并实现对稀疏的信道矩阵更为准确的重构,达到改善信道估计MSE性能的目的。仿真结果表明随信噪比的增大,加窗CS算法相比无窗CS算法有效改善了信道估计的性能。     

结构性稀疏阵列多信道联合估计

多信道估计时,如果利用信道的稀疏性和多信道的相关性,可以提高信道估计性能。本文利用阵列信道的结构性稀疏特性,提出了一种多路分组稀疏LMS算法(Group Sparse LMS,GS-LMS)。该算法将多路信道作为一个整体同时进行自适应信道估计,通过引入Ι_2,1范数,将结构性稀疏先验引入到稀疏LMS算法的代价函数中,导出新的滤波器权系数更新公式。仿真结果表明了在不同信道条件下,本文算法的稳态误差性能明显优于若干现有的稀疏LMS算法。      

基于压缩感知的OFDM宽带短波信道估计方法研究

为了提高OFDM宽带短波信道估计的精确性,针对短波信道固有的低稀疏性,在将压缩感知理论应用于OFDM宽带短波信道估计的基础上进行OFDM短波信道的稀疏建模,接着提出需要解决的问题,进而提出采用正交匹配追踪(OMP)算法进行短波信道的重构。通过仿真实验证实,与传统信道估计算法中的最小二乘(LS)算法比较,可以达到在使用更少导频的情况下提供更好的短波信道估计性能的效果,从而提高短波系统的频带利用率。     

OFDM联合信道估计与均衡技术研究

OFDM系统在时变信道中会受到子载波间干扰,单独进行信道估计和信号检测的策略对于提高接收机的抗干扰能力有限,将信道估计和信号检测统一考虑则可更有效地抵抗子载波间干扰。针对此问题,基于迭代SAGE算法提出了一种新的联合信道估计与符号检测算法,为了减低算法的复杂度,引入BEM信道建模方法。仿真评估了BEM算法的归一化均方误差性能,验证了BEM建模的有效性,显示该算法的误码率优于基于BEM算法的线性均衡检测算法和基于MMSE的干扰对消算法,而且该算法只通过少数几次迭代便可达到收敛域,较好地克服了子载波间干扰的问题。     

基于LASSO算法的波束空间信道估计

在大规模毫米波(mmWave)天线阵列通信中,多输入多输出(Multiple Input Multiple Output,MIMO)系统可以使用透镜天线阵列大幅减少射频链的数量,但由于天线的数量远远大于射频链路的数量,信道估计具有挑战性。由于波束空间信道具有稀疏的特性,那么用于求解稀疏信号恢复问题的算法,可以作为波束空间信道估计问题的解决方法。波束空间信道估计问题建立的模型是基于l₀-范数的非凸性问题,该问题为NP-hard。通常用l₁-范数代替l₀-范数,将该问题转化为凸优化问题。该凸优化问题可以用传统的贪心算法方法进行求解。然而,这些贪心算法估计精度差。而且随着稀疏度的增加,计算复杂度也会增加。文章提出了最小角回归(Least Angle Regression,LARS)算法和改进的最小绝对收缩和选择算子(Least Absolute Shrinkage and Selection Operator,LASSO)算法,高效地解决了稀疏信号的恢复问题,即波束信道的估计。实验仿真结果表明,LARS算法和所提出的改进LAS...     

针对块稀疏信道的估计算法

无线多径信道中存在着块稀疏结构。针对块稀疏信道中分块信息是否已知的不同场景,分别提出了两种基于块稀疏贝叶斯学习(BSBL)框架的OFDM系统信道估计算法。这两种算法根据边界最优(BO)方法估计信道分块的稀疏度参数,提升算法运算速率。为进一步提升信道估计性能,在基于BSBL框架算法仅利用导频信号估计信道的基础上,又提出了基于联合块稀疏贝叶斯学习(JBSBL)的信道估计新算法,该算法利用导频与数据子载波实现信道的联合估计。仿真结果表明,与传统的最小二乘算法比较,本文提出的算法均可获得很好的信道估计性能,且基于JBSBL的信道估计算法性能更佳。      

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。     

一种基于RLS自适应滤波跟踪的信道估计方法

对于快时变信道~([1]),基扩展模型(Basic Expansion Model,BEM)能很好地捕捉信道的时变特性,并能有效模拟信道的传输情况,进而常用于信道建模。本文提出了一种基于RLS自适应滤波跟踪的信道估计方法。自适应滤波器本身有一个重要的算法,即递归最小二乘(Recursive least squares,RLS)算法。文章利用RLS自适应滤波算法对BEM基系数g进行跟踪,并将其自适应的调整大小,然后对信道响应进行估计。为验证所提方法的性能,本文对所提算法与LS配合插值算法进行仿真对比。仿真结果表明,所提方法相较LS算法有很好的估计精度。     

基于先验支持集的自适应信道估计方法

针对大规模MIMO系统信道的稀疏性并且信道随时间缓慢变化的特性,提出了一种自适应信道估计算法。算法根据前一个符号的信道非零抽头索引集(先验支持集),利用质量参数衡量先验支持集的质量,自适应估计当前符号的稀疏索引集(支持集)及时域信道脉冲响应(CIR)。仿真结果证明:提出的算法能够有效提升信道估计的精度,对信道时延变化具有更好的适应性。     

Sparse least mean mixed‐norm adaptive filtering algorithms for sparse channel estimation applications

Sparse least‐mean mixed‐norm (LMMN) algorithms are developed to improve the estimation performance for sparse channel estimation applications. Both the benefits of the least mean fourth and least mean square algorithms are utilized to exploit a type of sparse LMMN algorithms. The proposed sparse‐aware LMMN algorithms are implemented by integrating an l ₁‐norm or log‐sum function into the cost function of traditional LMMN algorithm so that they can exploit the sparse properties of the broadband multi‐path channel and achieve better channel estimation performance. The proposed sparse LMMN algorithms are equal to adding an amazing zero‐attractor in the update equation of the traditional LMMN algorithm, which aim to speed up the convergence. The channel estimation performance of the proposed sparse LMMN algorithms are evaluated over a sparse broadband multi‐path channel to verify their effectiveness. Simulation results depict that the sparse LMMN algorithms are superior to the previously reported sparse‐aware least mean square/fourth, least mean fourth and least mean square and their corresponding sparse‐aware algorithms in terms of both the convergence and steady‐state behavior when the broadband multi‐path channel is sparse. Copyright © 2016 John Wiley & Sons, Ltd.     

NGB-W广播信道估计实现算法设计

针对下一代广播电视网无线系统(NGB-W)广播接收信道估计,提出了一种基于二次一维维纳滤波的信道估计实用算法.该算法实时估计信道多普勒频偏、时延扩展和噪声方差等参数,并根据参数估计值分别从离线获得的预选集中选择时域和频域的一维维纳滤波系数.通过Cocentric System Studio(CCSS)平台仿真,给出了参数估计对算法性能的影响,以及所提信道估计算法的均方误差和误块率性能.仿真结果表明该算法在不同移动速度下,与采用理想插值系数信道估计相比性能损失不超过0.4 dB,与理想信道估计相比性能损失在1 dB以内.     

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.     

A new optimized least-square sparse channel estimation scheme for underwater acoustic communication

In underwater acoustic (UWA) communication, orthogonal frequency division multiplexing (OFDM) is a promising technology that is highly essential to get channel state information meant for channel estimation (CE). Nevertheless, higher complexity, slower convergence, and poor performance, which degrade the performance estimation, are the limitations of the traditional CE methodologies. Thus, by amalgamating the least square (LS)-CE algorithm along with polynomial interpolated black widow optimization (PI-BWO) model, an optimized least square sparse (OLSS) CE algorithm has been proposed to intend for a UWA-OFDM communication system. Formerly, by utilizing the 2's complement shift left turbo encoding (2CSL-TE) methodology, the input signal is encoded. After that, the modulated encoded signal is provided for inverse fast Fourier transform (IFFT) operations; subsequently, they are transferred over the UWA channel toward the receiver OFDM. By employing the OLSS methodology, the received OFDM signal's interference-free region is utilized for sparse CE at the receiver. Regarding symbol error rate (SER), bit error rate (BER), mean square error (MSE), and peak signal-to-noise ratio (PSNR), the proposed model's experiential outcome is evaluated and analogized with the other prevailing methodologies. When analogized with the conventional models, the proposed estimation methodologies achieved better performance.     

水声稀疏时变SIMO信道盲均衡算法

基于复指数基扩展模型（Complex exponential basis expansion model, CE-BEM）,利用信道的稀疏特性和发送信号的常模特性（Constant Modulus, CM）,提出水声稀疏时变（Time-variant, TV）SIMO信道盲均衡算法。首先采用l0-范数约束的比例系数归一化最小均方误差常模算法对等效信道矩阵的稀疏时不变部分进行均衡,然后采用基频率估计算法估计基频率并对多普勒频移进行补偿,最后对恢复信号中存在的相偏进行估计补偿。仿真实验结果表明,本文算法提高了均衡器的收敛速度,降低了剩余码间干扰。      

基于改进广义Steiner估计的MC-CDMA系统空时多用户检测

研究了频率选择性瑞利衰落信道中的同步MC-CDMA系统上行链路空时信道估计及多用户检测算法。考虑对应于子载波的衰落系数是信道冲激响应的离散傅里叶变换,通过在两个数据块之间插入训练序列(midamble)进行所有用户的联合信道估计。首先采用广义Steiner估计器(GSE)来进行阵列天线信道冲激响应的初始估计,然后提出一种简单有效的适用于均匀线阵的互相关波达方向(CCDOA)估计算法,用以改进阵列天线信道冲激响应的估计,从空间的角度降低了信道响应中的噪声。在估计出所有用户空时信道参数的基础上,构造最大比合并(MRC)、解相关检测和最小均方误差检测(MMSE)来进行信号检测。仿真结果表明基于互相关DOA估计的改进信道估计算法与广义Steiner估计器相比在系统性能上有显著的改善。     

基于均值循环卷积特性的UWB信道盲估计算法

该文针对采用码片率抽头间隔的TH-PPM超宽带系统离散信道,利用接收信号的均值循环卷积特性,对UWB信道估计问题进行建模,结合UWB信道的稀疏簇结构,提出一种基于抽头探测的UWB信道盲估计算法,避免了无谓的零抽头估计,改善了算法性能。仿真表明:在低信噪比(0-15dB)的情况下,基于抽头探测算法的MSE比没利用信道结构特征的最小二乘算法平均低约5.5dB;在中等信噪比(15dB)的情况下,基于抽头探测算法的MSE比最小二乘算法平均低约3.5dB,同时基于抽头探测算法还能获得较好的SER(Signal-Error-Ratio)性能。     

Efficient compressive sensing based sparse channel estimation for 5G massive MIMO systems

Massive MIMO (multiple-input-multiple-output) is one of the key technologies of 5G mobile cellular networks, which can form a huge antenna array by providing a large number of antennas at the cell base station. It will greatly improve the channel capacity and spectrum utilization and has become a hotspot in the field of wireless communications in recent years. Aiming at the high complexity of channel estimation algorithm for massive MIMO system, a sparse channel estimation algorithm with low complexity is proposed based on the inherent sparsity of wireless communication channel. The algorithm separates the channel taps from the noise space on the basis of the traditional discrete Fourier transform (DFT) channel estimation, so that the channel estimation only needs to calculate the part of the channel tap, so the computational complexity of the algorithm is greatly reduced. The simulation results show that the proposed algorithm can achieve near minimum mean square error (MMSE) performance while maintaining low complexity. Moreover, the Bit Error Rate and Inter-Cell Interference also indicates that the proposed improved algorithm shows better overall performance than the conventional algorithms which makes it suitable from practical perspective.     

Low-Complexity Channel Estimation for Wireless Block Transmission Systems in Time-and Frequency-Selective Rayleigh Fading Channels

Mobility-induced Doppler spread and multipath propagation introduce the time- and frequency-selectivity (doubly selectivity) in fading channels. Based on the complex exponential basis expansion model (BEM) to approximate the doubly selective channel (DSC), a low-complexity channel estimation scheme for block transmission systems over DSC are developed in this paper. Using the developed scheme, the long data block is divided into a few short data subblocks in terms of the maximum normalized Doppler frequency and block length, and each subblock is performed to respective channel estimation. Thus the total calculation complexity is effectively decreased because the number of the BEM channel coefficients to estimate is greatly reduced for each sub-block. Moreover, by utilizing these channel estimation values to refit the true channel, we can obtain better channel estimation. Besides, the normalized mean square error (NMSE) expressions for the developed scheme and the existing scheme are derived in detail, respectively. Compared to the existing scheme, the proposed scheme has lower calculation complexity and superior performance. The simulation results verify the effectiveness of the developed scheme, and the theory values of the derived NMSE accord with corresponding simulation values.